vibration analysis level- 1 updated [compatibility mode]

Warmest Welcome to Vibration Analysis Level I Course

Vibration Analysis Level‐Iy

IMRAN AHMADDirector TechnicalSUMICO Technologies (Pvt) Ltd+92 321 427 [email protected]

TimingsTimings

• 1st Session 0900‐10451 Session 0900 1045• Tea Break 1045‐11002nd S i 1100 1300• 2nd Session 1100‐1300

• Lunch/Prayer Break 1300‐1400• 3rd Session 1400‐1530• Tea Break 1530‐1545Tea Break 1530 1545• 4th Session 1545‐1700

Typical Machinery Problems that Can Be Found Using Vibration Analysis

• Unbalance Mi li t• Misalignment

• Mechanical looseness• Structural problemsStructural problems• Bent shaft• Bearing faultsg

Typical Machinery Problems that Can Be Found Using Vibration Analysis

• Gear faults• Belt problems• Lubrication problems• Electrical motor faults• Cavitations and turbulence• others

What is CBM &Why ?What is CBM & Why ?• To try and maximise the plants production and increase the

mean time between outages many industries are moved g ytowards a ‘Condition Based Maintenance’ approach.

• Condition Based Maintenance or CBM is an activity that attempts to predict and trend component failure non‐intrusively given the end user valuable advanced warning of the problem at hand.

• Maximising asset reliability is of the utmost importance in today’s global economy. – As competition and the pressure to produce products cheaper

increases the higher consequence of machine/production failure becomes.

CBM Overview• Most machine faults generate some kind of signature that is

unique to the particular fault developing. By using the correct technology to detect these signatures we can notcorrect technology to detect these signatures we can not only tell that a fault is developing, but distinguish what the fault type is.

• There are several technologies available to help determine• There are several technologies available to help determine the condition of the machine being monitored and the type of fault developing and these are:– Vibration Analysisy– Tribology– Sonics– Thermography

Di i hi i j likDiagnosing a machine is just like a person…

Vibration: The ‘pulse’ of the

Oil: The ‘life blood’ of

Thermography:‘Taking its

Motor Current:The ‘brain waves’

machine the machine temperature’ of the machine

TotalPicture

Overview of TechnologiesOverview of Technologies• Vibration Analysis

– Used to Detect, Analyse and Confirm plant machinery problems. This y p y pcan be done in three ways:• On‐line (4500T & CSI6500) for automated and continuous monitoring and

protection of critical plant items• Portable (2130 Analyser) Route based data collection and analysis• Wireless used for remote monitoring of moving or inaccessible equipment

Overview of Technologiesg• Tribology

– Is the analysis of ‘interactive surfaces in relative motion’.• Lubricants are analysed on‐site using the 5200 mini‐lab series. • The results are plotted in a simple to understand tri‐vector plot showing the

‘Chemistry’ ‘Contamination’ and ‘Wear’ of each lubricant, this allowing the lubricant to be changed on condition rather than on a time based intervallubricant to be changed on condition rather than on a time‐based interval.

Wear

Contamination Chemistry

Overview of Technologiesg• Sonics

– Through a process known as ‘heterodyning’ Ultrasonic sounds that d bl h d b k dare non‐audible to human ears are converted back down to a

frequency that is audible to human ears, allowing the operator to hear and recognise faults developing within plant operating systems, such as:such as:• Mechanical – Bearings, Rubs, Gear Defects etc• Electrical Defects• Valve Operation• Steam Trap Operation• Leak Detection – Pressurised Systems and Vacuum Systems

Overview of Technologiesg• Thermography

– Thermal Imaging is used to locate potential problems by detecting g g p p y gabnormal temperature fluctuations at a glance. • This can be used in a wide array of circumstances but is most commonly

used in electrical control panels

Overview of Technologiesg• Corrective technologies allow the engineer to set‐up the machine to try

and prevent premature machine failure from such causes as ImbalancedMi li tand Misalignment

– When these forces are induced upon a machine components such as bearings, seals and even supports fail due to stressTechnologies such as Laser Alignment and Balancing prevent these from being– Technologies such as Laser Alignment and Balancing prevent these from being so much of a problem

Machinery Health Managery g

A1 - Recirculation Pump #5C # O O O G O O

• Each technology is stored and analysed from a single software platform, allowing the analyst to:

RCP#5 -M2H MOTOR INBOARD BRG. - HORIZONTATrend Display 36-65xTS

0 100 200 300 400 500

0 0.0050.0100.0150.0200.025

Days: 11-Aug-95 To 11-Dec-96

PK In

/Sec

ALERT

FAULT

The machines due to be monitored are defined within the softwareallowing the analyst to:

– Store all data and information in one database– Easily cross reference data for conformation of analysis

Collaborate all data into one single reportRoute Waveform 11 D 96 17 33 57

1.0s

Route Spectrum 11-Dec-96 17:33:57 OVERALL= .0604 V-DG PK = .0605 LOAD = 100.0 RPM = 3593. (59.89 Hz)

0 40 80 120 160 200 240

0 0.010.020.030.040.05

Frequency in kCPM

PK In

/Sec

software.– As much information as possible about the machines being monitored

is preferred when building the database.

– Collaborate all data into one single report. 11-Dec-96 17:33:57 RMS = .4233 PK(+/-) = 1.13/1.22 CRESTF= 2.89

0 1 2 3 4 5 6 7

-1.5-1.0-0.5

0 0.5

Revolution Number

Acc

in G

-s

O iOverview of

Condition Monitoring

Maintenance PhilosophiesMaintenance Philosophies

Definition of MaintenanceDefinition of Maintenance

• The act of causing to continue• The act of causing to continue(Webster)

• Keeping equipment in repair (Oxford)

MaintenanceReactive Maintenance– Often called ‘Breakdown Maintenance’

and has the concept ‘fix it when itbreaks’

Planned MaintenanceAlso known as ‘ShutdownMaintenance’. This is based uponbreaks .

• This is probably the most commontype of maintenance in industry todaybut can be the most costly, especiallyon critical machines.

p‘Timed Intervals’ betweenmaintenance.

Can be very effective if maintenanceand resources are aimed at the

• Maintenance costs are usually higherdue to the catastrophic failure thatoccurs.

and resources are aimed at themachines that need it the most.However it can be very difficult todistinguish which machines actuallyneed maintenance.

Predictive Maintenance– Also known as ‘Condition Based

Maintenance’.

Proactive MaintenanceOften referred to as ‘Root CauseAnalysis’.

• This approach uses non-intrutechnologies to determine the actuacondition of a machine and its rate offailure.

This philosophy works hand in handwith Predictive Maintenance,eliminating the source of the fault totry to prevent it from re-occurring.

• This can be very effective inextending machine life with bigfinancial savings if implementedproperly.

Today’s Industrial Demandy

• It should be unacceptable to deliverl f f– less performance for more money

– same performance for more money• It could be acceptable to deliver

– same performance for less money– same performance for less money– more performance for the same money– more performance for more money

• The desire is More Performance for Less Money!!!!

Predictive Maintenance Objectivesj

• To confirm good‐condition machinesTo confirm good condition machines • To detect developing problems

d i h d i f h• To determine the nature and severity of the problem

• To schedule repairs that can best fit with production and maintenance needs

Predictive Maintenance Techniquesq

• Vibration measurementVibration measurement• Electrical testing

l i• Motor current analysis• Reciprocating machine testing• Thickness testing• Visual inspectionVisual inspection• And many more…

Predictive Maintenance Basic Facts

• Every mechanical or electrical faults on aEvery mechanical or electrical faults on a machine has a distinct vibration behavior.

• Any change in the vibration signature• Any change in the vibration signature indicates changes in the dynamic operating condition of the machinecondition of the machine

Predictive Maintenance Mechanism (VA)( )

• Establish a database of all the machines thatEstablish a database of all the machines that need to be monitored

• Establish a data collection route that best• Establish a data collection route that best optimize the data collection timeD l d i h d ll• Download route into the data collector

• Collect data• Upload collected data into the database

Predictive Maintenance Mechanism

• Run exception reports to detect theRun exception reports to detect the problematic machines

• Analyze only the machines in the exception• Analyze only the machines in the exception reportsG i k b f d• Generate repair work to be performed

• Again collect data on the machine on which work is being done.

Predictive Maintenance

StartRules

+Experi

Predictive Maintenance

YES

NO

Experi

Create Ref.

RegularMeas.

FaultDiagnostics

Fault correction

Compare limits

Inputm/c

specsspecsCreate

New Ref. & Limits

Vibration FundamentalsHow Much Vibration is Too Much ?

Vibration Fundamentals

1. Use Absolute Vibration LevelsGi b hi k- Given by machine makers

- Published Vibration Severity Standardseg. ISO 2372, VDI 2056, BS 4675

2. Use Relative Vibration Levels

ISO 10816‐3

11 0 44

ISO 10816 3

11 0.44

7.1 0.28

4,5 0.18

3,5 0.11,

2,8 0.07

2,3 0.04

1.4 0.03

0,71 0.02

mm/s rms inch/s rms

rigid flexible rigid flexible rigid flexible rigid flexible Foundationpumps > 15 kW medium sized machines large machinespumps > 15 kW medium sized machines large machines

radial, axial, mixed flow 15 kW < P 300 kW 300 kW < P < 50 MW Machine Typeintegrated driver external driver motors motors

160 mm H < 315 mm 315 mm HGroup 4 Group 3 Group 2 Group 1 Group

A newly commissionedB unrestricted long-term operationC restricted long-term operationD vibration causes damage

ISO 10816‐3140 5.51

113 4.45

90 3 54

ISO 10816 3

90 3.54

71 2.80

56 2.20

45 1.77

36 1.42

28 1.10

22 0.87

18 0.71

11 0.43

µm rms mil rms

rigid flexible rigid flexible rigid flexible rigid flexible Foundationrigid flexible rigid flexible rigid flexible rigid flexible Foundationpumps > 15 kW medium sized machines large machines

radial, axial, mixed flow 15 kW < P 300 kW 300 kW < P < 50 MW Machine Typeintegrated driver external driver motors motors

160 mm H < 315 mm 315 mm H60 3 5 3 5 Group 4 Group 3 Group 2 Group 1 Group

A newly commissionedB unrestricted long-term operationC restricted long-term operation

Vibration standards are guidelinesVibration standards are guidelines

N t4528

ISO2372 ( BS 4675 , VDI 2056 )

NotPermissible

NotPermissible

NotPermissible

Just Tolerable es =

8dB

20dB

2818

11.27 1 /s

RM

S

Just Tolerable

AllowableJust Tolerable

JustTolerable

All bl

2.5

time

times

= 2 7.1

4.52.81 8 ity

mm

/

GoodLarge Machines

with rigid and heavyfoundations whoseG d

AllowableAllowable

Good15 kW<

Medium Machines

10 1.8

1.121.710 45

Velo

ci

foundations whose natural Frequency

exceedsmachine speed

GoodSmall

Machines< 15 kW

<75kW

<300 kW on specialfoundations

0.450.280.18

Group K Group M Group G

Predictive Maintenance Database SetupPredictive Maintenance Database Setup

• Identify which machines to monitorIdentify which machines to monitor• Identify each machine characteristics

fi l i i f h• Define analysis requirements for each machine

• Define acceptable levels and alarm limits• Define data collection point locations and p

monitoring methods

Which Machine to Monitor?Which Machine to Monitor?

• Machine that are vital to the operationMachine that are vital to the operation• Machines that are expensive to repair

hi h bl k• Machines that are trouble makers• Machines that are in remote or inaccessible

locations

Why Machine Characteristics?Why Machine Characteristics?

• Initially the knowledge of the machine designInitially, the knowledge of the machine design and its operating characteristics is mandatory to successfully establish a good databaseto successfully establish a good database

• Later, this knowledge will provide the basis for analyzing the data accuratelyfor analyzing the data accurately

What Machine Information Is needed?What Machine Information Is needed?

• Machine speedp• Machine load• Bearing typeg yp• Coupling type• Gear type and teeth countyp• Blades and vanes• Machine drawings and typical operating g yp p g

conditions

Machine Analysis RequirementsMachine Analysis Requirements

• List all possible problems of the machineList all possible problems of the machine• Determine the particular effects that each

problem impose on the machineproblem impose on the machine• Determine the best method to monitor the

i f h blseverity of the problem

Manpower Required Depends onManpower Required Depends on

• Number of data collection points:Number of data collection points:– Complexity of the machine

Number of machines to be monitored– Number of machines to be monitored

Manpower Required Depends onManpower Required Depends on

• Analysis time requiredAnalysis time required – Complexity of the machine

Complexity of the problem– Complexity of the problem

• Frequency of analysis– Machine classification– Machine history

Machine ComplexityMachine Complexity

• Simplex machinesSimplex machines– Constant speed and load

Direct drive (coupling)– Direct drive (coupling)– 5‐10 measurement points


• Compound Machines:Compound Machines:– Constant speed and load

Intermediate drive (gearbox and belts)– Intermediate drive (gearbox and belts)– 10‐20 measurement points


• Complex MachinesComplex Machines– Variable speed and load

Multiple components– Multiple components– More than 20 measurement point

Machine Classifications:Machine Classifications:

• Vital Machines:Vital Machines:– Irreplaceable

Halt production– Halt production– Hard to find parts

E i t i– Expensive to repair

Machine ClassificationMachine Classification

• Critical Machines:Critical Machines:– Halt part of production

Expensive to repair– Expensive to repair– Costly replacement

H d t fi d t– Hard to find parts– Frequent repairs


• Support Machines:Support Machines:– Not too expensive to repair

Parts are readily available– Parts are readily available– Affect but don’t halt operation

M d t l tl i l t– Moderately costly repair or replacement


• Other Machines:Other Machines:– Parts are readily available

Replacement is easy and inexpensive– Replacement is easy and inexpensive– Do not affect operation directly

N hi t f i– No history of repair

Monitoring FrequencyMonitoring Frequency

• Vital Machines– On‐line Monitoring or every 1‐2 weeks

• Critical Machines– Every 2‐4 weeks

• Support Machines– Every 4‐8 weeks

• Other MachinesE 8 12 k– Every 8‐12 weeks

Methods of Data CollectionMethods of Data Collection

• On‐line Continuous MonitoringOn line Continuous Monitoring• Manual Data collection through portable data

collectioncollection

Continuous MonitoringContinuous Monitoring

• Real‐time data acquisition through dedicatedReal time data acquisition through dedicated sensors and instrumentation that monitor the machine during every second of its operationmachine during every second of its operation.

• Sometimes the instrumentation supplied with relays for automatic shutdown when alarmrelays for automatic shutdown when alarm levels are exceeded.

Manual Data AcquisitionManual Data Acquisition

• Using a portable instrumentation withUsing a portable instrumentation with sensors, data can be captured on a scheduled intervalsintervals

• Data then is dumped back to a PC for trending analysis and reportingtrending, analysis, and reporting.

Manpower Required for Data CollectionManpower Required for Data Collection

• Level of expertise: TechLevel of expertise: Tech• Amount of training: Minimum

f i i• Frequency of training: once a year• High level of commitment

Manpower Required for Data AnalysisManpower Required for Data Analysis

• Level of expertise: Engineer or highlyLevel of expertise: Engineer or highly knowledge mechanic

• Duties: analyze data and run and manage the• Duties: analyze data and run and manage the programA f i i V i• Amount of training: Varies

• High level of commitment

Predictive Maintenance• Results:

– Increase machine availability

– Save on maintenance cost– Reduce spare‐partsReduce spare parts

inventory– Increase machine life– Avoid unnecessary repairs– Avoid unnecessary repairs– Organize maintenance

activitiesI l t f t– Improve plant safety

Introduction to Vibration lAnalysis

Introduction to Vibration AnalysisIntroduction to Vibration Analysis

General Description‐VibrationGeneral Description Vibration• There are many different parameters we can measure to help us determine

machinery health:Voltage Current Power Flow TempPower Flow TempPressure Torque SpeedViscosity Density EmissionParticles Load• None contains as much information as the vibration signature!!!• None contains as much information as the vibration signature!!!• Not only does it provide the severity of the problem but can also point to the

source of the problem

• ‘Vibration’ can be simply stated as ‘A response to some form of excitation’ – The ‘excitation’ is generally referred to as the ‘Forcing Function’

• Vibration is the motion of a body about a reference position caused by a force

General Description – Forcing Function

• When a forcing function is applied to a shaft within a plain bearing the freeshaft within a plain bearing the free movement will cause the shaft to vibrate within the bearing– Here we are measuring actual shaft

movementmovement

When a forcing function is applied When a forcing function is applied to a shaft within a bearing housing where there is very little free movement, then the vibration will

i h itransmit to the casing– Measuring the casing movement of

a specific component as result of the forcing functionthe forcing function

Vibration from Mechanical Faultsb at o o ec a ca au ts

Vibration from Mechanical Faults

Vibration Characteristics

• Amplitude How Much

• Frequency How Often

• Phase. When

General Description – Measuring Response• You can also look at vibration as the amount of ‘Time’ it takes to complete

a particular cycle– If we examine the motion of a forcing function on a fan blade ‘Heavy Spot’ overIf we examine the motion of a forcing function on a fan blade Heavy Spot over

a period of time a distinct signature will occur.

This motion is called a sine wave. – The horizontal axis is

measuring Timemeasuring Time– The vertical axis is

measuring Amplitude This is known as a

‘Ti W f ’‘Time Waveform’– Amplitude versus Time

Time WaveformsTime Waveforms• Unfortunately there are multiple sources of forcing functions that can emit from a

machine or component. – Thus resulting in the time waveform becoming complex in nature

• The plot shown on the right is l ti f

A8 - Example 15Ex15 -F2V Fan Outboard Vertical

Route Waveform 22-Aug-02 11:33:16

PK 14950.3

0.4

a complex time waveform.– Amplitude versus Time

• This is just one format (domain) for analysing vibration data

PK = .1495 LOAD = 100.0 RPM = 832. (13.86 Hz)

PK(+) = .3263 PK(-) = .3572 CRESTF= 3.38

0.0

0.1

0.2

ratio

n in

G-s

Los - Example 8EX 8 -P2V Pump Outboard Vertical

Analyze Spectrum 15-Nov-95 10:00:16

RMS = 1.27 LOAD = 100.0 RPM = 737.

0.8

1.0

vibration data. • Data can also be analysed in a

‘Spectrum’ – (Amplitude Vs Frequency) through a process known as the FFT

-0.3

-0.2

-0.1Acc

ele RPS = 12.28

0.4

0.6

RM

S Ve

loci

ty in

mm

/Sec

known as the FFT 0 50 100 150 200 250 300 350

-0.4

Time in mSecs

Time: Ampl:

120.44 -.07595

0 6000 12000 18000 24000 30000

0

0.2

R

Freq: 736.86

Label: Looseness

0 6000 12000 18000 24000 30000Frequency in CPM

qOrdr: Spec:

1.000 .245

Fast Fourier Transform – FFT ProcessFast Fourier Transform FFT Process• When a problem starts to develop within a rotating component it will

generate a vibration signature. This signature should be captured in the time waveform

Di ti i hi th t i t b diffi lt h l ki t ti l t– Distinguishing that signature can be very difficult when looking at a time plot• To understand the problem we need to understand the frequency

– ‘How often is it occurring?’

• The ‘FFT’ is a process that determines the frequency of a signal from a time fwaveform.

• The FFT is named after an 18th century mathematician named ‘Jean Baptise Joseph Fourier’. He established:– ‘Any periodic signal can be represented as a series of sines and cosines’.

f k f d h ll l l h– Meaning if you take a time waveform and mathematically calculate the vibration frequency, it can be converted to a more familiar format

How the Vibration Spectrum is CreatedHow the Vibration Spectrum is Created

eA

mpl

itude

eitude

Am

plitu

de

Time

Am

pli

Frequency Domainq y• The frequency domain (Spectrum) plots the data as ‘Amplitude’ in the (Y)

axis and ‘Frequency’ in the (X) axis. This data is derived from the time domain – mathematical manipulation of the time waveform. p

• Recall the waveform and spectrum from the previous slide. If you tried to determine all the frequencies from the waveform plot, you would need all day just to analyse one point of data.

• As the FFT plots the frequencies from the waveform for you the analysis of this data becomes easier and reduces the amount of time needed for analysis of each point.

A8 - Example 15Ex15 -F2V Fan Outboard Vertical


PK = .1495 LOAD = 100.0 RPM = 832. (13.86 Hz)

PK(+) = .3263 PK(-) = .3572 CRESTF= 3.38 0.1

0.2

0.3

0.4

G-s



RMS = 1.27 LOAD = 100.0 RPM = 737. RPS = 12.28

0.8

1.0

m/S

ec

-0.3

-0.2

-0.1

0.0

Acc

eler

atio

n in

G

0.2

0.4

0.6

RM

S Ve

loci

ty in

mm

0 50 100 150 200 250 300 350

-0.4

Time in mSecs

Time: Ampl:

120.44 -.07595

Label: Looseness

0 6000 12000 18000 24000 30000

0

Frequency in CPM

Freq: Ordr: Spec:

736.86 1.000 .245

Introduction to Vibration lAnalysis

Units of MeasurementsUnits of Measurements

Measuring Amplitude and FrequencyMeasuring Amplitude and Frequency• You can measure amplitude from a time waveform as shown:

0 to Peakde

+ RMS

Average

Am

plitu

d

Time ‘t’

• The period ‘t’ is the time required for one revolution of the shaft in this illustration which equals one cycle of the waveform

-

Peak to Peak

illustration, which equals one cycle of the waveform– During this period, the amplitude of the waveform reaches a positive (+) peak,

returns to rest, and reaches a negative (‐) peak before returning to rest

Measuring Amplitude and FrequencyMeasuring Amplitude and Frequency• Peak (Pk) – Amplitude measured from the ‘at rest’ position (0) to the

highest value (0 to Peak)• Peak to Peak (Pk‐Pk) – Amplitude measured from the peak positive (+)Peak to Peak (Pk Pk) Amplitude measured from the peak positive (+)

value to the peak negative (‐) value• RMS (Root Mean Square) – obtained by averaging the square of the signal

level over a period of time and then taking the square root resultA (A ) A lit d l th t th k l f th• Average (Avg) – Amplitude value that averages the peak values of the waveform

• You can calculate the different amplitudes when one of the values are known:

RMS 0 707 times the peak value itude

+

Time ‘t’

0 to Peak

RMS

Average

– RMS = 0.707 times the peak value– Avg = 0.637 times the peak value– Pk‐Pk = 2 times the peak value -

Ampl

Time t

Peak to P kPeak

Measuring Amplitude and FrequencyMeasuring Amplitude and Frequency• Severity of a vibration problem can be determined by the amplitude of

the vibration.• We can measure amplitude in one of three ways

1. Displacement – measures the distance the shaft moves in relation to a reference point.

2. Velocity – measures the displacement of the shaft in relation to time3. Acceleration – measures the change in velocity in relation to time

• The most common industrial applications are:The most common industrial applications are:1. Displacement ‐Microns ‐ Peak to Peak value2. Velocity ‐mm/sec ‐ RMS3. Acceleration ‐ G‐s ‐ Peak value

– G‐s = 1 x force of gravity (G‐force)g y ( )

Amplitude RelationshipsAmplitude Relationships• The three types of amplitude measurements used to display data are directly related

to each other– Changing from one amplitude unit to the next alters the way in which the data is displayed

• Velocity is the default unit for standard data collection techniques

h d l fA8 - Example 15

Ex15 -F1H Fan Inboard HorizontalR t S t5

A8 - Example 15Ex15 -F1H Fan Inboard Horizontal

R S140

A8 - Example 15Ex15 -F1H Fan Inboard Horizontal

R t S t0.35 – High and low frequency events can be seen

Route Spectrum 22-Aug-02 11:30:50

OVERALL= 3.45 V-DG RMS = 3.44 LOAD = 100.0 RPM = 831. (13.85 Hz)

3

4

5

mm

/Sec

For normal operating speed ranges, velocity data

provides the best indication of hi diti


OVERALL= 3.45 V-DG P-P = 104.98 LOAD = 100.0 RPM = 831. (13.85 Hz)

80

100

120

140

in M

icro

ns

Low frequencies require very little force to move an object


OVERALL= 3.45 V-DG PK = .3909 LOAD = 100.0 RPM = 831. (13.85 Hz)

0 20

0.25

0.30

0.35

n in

G-s

Increasing the frequency that

Displacement measures low frequency events ignoring high frequencies

1

2

RM

S Ve

loci

ty in

machine condition

40

60

80

P-P

Dis

plac

emen

t

0.10

0.15

0.20

PK A

ccel

erat

ion Increasing the frequency that

the objects move with the same velocity, the force

needed to move it increases, thereby reducing the distance it

ignoring high frequencies– Relative shaft motion

Acceleration accentuates the high frequencies ignoring the low frequencies

Label: Large Fan Unit - Easy

0 20000 40000 60000

0

Frequency in CPMLabel: Large Fan Unit - Easy

0 20000 40000 60000

0

20

Frequency in CPMLabel: Large Fan Unit - Easy

0 20000 40000 60000

0

0.05

Frequency in CPM

can travel ignoring the low frequencies– Good for early bearing

detection (Whenever there is Metal to Metal Impacting involve)

Frequency UnitsFrequency Units• Frequency refers to how often something occurs:

– How often a shaft rotates?– How often a rolling element hits a defected race?

• There are three ways to express frequency:1. CPM – Cycles Per Minute

– 1CPM = 1RPM2. Hz – Cycles Per Secondy

– CPM / 603. Orders – Multiples of Turning Speed

– Frequency/Turning Speed

• Consider a motor has a rotational speed of 1485RPM, in terms of frequency this equates to:

1485 CPM (1rpm = 1cpm)– 1485 CPM (1rpm = 1cpm)– 24.75 Hz (1485/60) (minutes to seconds)– 1 Orders (1 x revolution of the shaft)

Frequency Unitsq y

• Shown below is a table showing the relationship between all three frequencyrelationship between all three frequency units with reference to the turning speed

Motor Turning Speed = 1500RPM

CPM 1500 2250 3000 6000 12000

Hz 25 37.5 50 100 200

Orders 1 1.5 2 4 8

Frequency DomainFrequency Domain

• The vibration analyst can divide the frequency domain data h finto three major areas of interest

1. Synchronous Equal to Ts or Harmonics of Ts2. Sub synchronous < 1 x Ts3 N h 1 T b t t i t3. Non synchronous > 1 x Ts but not an integer

• Note ‘Ts’ is the turning speed or rotational frequency (RPM) of the shaft at the position where you make the measurementmeasurement

• Each defect that can materialise in the frequency domain can be categorised into one of three types of energy listed aboveabove

– Knowing the type of energy within the data can help the analyst quickly eliminate 2/3rd of the fault types

Harmonic OrdersHarmonic Orders• Harmonics are cursors that are exact multiples of the primary frequency

– They are used to locate other frequencies related to the primary cursor

Los - Example 3EX3 -P2V Pump Outboard Vertical


RMS = 1.27 LOAD = 100.0 RPM = 737.

0.8

1.0

Here the primary cursor is at 1 Order (1xTs). All the other cursors

RPS = 12.28

0 4

0.6

Velo

city

in m

m/S

ec

( )are harmonics (exact multiples of the primary cursor)

0.2

0.4

RM

S V

• Therefore:– When the primary cursors is located on 1Order all the harmonics will be

h0 6000 12000 18000 24000 30000

0

Frequency in CPM

Freq: Ordr: Spec:

736.86 1.000 .245

synchronous– Harmonic cursors can be used to show non‐synchronous and sub‐

synchronous harmonics depending upon the energy of the primary frequencyfrequency

Energy in the SpectrumEnergy in the SpectrumC1 - Example 4

E4 -MOH MOTOR OUTBOARD HORIZONTAL0 5 Route Spectrum

09-Feb-00 12:41:33

OVRALL= .5785 V-DG RMS = .5716 LOAD = 100.0 RPM = 2937.

0.4

0.5

RPS = 48.95

0.3

ocity

in m

m/S

ec

0 1

0.2

RM

S Ve

l

0 20 40 60 80 100 120 140 160

0

0.1

Freq: 2.9370 20 40 60 80 100 120 140 160

Frequency in kCPMOrdr: Spec:

1.000 .01038

Synchronous Energyy gy

• Synchronous energy ‐ related to i d



RMS = 1.27 LOAD = 100.0 0.8

1.0

turning speed.

• We can see from the spectrum that the first peak

RPM = 737. RPS = 12.28

0.6

0.8

city

in m

m/S

ecAll th th k

spectrum that the first peak is at 1 Orders (which means it is 1 x turning speed)

0.2

0.4

RM

S Ve

loc

• All the other peaks are harmonics off, which means they are related to the first peak 0 6000 12000 18000 24000 30000

0

Frequency in CPM

Freq: Ordr: Spec:

736.86 1.000

245

Examples of synchronous energy:1) Imbalance 2) Misalignment 3) Gearmesh

Label: LoosenessFrequency in CPM Spec: .245

Non‐Synchronous Energyy gy

• Non‐synchronous energy ‐not related to turning speed

BF - Example 5E5 -R4A ROLL BRG. #4 - AXIAL

Route Spectrum 12-Jul-96 17:16:42

OVRALL= 2.63 V-DG RMS = 2.69 LOAD 100 0

1.6

1.8

2.0

not related to turning speed

• We can see from the spectrum that the first

LOAD = 100.0 MPM = 3225. RPM = 380.

1.0

1.2

1.4

ocity

in m

m/S

ec

spectrum that the first peak is at 10.24 Orders. This is not related to turning speed.

0.4

0.6

0.8

RM

S Ve

lturning speed.

Label: Outer Race DefectPriority: 1

0 6000 12000 18000 24000 30000

0

0.2

Frequency in CPM

Freq: Ordr: Spec:

3888.9 10.24 .748

• Examples of non-synchronous energy:• Bearings Multiples of belt frequency Other Machine Speeds

Priority: 1

Sub‐Synchronous Energyy gy• Sub‐synchronous energy ‐ Less

than turning speedthan turning speed

• The spectrum shows the first impacting peak below 1 Order. This is sub‐synchronous energy

• Examples of sub‐synchronous energy are:

• Belt Frequencies• Other Machine SpeedsOther Machine Speeds• Cage Frequencies

Energy in a SpectrumEnergy in a Spectrum

Synchronous– N x RPM where N is an integer g

Sub-synchronous– <1 x RPM

Non-synchronous– F x RPM where F is >1x RPM but not integerg

Causes of Sub Synchronous Energyy gy• Frequencies that show

below the rotational frequency (Less than 1 Order) are sub synchronous.

Another component– Another component– Cage frequencies– Primary belt frequency– Oil whirl (plain bearings)

Causes of Synchronous Energyy gy• Frequencies that are equal

too or a direct multiple of running speed are Synchronous

• Possible causes of• Possible causes of Synchronous energy are:– Imbalance



RMS = 1.27

1.0

– Misalignment– Looseness– Vane pass frequency

RMS 1.27 LOAD = 100.0 RPM = 737. RPS = 12.28

0.4

0.6

0.8

RM

S Ve

loci

ty in

mm

/Sec

Vane pass frequency– Gears etc

Label: Looseness

0 6000 12000 18000 24000 30000

0

0.2

Frequency in CPM

Freq: Ordr: Spec:

736.86 1.000 .245

Causes of Non Synchronous Energyy gy• Frequencies above (but not

integer multiples of) turning speed are non synchronous.

• Possible causes of non synchronous energy are:synchronous energy are:– Another component – Antifriction bearings

BF - Example 5

– Electrical– System resonances– Multiples of belt frequency

E5 -R4A ROLL BRG. #4 - AXIARoute Spectrum 12-Jul-96 17:16:42

OVRALL= 2.63 V-DG RMS = 2.69 LOAD = 100.0 MPM = 3225. RPM = 380.

1 0

1.2

1.4

1.6

1.8

2.0

ty in

mm

/Sec

Multiples of belt frequency

0

0.2

0.4

0.6

0.8

1.0R

MS

Velo

cit

Label: Outer Race DefectPriority: 1

0 6000 12000 18000 24000 30000Frequency in CPM

Freq: Ordr: Spec:

3888.9 10.24 .748

Lines of ResolutionLines of ResolutionLOR

Lines of ResolutionLines of Resolution• Lines of Resolution (LOR) determine how clear the peaks(data) are

defined within our spectrum.

• Example.

• The more lines we have over the same F‐max (Maximum frequency scale). The more accurate our data will be

– The diagram below shows data that has been collected using 400 LOR. Notice how the top of the peaks are capped. When the LOR are increased the data becomes more accurate.

Lines of ResolutionL2 - TA 16

TA16 -M1H Motor Outboard HorizontalAnalyze Spectrum 13-Mar-01 09:13:53

PK 7078

0.5

L2 - TA 16TA16 -M1H Motor Outboard Horizontal

Analyze Spectrum 13-Mar-01 09:14:16

PK 3852

0.20

• The spectrum shown displays PK = .7078 LOAD = 100.0 RPM = 1496. RPS = 24.94

0.3

0.4

in G

-s

PK = .3852 LOAD = 100.0 RPM = 1497. RPS = 24.95

0.12

0.16

in G

-s

• The spectrum shown displays data at 800 L.O.R with an Fmax of 1600 Hz

0.2

PK A

ccel

erat

ion

0.08

PK A

ccel

erat

ion

The second spectrum displays the same data but

0.10.04

displays the same data but with 3200 L.O.R over the same Fmax

0 400 800 1200 1600

0

Frequency in Hz0 400 800 1200 1600

0

Frequency in Hz

Lines of ResolutionLines of Resolution• The range of LOR settings that we can choose from on the analyzer

100 Li d 12800 Listarts at 100 Lines and go up to 12800 Lines.

• The average number of LOR is around 1600 Lines for a typicalThe average number of LOR is around 1600 Lines for a typical motor/pump set up

To change the LOR settings we need to alter our parameter setTo change the LOR settings we need to alter our parameter set. This is done in the Database Setup program

Remember. If you double your lines of resolution you double your data collection time.

Spectral Summaryp yBF - Example 5

E5 -R4A ROLL BRG. #4 - AXIALRoute Spectrum 12-Jul-96 17:16:42

1 8

2.0

Energy Types H iOVRALL= 2.63 V-DG RMS = 2.69 LOAD = 100.0 MPM = 3225. RPM = 380. 1.4

1.6

1.8

Sec

s spla

cem

ent Energy Types

Synchronous

Non Synchronous

HarmonicsMultiples of Primary

Frequency

0.8

1.0

1.2

S Ve

loci

ty in

mm

/S

mpl

itude

sVe

loci

ty D

is Sub SynchronousResolution

Clarity of the spectral d t

0.4

0.6

0.8

RM

S

Am

cele

ratio

n, V data

0 6000 12000 18000 24000 30000

0

0.2

Frequency in CPM

Freq: Ordr: Spec:

3888.9 10.24 .748

Acc

FrequencyLabel: Outer Race DefectPriority: 1

pFrequencyHz (CPS), CPM, Orders

Introduction to Vibration Analysis

Data CollectionData Collection

Transducers and Mounting Techniquesg q• Although there are many different types of transducers

available, the most common type used for day to day data collection are Accelerometers.

• These transducers provide an electrical charge proportional to acceleration by stressing piezoelectric crystals typicallyacceleration by stressing piezoelectric crystals typically 100mV/g sensors are used.

Data Qualityy• Whether it is your job to collect the data and/or analyse the

data it is important to understand that the technologies will not give you the answer to a machines problem unless you have collected meaningful, quality data

• There are certain considerations that must be taken prior to any data being collected, these are:– A good understanding of the internal make up of the machine in orderA good understanding of the internal make up of the machine, in order

to understand the best transmission path for data collection ‐ bearing locations, load zones etc.

– Ensure data is collected in a repeatable manner so we can compare p ptwo or more readings to each other ‐ trending purposes

– Variable speed machines ‐ it is very important to collect data with the correct running speed enter into the analyser

Transmission Path• Damaged caused to a machine component will cause a certain

amount of vibration/sound or heat to propagate away from the initial impactinitial impact.– It is the effect of the impact/force that we are trying to detect

• In many cases the further you are away from the initial event the weaker the signal will become, resulting in the data appearing to be lower in value. – In more extreme cases the impact can be lost amongst other machine

noise by the time it has reached your transducer, resulting in no detection of a machine problem.

• Usually the best place to acquire data from a machine, is at the bearings. – This is because the bearings are the only part of the machine that connect the

internal rotating components to the stationary components (Casing)

Repeatable Datap• Collect data in the same manner each time.

– This consistency will allow you to trend the machinery condition and y y yproperly judge the progression of faults

• In order to aid with repeatable data the analyser requests for d b ll d i i l i h hidata to be collected in certain locations on the machine. – These are called ‘Measurement Points’

A measurement point is determined by three characters and a description.

Each character refers to a particular place on the machine being monitored– E g M1H is a typical measurement pointE.g. M1H is a typical measurement point

Measurement PointsMeasurement Points• A measurement point is defined as three alpha numeric digits

along with their respective definition– Orientation and location on each componentOrientation and location on each component

• The image on the right is taken from the screen of the 2130 analyser duringthe 2130 analyser during a collection ‘route’

• The measurement ‘point identifier’ can be seen inidentifier can be seen in the top right while the ‘point description’ is shown just below j

Measurement PointsMeasurement Points• The first letter of the ‘Point Identifier’ refers to the type of

machine being monitored– M =Motor P = Pump F = FanM Motor P Pump F Fan

• The second character represented by a number indicates the location on the machine– Inboard (Drive End) or Outboard (Non Drive End)

• The third letter refers to the orientation of the sensor or the type of processing being done by the analyser– H = Horizontal V = Vertical P = Peakvue Change in DSP of Analyser

Measurement PointsMeasurement Points• The following example shows how the numbering system

changes as you cross from one component to the next

121

2

M1H – Motor Outboard Horizontal M1P – Motor Outboard Horizontal Peakvue

2 P1H – Pump Inboard Horizontal P1P – Pump Inboard Horizontal Peakvue

• Notice how the ‘1’ is not always the ‘Outboard’ – This changes when the next component is required for data collection

• The numbering system starts from 1 again• The numbering system starts from 1 again

Introduction to Vibration AnalysisAnalysis

Fault DiagnosticsFault DiagnosticsImbalance, Misalignment, Looseness

Fault DiagnosticsFault Diagnostics

• Each type of machine fault or defect reveals a specific yp f f pvibration characteristic in the spectrum and time waveform domain that distinguish that fault from another.Si l b i i b i k l d f th tt d• Simply by gaining a basic knowledge of these patterns and applying a few rules of thumb we can start to analyse machine vibration and prevent machine failure.

• This section concentrates the characteristics / patterns and rules that apply to diagnose machine faults such as:

I b l Mi li t L G– Imbalance Misalignment Looseness Gears– Bearings (Peakvue) Belts Electrical– Resonance

ImbalanceImbalance

ImbalanceImbalance• Imbalance (Unbalance) occurs when the centre of mass differs

from the centre of rotation.• If the centre of mass changes on the rotor due to a heavy spot

or some other influence then a centrifugal force is produced. This results in the centre of rotation being offset from theThis results in the centre of rotation being offset from the centre of mass causing the vibration to increase at the rotational frequency.

Imbalance (Types)( yp )

ImbalanceImbalance• Causes of Imbalance

– Improper Assembly– Material build up / dirtMaterial build up / dirt– Wear to components– Broken or missing parts

All of the above conditions will result in an unbalanced state

• Diagnostic Rules for Imbalance– Periodic non‐impacting sinusoidal waveformPeriodic non impacting sinusoidal waveform– Spectral peak at 1xTs (1 Order)– Very little axial vibration – Similar amplitudes between horizontal and vertical plainsp p– Synchronous fault type– Amplitudes will increase with speed– Very low harmonics of 1xTs

Imbalance Spectral DataImbalance Spectral Data• The spectrum shown represents a simple unbalance state

– Single peak at 1xTs (1 Order)– Little indication of harmonics

IF - Example 2Ex2 -F1H Fan Inboard Horizontal

Route Spectrum 6

Little indication of harmonics

16-Sep-99 08:36:29

OVRALL= 4.58 V-DG RMS = 4.56 LOAD = 100.0 RPM = 3000. RPS = 50.00

4

5

/Sec

• What should the waveform show?

3

MS

Velo

city

in m

m/

1

2R

0 20000 40000 60000 80000

0

Frequency in CPM

Freq: Ordr: Spec:

3000.0 1.000 4.539

Imbalance Waveform DataImbalance Waveform Data• Despite the waveform being displayed in Acceleration

– Default unit for route based waveform data• There is still a predominant sinusoidal waveform pattern• There is still a predominant sinusoidal waveform pattern

– 1 x Revolution sine wave

IF - Example 2Ex2 -F1H Fan Inboard Horizontal

1 0 Waveform Display 02-Feb-00 15:13:51

PK = .5289 LOAD = 100.0 RPM = 2985. RPS = 49.76

PK( ) 83320.4

0.6

0.8

1.0

PK(+) = .8332 PK(-) = .8893 CRESTF= 2.38

-0.2

-0.0

0.2

Acc

eler

atio

n in

G-s

Ch i th it t l it ld d th t f hi h-0.8

-0.6

-0.4

A

• Changing the units to velocity would reduce the amount of high frequency noise residing on the waveform 0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

-1.0

Revolution Number

Imbalance Trend DataImbalance Trend Data• The trend data is a good way of determining if there has been

a change in condition, as this plots amplitude against time (where time is in days)( e e t e s days)

• Here the 1xTs parameter is being trended– Vibration has been steady at 3mm/sec for a period of time– A sudden change instate should alert the analyst to a fault developing

E02N - JB1420C CONDY RECOVERY PUMPJB1420C -M1H Motor Outboard Horizontal

Trend Display of 1xTS

12

14

-- Baseline -- Value: 3.063 Date: 07-Apr-00

8

10

ocity

in m

m/S

ec

FAULT

2

4

6

RM

S Ve

lo

0 100 200 300 400 500

0

Days: 07-Apr-00 To 21-May-01

Date: Time: Ampl:

21-May-01 14:24:29 11.21

Imbalance Problem ‐ PracticalImbalance Problem Practical• The following fan unit has an imbalance present on the rotor.

– 1xTs Peak in the Spectrum– 1xTs Peak in the Waveform1xTs Peak in the Waveform

ImbalanceIF - Example 2

Ex2 -F1H Fan Inboard HorizontalRoute Spectrum 16-Sep-99 08:36:29

OVRALL= 4.58 V-DG RMS = 4.56

5

6

LOAD = 100.0 RPM = 3000. RPS = 50.00

0 20000 40000 60000 80000

0

1

2

3

4

Frequency in CPM

RM

S Ve

loci

ty in

mm

/Sec

Freq: Ordr: Spec:

3000.0 1.000

4 539eque cy C Spec: 4.539IF - Example 2Ex2 -F1H Fan Inboard Horizontal

Waveform Display 02-Feb-00 15:13:51

PK = .5289 LOAD = 100.0 RPM = 2985. RPS = 49.76

PK(+) = .8332 PK(-) = .8893 CRESTF= 2.38

-0.6

-0.4

-0.2

-0.0

0.2

0.4

0.6

0.8

1.0

Acc

eler

atio

n in

G-s

• What would happen to the data if the following occurred to th f ?

0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

-1.0

-0.8

Revolution Number

the fan?

Imbalance Case Study 1Imbalance Case Study 1• Background• The following data is taken from a Recirculation Fan designed to circulate

the hot air through an Oven to aid with drying the process. The oven is vertically mounted and the product comes into the oven from the top and exits at the bottom. There is one Recirculation Fan and one Extract Fan. L f f ti f ith f lt i th b i t k ffliLoss of function from either fan results in the oven being taken offline.

• The spectral plots shows

Bm/c - TOP RECIRC FANTRF B m/c -F2H Fan Outboard Horizontal

ROUTE SPECTRUM 08-Nov-04 14:16:45

OVERALL= 4 70 V DG

6

The spectral plots shows a dominant 1xTs peak (1 Order) with very little other vibration present

OVERALL= 4.70 V-DG RMS = 4.70 LOAD = 100.0 RPM = 1246. RPS = 20.77

3

4

5oc

ity in

mm

/Sec

1

2RM

S Ve

lo

0 20000 40000 60000 80000 100000

0

Frequency in CPM

Freq: Ordr: Spec:

1246.3 1.000 4.673

Imbalance Case Study 1Imbalance Case Study 1• The waveform from this data shown on the following page

represents a sinusoidal waveform clearly shown once per revolution of the shaft – here the waveform is displayed in e o ut o o t e s a t e e t e a e o s d sp ayedvelocity.

• All indications pointBm/c - TOP RECIRC FAN

TRF B m/c -F2H Fan Outboard HorizontalRoute Waveform 08-Nov-04 14:16:45

8

• All indications point towards an imbalance problem. The

08-Nov-04 14:16:45

RMS = 4.66 LOAD = 100.0 RPM = 1246. RPS = 20.77

PK(+) = 7.03 PK(-) = 7.40 CRESTF= 1.59 0

4

m/S

ec

amplitudes should be checked in both radial directions to confirm

8

-4

Velo

city

in m

m

this problem 0 1 2 3 4 5

-12

-8

Revolution Number

Imbalance Case Study 1Imbalance Case Study 1• The plot shown indicates a multi spectral plot showing all the

radial directions.

Bm/c - TOP RECIRC FANTRF B m/c - Multiple Points (08-Nov-04)

Max Amp 4.27

Plot

• It is clear that thecity

in m

m/S

ec

Plot Scale

5

TRF B m/c -F2H

TRF B m/c -F2V

It is clear that the amplitudes are common to both b i I b d

RM

S Ve

loc

0 TRF B m/c -F1V

bearing – Inboard and Outboard in all radial plains Frequency in CPM

0 8000 16000 24000TRF B m/c -F1H

Imbalance Case Study 1Imbalance Case Study 1• The trend data for the 1xTs parameter has been steady for a

considerable amount of time. The last two readings has shown a significant increase in amplitudea s g ca t c ease a p tude

Bm/c - TOP RECIRC FANTRF B m/c -F2H Fan Outboard Horizontal

Trend Display of

6

• The fan was

1xTS

-- Baseline -- Value: .428 Date: 24-Nov-00

4

5

y in

mm

/Sec

The fan was recommended to be cleaned at the next

il bl t it1

2

3

RM

S Ve

loci

ty

ALERT

FAULT

available opportunity and for it to be re‐tested afterwards

0 300 600 900 1200 1500

0

Days: 24-Nov-00 To 08-Nov-04

ALERT

Date: Time: Ampl:

08-Nov-04 14:16:38 4.688

Imbalance SummaryImbalance Summary

• Diagnostic Rules for ImbalanceDiagnostic Rules for Imbalance– Periodic non‐impacting sinusoidal waveformSpectral peak at 1xTs (1 Order)– Spectral peak at 1xTs (1 Order)

– Very little axial vibration Si il lit d b t h i t l d– Similar amplitudes between horizontal and vertical plainsSynchronous fault type– Synchronous fault type

– Amplitudes will increase with speedV l h i f 1 T– Very low harmonics of 1xTs

MisalignmentMisalignment

MisalignmentMisalignment• When two mating shafts do not share the same collinear axis

then misalignment is induced.

• Misalignment is one of the primary reasons for premature machine failure. The forces that are exerted on the machine and its components when in a misaligned state are greatly increased from normal operating conditions p g

MisalignmentMisalignment• Operational Deflection Shape (ODS) is a technique that

machine movement based upon the phase and magnitude of data collected from the analyser. Shown below is an image data co ected o t e a a yse S o be o s a agefrom the ODS illustrating the forces that are exerted onto the machine and components when running in a misaligned condition

MisalignmentMisalignment• Misalignment can be broken into three basic categories, these

are:

• Angular – Where the shaft centrelines cross producing a 1xTs peak axially

Offset – Where the shaft centrelines are parallel but they do not meet producing a radial 2xTs peakp g p

More commonly seen – A combination of the above

Misalignmentg

Misalignmentg• Another common problem

associated with alignment is ‘b i i li t’‘bearing misalignment’.

• Bearing misalignment occurs when the bearings are not

t d i th l imounted in the same plain possibly due to: – one or more of the bearings

being cocked in the housingbeing cocked in the housing– The machine itself distorts due to

thermal growth or soft foot conditions

– Misalignment at the drive causes shaft bending.

MisalignmentMisalignment• Diagnostic Rules for Misalignment

– High axial levels of vibration at 1xTsg– High radial levels of vibration at 2xTs– Repeatable period sine waveform showing 1 or 2 clear peaks per

revolution (Most likely “M” or “W” shape)revolution (Most likely M or W shape)– Data can usually be seen across the coupling

• Diagnostic Rules for Bearing MisalignmentDiagnostic Rules for Bearing Misalignment– High levels of vibration at 1xTs and 2xTs– Repeatable periodic sine waveform showing 1 or 2 clear peaks per

l tirevolution– Data usually shown either the driver or driven component

Offset Misalignment Spectral Datag p• The spectral data shown represents a simple misalignment

plot. – The primary cursor denotes the 1xTs peak while the harmonic cursors

indicate a larger 2xTs peak. This type of data is common to that of Offset Misalignment

ST.1 - Raw Water PumpP029 -M2H

Route Spectrum 15-FEB-93 11:04:18

OVRALL= 6.50 V-DG RMS = 6.47 LOAD = 100.0

6

7

c LOAD 100.0 RPM = 2976. RPS = 49.61

3

4

5

MS

Velo

city

in m

m/S

ec

1

2

RM

0 10000 20000 30000 40000 50000

0

Frequency in CPM

Freq: Ordr: Spec:

2925.0 .983 2.046

Angular Misalignment Spectral Datag g p• The spectral data below represents a simple misalignment

plot. – The primary cursor denotes the 1xTs peak while the data was taken

in the axial direction. This type of data is common to that of Angular Misalignment

B29 - PUMP NO 33601PUM003-M2A Motor Inboard Axial


OVERALL= 6.33 V-DG RMS = 6.31 LOAD = 100.0 RPM 1071 (17 84 H )6

7

8

RPM = 1071. (17.84 Hz)

3

4

5

S Ve

loci

ty in

mm

/Sec

1

2

3

RM

S

0 30 60 90 120

0

Frequency in kCPM

Freq: Ordr: Spec:

1.071 1.000 5.966

Offset Misalignment Waveform Datag• The waveform above is showing two clear peaks per

revolution of the shaft. This type of waveform resembling an ‘M’ or ‘W’ shape is common to offset misalignment. o s ape s co o to o set sa g e t– Data shown in velocity

ST.1 - Raw Water PumpP029 -M2H

Waveform Display 26-MAR-93 13:32:52

RMS = 17.00 LOAD = 100.0 RPM = 2996. RPS 49 9320

30

40

RPS = 49.93

PK(+) = 30.66 PK(-) = 26.81 CRESTF= 1.82

0

10

20

Velo

city

in m

m/S

ec

-20

-10

0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

-30

Revolution Number

Misalignment WaveformMisalignment Waveform• The waveform data shown above is predominantly showing

one sinusoidal waveform per revolution of the shaft. – Here the data is shown AccelerationHere the data is shown Acceleration

B29 - PUMP NO 33601PUM003 M2A Motor Inboard Axia3601PUM003-M2A Motor Inboard Axia


PK = .2596 LOAD = 100.0 RPM = 1071. (17.84 Hz)

PK(+) = .6277 0.4

0.6

0.8

PK(-) = .5683 CRESTF= 3.42

-0.2

0.0

0.2

Acc

eler

atio

n in

G-s

-0 8

-0.6

-0.4

0 0.4 0.8 1.2 1.6 2.0 2.4 2.8 3.2 3.6

-0.8

Revolution Number

Rev : Ampl:

.680 -.306

Case Study 3 – Kiln Main Motor Gearboxy• Introduction• The Kiln drive gearbox motor had been replaced during aThe Kiln drive gearbox motor had been replaced during a

planned plant shutdown. • During the start up of the plant after the shutdown it was

noted that the motor and gearbox were excessively noisy. Vibration data was taken during the run up of the plant to determine the cause of the problem.p

Main Motor

Kiln GearboxKiln Gearbox

Case Study 3 – Kiln Main Motor Gearboxy• The spectral plot shown above is the data taken from the drive

end of the motor. Here there is a dominant 2xTs peak.

04 - Kiln Drive0804 -M2H Motor Inboard Horizontal

Route Spectrum 29-Mar-01 11:33:43

OVRALL= 2.47 V-DG RMS = 2.46

2.1

2.4

In addition to the misalignment the excessive forces being applied to the machine were causing excessive LOAD = 100.0

RPM = 1418. RPS = 23.64

1.2

1.5

1.8

Velo

city

in m

m/S

ec

being applied to the machine were causing excessive loading on the gears.

04 - Kiln Drive0804 -G2A Shaft 01 Outboard Axial

12345

Max Amp 5.98

0.3

0.6

0.9

RM

S V

Am

plitu

de -

Mix

ed U

nits

0

15:02:0023-Jan-01

12:11:1226-Mar-01

09:40:0929-Mar-01

09:40:2029-Mar-01

After Shutdown

0 20000 40000 60000

0

Frequency in CPM

Freq: Ordr: Spec:

1418.3 1.000 1.346

Frequency in kCPM0 60 120 180 240 300

14:06:5608-Aug-00

09:04:1725-Oct-00Before

Shutdown

Case Study 3 – Kiln Main Motor Gearboxy• During data collection it was also observed that the grouting

around the front feet of the motor had begun to crack as a result of the excessive force being applied to the motor base and feet due to the misalignment.

• Conclusion• Conclusion– It was confirmed the engineers that replaced the motor during the

shutdown and assumed as the motor was a like for like swap, as long as th k t th hi i th t l th li t tthey kept the shims in the correct place then alignment was not necessary.

– Corrective action was required and production was stopped so the t ld b li d d th ti dmotor could be re‐aligned and the mountings re‐secured.

Misalignment SummaryMisalignment Summary

• Diagnostic Rules for MisalignmentDiagnostic Rules for Misalignment– Periodic non‐impacting sinusoidal waveform with 1 or 2 clear peaks per revolution (Most Likely “M”1 or 2 clear peaks per revolution (Most Likely M or “W” shape)

– Spectral peak at 1xTs and 2xTsSpectral peak at 1xTs and 2xTs– Axial vibration at 1xTs– Synchronous fault typeSynchronous fault type– Data can be seen across the coupling or across the component itselfcomponent itself

LoosenessLooseness

How would looseness ?

LoosenessLooseness• Looseness can be broken down into two main categories,

Structural and Component Structural looseness occurs when there is free

movement within the machines support structure causing excessive vibration. This can be a result of:excessive vibration. This can be a result of:– Loose support bolts to the components feet and supports– Cracked welds– Deterioration of the base itself.

Component looseness generally occurs when there is excessive clearance to the components within the pmachine, such as:– Excessive clearance between the shaft and bearings

Excessive clearance between the shaft and an impeller etc– Excessive clearance between the shaft and an impeller etc.

LoosenessLooseness• Diagnostic Rules for Looseness

– Multiple harmonics of the 1xTs peak ‐ Structuralp p– Multiple Harmonics of the component that is loose ‐ Component– Number of harmonics will increase as the looseness progresses

Random non periodic waveform Structural– Random, non‐periodic waveform ‐ Structural– Waveform shows predominant impacts ‐ Component– Raised noise level around the 1xTs + harmonics– Half harmonics may also be present– Can be present in all Directions

Looseness Spectral Data (Structural)p ( )

• The spectral plot shown is demonstrating Looseness. – The 1xTs peak has been highlighted by the primary cursor and the p g g y p y

relevant harmonics have been displayed.– Multiple harmonics of 1xTs are shown up to around 10 orders of 1xTs.

40 - Kiln Main DriveM4441 -G2H Shaft 01 Outboard Horizontal

Route Spectrum 06-Nov-02 11:02:11

OVERALL= 5.22 V-DG RMS = 5.22 LOAD = 100.0 3 0

3.5

4.0

RPM = 635. (10.58 Hz)

2.0

2.5

3.0

Velo

city

in m

m/S

ec

0.5

1.0

1.5

RM

S

0 200 400 600 800 1000

0

Frequency in Hz

Freq: Ordr: Spec:

10.58 1.000 3.088

Looseness Spectral Data (Component)p ( p )

• The spectral plot shown is demonstrating rotational Looseness. – The primary cursor is on 5xTs peak

• The 5 Order peak is vane pass frequency (5 vanes on the impeller)

– Multiple harmonics of 5xTs are shown indicating the impeller has p g pcome loose. L1 - Example 9

Ex 9 -P2A Pump Outboard AxialRoute Spectrum* 17-Aug-01 08:52:02

OVERALL= 6.62 V-DG RMS = 6.13 LOAD = 100 0

1.2

1.5

The raised noise level around the vane pass frequency is LOAD = 100.0

RPM = 2974. (49.57 Hz)

0 6

0.9

Velo

city

in m

m/S

ec

the vane pass frequency is common to a pumping problem known as Cavitation– This would be the likely cause

0.3

0.6

RM

S V This would be the likely cause

of the impeller problem

Label: Centrifugal Pump - Medium

0 40 80 120 160 200 240

0

Frequency in kCPM

Freq: Ordr: Spec:

14.88 5.002 .742

Looseness Waveform Data• Here the waveform is demonstrating a lot of energy and

appears to be more random and non‐periodic.– Displaying the waveform in velocity may help to show the random

non‐periodic pattern.


Route Waveform 06-Nov-02 11:02:11

RMS = .3174 LOAD = 100.0 RPM = 635. (10.58 Hz)

0.8

1.2

PK(+) = .9797 PK(-) = .9874 CRESTF= 3.11

0.0

0.4

eler

atio

n in

G-s

-0.8

-0.4Acc

0 50 100 150 200 250 300 350 400

-1.2

Time in mSecs

Looseness Trend DataLooseness Trend Data• Here the trend plot is showing the parameter labelled as the 3‐

15xTs. This is measuring the amount of energy from 3 orders to 15 orders, which is where the harmonics of looseness will 5 o de s, c s e e t e a o cs o oose essappear.


Trend Display of 3-15xTS

-- Baseline -- Value: 8376

7

8

Value: .837 Date: 28-Feb-02

4

5

Velo

city

in m

m/S

ec

ALERT

FAULT

1

2

3

RM

S

0 10 20 30 40 50

0

Days: 28-Feb-02 To 16-Apr-02

Case Study 4 – Reciprocator FanCase Study 4 Reciprocator Fan• Introduction• Data had been collected on the following fan for severalData had been collected on the following fan for several

months as part of a routine periodic vibration routine. During a routine visit to the machine it was observed that there was a lot of low frequency activity showing around the bearing onlot of low frequency activity showing around the bearing on the inboard of the fan (F1H)

Case Study 4 – Reciprocator FanCase Study 4 Reciprocator Fan• The multiple plots shown above indicate the change over time

from the data taken on F1H. – It is quite apparent that the data shown here is indicating multiple

harmonics of the 1xTs frequency (the rise energy as you move further away from the 1xTs).

– This type of data is common to that of a looseness problem.40 - Precip Fan

M2237 -F1H Fan Inboard Horizontal

2.0

2.4Max Amp 2.74

ty in

mm

/Sec

0

0.4

0.8

1.2

1.6

11:00:0229-Oct-02

RM

S Ve

loci

t

15:30:1829-Aug-02

09:13:2618-Sep-02

Frequency in Hz0 300 600 900 1200

11:14:4822-Aug-02 RPM= 998.9

11:00:02 29-Oct-02

Freq: Ordr: Sp 4:

16.65 1.000 2.811

Case Study 4 – Reciprocator Fany p• The waveform data taken for this particular point is not

showing a random type of waveform pattern which you would expect from Structural looseness, but there is a more ou d e pect o St uctu a oose ess, but t e e s a o ea repeatable (timed interval) pattern.

40 - Precip FanM2237 -F1H Fan Inboard Horizonta

3 Analyze Waveform 18-Sep-02 09:24:16

RMS = .3747 LOAD = 100.0 RPM = 998. (16.63 Hz)

PK(+) = 2.36 1

2

3

PK(-) = 2.83 CRESTF= 7.55

-1

0

Acc

eler

atio

n in

G-s

-3

-2

A

0 100 200 300 400 500 600 700 800

-4

Time in mSecs

Case Study 4 – Reciprocator FanCase Study 4 Reciprocator Fan• This type of waveform would more be indicating Component

looseness and may indicate a problem with a loose bearing.

• Conclusion• It was recommended that the bearing should be inspected at

the next available opportunity. – Upon inspection it was found that the bearing was a ‘Taper‐Lock’Upon inspection it was found that the bearing was a Taper Lock

bearing and the taper lock was loose, thus resulting in excessive clearance between the bearing and the rotor.

Looseness Summaryy

• Diagnostic Rules for LoosenessDiagnostic Rules for Looseness– Multiple harmonics of the 1xTs peak– Number of harmonics will increase as the looseness progresses

– Random, non‐periodic waveform – Structuralf h d i i– Waveform shows predominant impacts ‐ Component

– Raised noise level around the 1xTs + harmonicsHalf harmonics may also be present– Half harmonics may also be present

– Can be present in all Directions

Introduction to Vibration AnalysisAnalysis

Fault DiagnosticsFault DiagnosticsGears, Bearings, Peakvue, Electrical, Belts,

Resonance

Gear DefectsGear Defects• There are many different types of gears and gear combinations

available for various speed and power requirements.• Regardless of gear type they all produce the same basic• Regardless of gear type they all produce the same basic

vibration patterns and characteristics when a defect is present

• The following topic will discuss the basic characteristics for the following types of gears:– Spur Gears– Helical Gears– Bevel Gears

Spur GearsSpur Gears• Spur Gears are most commonly thought of when diagnosing

gears. The teeth are cut parallel to the shaft. These gears are good at power transmission and speed changes but are noisier good at po e t a s ss o a d speed c a ges but a e o s ethan other gear types.

• Spur Gear Advantages– High efficiency

L h i– Low heat generation

• Spur Gear Disadvantages– Can be very noisyy y

Helical GearsHelical Gears• Helical Gears have teeth cut at an angle to the shaft. These

gears are much quieter than spur gears but due to the angular nature of the gear meshing, axial thrust and therefore axial vibration is higher than those of spur gears

Sometimes to counter act the axial thrust these gears can be double up– Sometimes to counter act the axial thrust these gears can be double up and are known as ‘Double Helical’ or ‘Wishbone Gears’

• Helical Gear AdvantagesQuiet Operation– Quiet Operation

• Helical Gear Disadvantages– Less power transmission efficiency

and greater heat generation thanand greater heat generation than spur gears

– Axial loading on bearings

Bevel GearsBevel Gears• Bevel Gears are used to transmit power and speed to an

output shaft perpendicular to the drive shaft. These gears use a bevel design to transmit the power better.– These gears are most commonly seen on right angle gearboxes (where

the input shaft is at 90 degrees to the output shaft)the input shaft is at 90 degrees to the output shaft)

• Bevel Gear AdvantagesBevel Gear Advantages– Converts the direction of power transmission

• Bevel Gear Disadvantages– Less efficient– Higher heat generation

Gear AnalysisGear Analysis• Vibration analysis of gears can provide a wealth of information about the

mechanical health of the gears. This section discusses the basic frequencies that may be present within a gearbox.

G M h F S t l D t• Gear Mesh Frequency Spectral Data• The gear mesh frequency (GMF) refers to the frequency at which to mating

gears interact with each other and is the most commonly discussed gear frequency.

• However, GMF by itself is not a defect frequency. The GMF should always be present in the spectral data regardless of gear condition. What is important is the amplitude as this may vary depending upon gear condition or loading of the gear.

Gears• Two mating gears will generate a frequency known as the

GMF and will show in the spectral data regardless of gear condition.co d t o

40 - Kiln Main DriveM4441 -G1V Shaft 01 Inboard Vertical

Route Spectrum* 1.2 p 08-Jun-02 23:11:51

OVERALL= 2.22 V-DG RMS = 2.14 LOAD = 100.0 RPM = 1548. (25.80 Hz) 0.9

mm

/Sec

0.3

0.6

RM

S Ve

loci

ty in

0 200 400 600 800 1000

0

Frequency in Hz

Freq: Ordr: Spec:

386.98 15.00 .864

Calculating GMF – Single ReductionCalculating GMF Single Reduction• Single Reduction Gear Train

– The GMF is simply defined as the number of teeth on a gear multiplied p y g pby its turning speed

GMF = (#teeth) x (Turning speed)E l• Example:– Consider the following gear train,

INPUT

OUTPUT

Input = 1490RPM

Gear 1 = 44 Teeth

Gear 2 71 Teeth

GMF = #teeth x turning speed

Gear 2 = 71 Teeth

GMF = 44teeth x 1490 RPM

GMF = 65560 CPM or 65560/60 = 1092.6 Hz

Calculating GMF – Multi ReductionCalculating GMF Multi Reduction• Calculating the GMF for gearboxes that have multiple trains use

the following. GMF = (#teeth) x (Turning speed)

Gear Ratio = (#teeth in) / (#teeth out)Speed out = (Speed in) x (Gear Ratio)Speed out = (Speed in) x (Gear Ratio)

• Example:– Consider the following gear train:

Input = 1490RPM

Gear 1 = 15 teethGear 2 21 teeth

INPUT

OUTPUT

Gear 2 = 21 teeth

Gear 3 = 19 teethGear 4 = 54 teeth

Calculating GMF – Multi ReductionCalculating GMF Multi ReductionInput = 1490RPM

Gear 1 = 15 teethINPUT

OUTPUT

Gear 2 = 21 teeth


Gear Ratio 1 = 15 teeth / 21 teeth = 0.714Speed Out = 1490 RPM x 0.714 = 1064.28 RPMp

Gear Ratio 2 = 19 teeth / 54 teeth = 0.351Speed Out = 1064.28 RPM x 0.351 = 374.47 RPM

GMF 1 = 1490 RPM x 15 teeth = 22350 CPMGMF 2 = 1064.28 RPM x 19 teeth = 20221.32 CPM

GMF Calculation ExerciseGMF Calculation Exercise• Using the formulas on P153 from the manual calculate:

– Speeds of all shafts– All GMF from the following gearbox arrangementAll GMF from the following gearbox arrangement

Input = 1000 RPM

Gear 1 = 10 teeth

INPUT

OUTPUT

Gear 2 = 40 teeth


• Gear Ratio 1 = 10/40 = 0.25• Shaft 2 speed = 1000 x 0.25 = 250 RPM• Gear Ratio 2 = 10/20 = 0 5Gear Ratio 2 10/20 0.5• Shaft 3 Speed = 250 x 0.5 = 125 RPM• GMF 1 = 1000 x 10 = 10000 CPM• GMF 2 = 250 x 10 = 2500 CPM

Gears – Sideband FrequenciesGears Sideband Frequencies• Sidebands are the most common indication that a gear is

defected.– Sidebands are equally spaced frequencies in the spectral data that

materialise either side of the main GMF peak.– The sideband frequency spacing is equal to either the turning speed ofThe sideband frequency spacing is equal to either the turning speed of

the input gear or the turning speed of the output gear.

• Sidebands show in the data when either the gear is worn, loose or eccentric. – The speed of the shaft with the bad gear on it will

produce the most dominant sidebands in the pspectral data.

GearsGears• The spectral data shows GMF with sideband data.

– The sidebands are equally spaced at intervals of 310 CPM. This is indicating the gear that rotates at 310 RPM is the one that is worn or

FPP - SAND MILLS (OLD)AX401A -G3A Shaft 02 Inboard Axial

Route Spectrum 07-Nov-02 09:11:53 (SST-Corrected)

1.0

g gdamaged.


0.6

0.8

mm

/Sec

GMF

0.4

RMS

Velo

city

in m

Sidebands

0.2

Sidebands

0 8000 16000 24000

0

Frequency in CPM

Freq: Ordr: Spec: Dfrq:

18363. 59.23 .564 310.82

Gears – Waveform DataGears Waveform Data• Gears can produce different types of waveforms, the one

shown below is indicating gear wear.– As the defective teeth come into mesh the noise generated increasesAs the defective teeth come into mesh the noise generated increases

showing an increase in amplitude in the vibration dataFPP - SAND MILLS (OLD)A

X401A -G3A Shaft 02 Inboard AxialRoute Waveform 07 Nov 02 09:11:53

1.5

07-Nov-02 09:11:53

PK = .4580 LOAD = 100.0 RPM = 311. (5.19 Hz)

PK(+) = 1.27 PK(-) = 1.13

0.6

0.9

1.2

CRESTF= 3.91

-0.3

0

0.3

Acce

lera

tion

in G

-s

-1.2

-0.9

-0.6

0 1 2 3 4 5 6

-1.5

Revolution Number

Case Study 5 –GearboxCase Study 5 Gearbox• The following case study is from a motor gearbox unit that

drives a roller. – Product (Fibre) is fed along the top of the roll while being washedProduct (Fibre) is fed along the top of the roll while being washed

through a series of baths. – There are several of these Wash Nip Rollers in a continuous stream,

failure of any one of them results in lost production

• Data is collected on aData is collected on a fortnightly basis as part of a routine data collection route

Case Study 5 –GearboxCase Study 5 Gearbox• The spectral data shown below is taken from the motor in the

axial direction – (As the motor is mounted directly into the gearbox the first helical gear(As the motor is mounted directly into the gearbox the first helical gear

is mounted on the end of the motor shaft).

Th GMF i hi hli ht d b

L1NG - WASH LINE NIP UNIT 33-32J03 -MIA MOTOR INBOARD AXIAL


0.6EEEEE EEEEE• The GMF is highlighted by

the primary cursor at 49 OrdersTh f lt f d t

01 Aug 04 10:21:41


0.4

0.5

mm

/Sec

>REN Wash Nip E=Gm(1>2)-S1

• The fault frequency data (dotted lines) indicate the sideband data showing gear wear on the first gear

0.2

0.3

RM

S Ve

loci

ty in

gear wear on the first gear in the gear train

0 10 20 30 40 50 60 70 80

0

0.1

Ordr: Freq:

49.0057551

Frequency in OrdersFreq: Spec: Dord:

57551. .275 .00649

Case Study 5 –GearboxCase Study 5 Gearbox• The waveform data is showing a distinct pattern commonly

associated with gears. • The amplitude increases In noise as the damaged teeth come• The amplitude increases In noise as the damaged teeth come

into mesh– Producing over 2G‐s of force in both the positive and negative direction

Case Study 5 –GearboxCase Study 5 Gearbox• The gears were inspected due to the critical nature of the

asset. It was found the gear to be severely damaged.• A new gearbox was fitted and new data was taken showing the• A new gearbox was fitted and new data was taken showing the

difference between the good and bad gear

Bearing Defectsg

Rolling ElementgPlain Bearings

Peakvue

Rolling Element BearingsRolling Element Bearings• Rolling element bearings have specific bearing failure modes

that can be observed in the spectral and waveform data.

• Bearing frequencies differ from most other frequencies present within the spectral data because unless the bearingpresent within the spectral data because unless the bearing has a defect there will be no frequency peaks in the data relating to the bearing. Only if the bearing has a defect will frequencies show in the spectral data.

There are four main fundamental bearing defect frequencies these are:q

Rolling Element Bearingsg g

Outer Race

Inner RaceInner Race

How Bearing Faults Generate Vibrationg

Rolling Element BearingsRolling Element Bearings• Bearing defect frequencies are calculated based upon the geometry of the

bearing these calculations may include:– Number of rolling elements– Pitch Circle Diameter– Rolling element diameter– Contact angle

• Defined within Machinery Health Manager there are over 100000 predefined bearing stored in the CSI bearing warehouse

BEARINGS in CSI Warehouse:

c:\RBMsuite\SysData\CSI_CMP.WH****************************************************

BRG ID Bearing Type #B/R FTF BSF BPFO BPFI12143 RHP 6218 11 0.418 2.967 4.598 6.40224421 SKF 6313E 8 0.376 1.894 3.009 4.99125372 SKF I 26313 19 0 433 3 568 8 219 10 78125372 SKF I‐26313 19 0.433 3.568 8.219 10.781

Rolling Element BearingsRolling Element Bearings• Characteristics of Bearing Defects

– High frequency raised noise level (Hump of energy)– High frequency raised noise level (Hump of energy)– Non‐Synchronous harmonic peaks (Both low and high frequency)

– Time waveform will show a lot of noise/impacting – Early stages of bearing wear may show better if viewed in

l ti i th f d iacceleration in the frequency domain– Fundamental bearing defect frequency (First calculable frequency) may not be present in the spectral dataq y) y p p

Failure Mode 1Failure Mode 1• The early stages of bearing defects produce low

amplitudes of vibration at higher frequencies – (Appears on the right hand side of the spectrum). ( pp g p )

• These are normally humps of energy or peaks that are harmonics to the fundamental frequency. – (The fundamental frequency should not be

i ibl hi )visible at this stage).

Failure Mode 2• Distinct harmonics of Non‐Synchronous peaks

appear. – (These should appear lower down the scale of the

spectrum – towards the left / middle of the plot)

• Sidebands may appear around these frequencies usually equating to turning speed. – (The fault frequencies may not match exactly with

the peaks in the spectrum due to the fact that the bearing geometry will have changed)bearing geometry will have changed).

Failure Mode 3• The fundamental frequency normally appears at this stage

– (First calculable frequency of the bearing – towards the left‐hand side of the spectral plot). This is classed as advanced stages of bearing wear.

• Sidebands may be visible that equate to other bearing )frequencies – BSF, FTF etc).

Failure Mode 4Failure Mode 4

• The bearing degrades so much that the spectrumThe bearing degrades so much that the spectrum becomes a mass of noise. At this point the bearing will fail at any point (If it last this long – most fail around Mode 3).

Rolling Element Bearings ‐ BPFIg g• Typical data showing a defected inner race

– Fundamental frequency showing– Harmonics low and high frequency + sidebands

Rolling Element Bearings ‐ BPFOg g• Data showing a defect related to the BPFO

– The fundamental frequency is showing– Harmonics from low to high frequencyHarmonics from low to high frequency

Rolling Element Bearings ‐ BSFg g• Bearing defect showing the BSF – Rolling elements

– Sidebands around the BSF = FTF

Rolling Element Bearings ‐ FTFRolling Element Bearings FTF• The FTF is the only bearing frequency that is sub‐synchronous

– May not detect then with conventional vibration data– FTF defect at 0.4 orders shown in Peakvue

• Bearing

FTF & BSFFTF & BSF

BPFI & BPFOBPFI & BPFO

Rolling Element Bearings ‐WaveformRolling Element Bearings Waveform• As a bearing becomes defected then the amount of

noise/force generated as the rolling elements impact the defective area increases. de ect e a ea c eases– This can show significant G‐levels in the time waveform. This value is

trended in the software as the Peak‐Peak value

• This data is taken from a pump with a damaged bearing– The force levels are

reaching 40G‐s

Case Study 6 – Bearing Defecty g• The spectral plot below is showing the data from the

inboard vertical direction of the motor. – The primary cursor is indicating the fundamental defect

BPFO f + h iBPFO frequency + harmonics. – The frequency range of the harmonics covers both low

and high frequency ranges suggesting the bearing is more advanced stages of failure.

Case Study 6 – Bearing DefectCase Study 6 Bearing Defect• The time waveform is showing significant impacting levels

reaching in excess of +/‐ 8G‐s of force. – This level of impacting is higher than would be suspected for a motor ofThis level of impacting is higher than would be suspected for a motor of

this type.

• The repetitive impactingThe repetitive impacting pattern shown above is common to antifriction bearing defects. g– In this instance the impacting

is representing the rolling elements striking a defect on the race.

Case Study 6 – Bearing DefectCase Study 6 Bearing Defect• The trend plot above is showing the increase in amplitude of

the Peak‐Peak parameter. – The peak‐peak parameter is measuring the amount of energy in theThe peak peak parameter is measuring the amount of energy in the

time waveform from the Peak+ to the Peak‐

C l i• Conclusion• The motor was reported as having a bearing

defect to the engineering group. As the f d t l d f t f tfundamental defect frequency was present and the trend had shown sudden increases it was recommended to change the bearing at the next available opportunitythe next available opportunity.

Bearing DefectsgRolling Element

Plain BearingsPeakvue

Plain BearingsPlain Bearings• Rotating elements are not used in sleeve (plain) bearings;

rather the shaft rides on a layer of lubricating oil inside the bearing journal. bea g jou a– Therefore the fundamental frequencies seen from antifriction bearings

do not apply to sleeve bearings.

• Since there is no contact between the bearing and the shaft monitoring of sleeve bearings for vibration analysis usually requires the use of displacement probes mounted 45 p pdegrees either side of top dead centre.

Plain BearingsPlain Bearings• As there are no rotating components in the bearing that

produce high frequency noise (force) there is no need to monitor a high frequency range. Usually 10 to 15 orders of turning speed will be sufficient.

• Sleeve bearings have specific defects that contribute towards bearing failure, these are:– Excessive clearanceExcessive clearance– Hydraulic instability (oil whirl)

Plain Bearings – Spectral Diagnostics• Excessive Clearance

– When there is excessive clearance between the rotor and the bearing then this will have an effect on the system vibration. When the bearings have excessive clearance then a ‘looseness’ occursbearings have excessive clearance then a looseness occurs.

• The spectral data shown below is indicating a sleeve bearing with excessive clearanceexcessive clearance.

Fu - Turbine Brg Thrust EndTBT -R1Y Radial 'Y' Direction

Route Spectrum* 27-Jul-04 14:08:21

OVERALL= 2.93 V-DG P-P = 22.71 LOAD= 100 0

16 As the clearance increases then the harmonics of 1xTs will increase and can go up to 10–15xTs.

Like looseness the more harmonics LOAD = 100.0 RPM = 941. (15.69 Hz)

8

12

cem

ent i

n M

icro

ns

– Like looseness the more harmonics there are the more severe the problem will be.

– A good sleeve bearing will still show a few harmonics as there is a small

l b t th h ft d

4

P-P

Dis

plac clearance between the shaft and

bearing

0 3 6 9 12

0

Frequency in Orders

Ordr: Freq: Spec:

1.000 15.68 7.494

Plain Bearings – Spectral DiagnosticsPlain Bearings Spectral Diagnostics• Oil Whirl

– One of the major problems encountered with these types of bearings is j p yp gthe possibility of hydraulic instability of the shaft within the bearing; known as oil whirl or oil whip.

– Oil Whirl is a result of turbulent flow within the oil resulting in the oil

Fu - Turbine Brg Thrust EndTBT -R1Y Radial 'Y' Direction

Route Spectrum* 27-Jul-04 14:08:21

16

pushing the shaft around of centre.• The dominant peak within the spectral

data will be typically at 0.4 orders. (.40‐.48)

Oil Whi l t 0 4 d OVERALL= 2.93 V-DG P-P = 22.71 LOAD = 100.0 RPM = 941. (15.69 Hz)

8

12

ent i

n M

icro

ns

– This defect is sub‐synchronous data. – When the amplitude of the oil whirl is

equal to or greater than the 1xTs peak a problem exists

I thi i t il hi l b

Oil Whirl at 0.4 orders

4

8

P-P

Dis

plac

em • In this instance oil whirl can be corrected by:

– Properly loading the bearing– Change the oil viscosity

Ch th il

0 3 6 9 12

0

Frequency in Orders

Ordr: Freq: Spec:

1.000 15.68 7.494

– Change the oil pressure

Oil WhirlOil Whirl

Bearing DefectsRolling ElementPlain Bearings

Peakvue

Peakvue ProcessingPeakvue Processing• The detection of bearing and gear defects is one of the primary

expectations of a predictive maintenance program. – As analysts we can spend a lot of time tying to determine these faults. – Peakvue is a process that concentrates on these defects to help the analysts

determine potential faults developing

• Peakvue stands for the Peak Value and is a technique that detects high frequency stress waves generated from metal to metal contact such as:frequency stress waves generated from metal to metal contact, such as:– Bearing defects – Rotating elements striking a defect on the race– Gear defects – Damaged teeth in mesh– It is the detection of these high frequency stress waves that will aid with

analysisanalysis

Peakvue Processing ‐ FiltersPeakvue Processing Filters• In order to capture the stress wave signal the process requires

the use of a filter to remove all unwanted noise that can dominate the datado ate t e data

1. Conventional Vibration Signals that are filtered from the Peakvue Signal

ImbalanceMisalignment

2. Peakvue filter removing low frequency noise from the stress wave data

This is to prevent low frequency noiseMisalignment

GearsBearingsResonance

frequency noise consuming the stress wave activity

3. High frequency stress wave activity occurring in the 1000Hz -20000Hz frequency range at a rate governed by a low frequency event

BearingsGears

Peakvue Processing ‐ FiltersPeakvue Processing Filters• There are two types of filters available• Band Pass FiltersBand Pass Filters

– The band pass filter removes all the data above and below the filter corner values

f

• High Pass Filter– The high pass filter removes all data lower in frequency to that of the

f

g p q yfilter selection allowing only the high frequency stress waves to pass through

• After the filtering process what should remain is the highAfter the filtering process what should remain is the high frequency stress wave activity that is occurring at the rate of the excitation – such as from a bearing.

Peakvue Processing – Spectral DataPeakvue Processing Spectral Data• Shown below is a typical Peakvue spectrum with a defect

present• The filter used is shown in the top Stress waves are showing • The filter used is shown in the top

right hand corner Stress waves are showing

clearly in the data at 4.6 Orders

G d S t ill Good Spectrum will show only a noise level

Noise removed by yfilter

Peakvue Processing – Waveform DataPeakvue Processing Waveform Data• As stress waves are small in amplitude severity of the problem

can be judged using the time waveform– Peak Value of force from the impactPeak Value of force from the impact

• The waveform can resemble a spectrum as there is no negative half to the data

B42 - ZONE 5 DF FAN 116/16EXT01-M2P Motor Inboard Horz Peakvue

Route Spectrum 09-Jul-03 09:50:49 (PkVue-HP 1000 Hz) OVERALL= 1.37 A-DG RMS = 1.37 LOAD = 100.0 RPM = 1342 (22 37 Hz)0 3

0.40.50.60.70.8

cele

ratio

n in

G-s

N N N N N N N N N

For Peakvue analysis

R t W f

RPM = 1342. (22.37 Hz)

0 200 400 600 800 1000

0 0.10.20.3

Frequency in Hz

RM

S A

cc

>NTN 6217 N=BPFO -OB Use the Spectrum

– Diagnose the defect Use the Waveform

Route Waveform 09-Jul-03 09:50:49 (PkVue-HP 1000 Hz) RMS = 2.97 PK(+) = 8.35 CRESTF= 2.81

2345678

Acc

eler

atio

n in

G-s – Determine the severity

0 4 8 12 16 20 24 28 32 36

01

Revolution NumberLabel: Bearing Fault - BPFO NTN6217

Freq: Ordr: Spec:

1.250 .05587 .01367

Case Study 7 – Peakvue on Fan Bearingy g

• The following machine is a pre‐heater fan designed topre heater fan designed to heat the product prior to it entering a Kiln– There is no standby for this

machinemachine– Failure results in stopped

production

• The following data was taken from the above fan unit.– The problem bearing resided on the fan inboard bearing.– Data was collected on a monthly basis. Both conventional vibration

data and Peakvue data were taken during the route collection.

Case Study 7 – Peakvue on Fan Bearingy g• The data shown below is taken using conventional vibration

methods on the inboard bearing of the fan– 1x peak is highlighted showing amplitudes of 4mm/sec

40 - Preheater FanM4425 -F1H Fan Inboard Horizontal

Route Spectrum 5

ec

1x peak is highlighted showing amplitudes of 4mm/sec– Waveform is showing less than 1G of force both +/‐

29-Oct-02 11:19:26 OVERALL= 4.18 V-DG RMS = 4.18 LOAD = 100.0 RPM = 825. (13.75 Hz)

1

2

3

4

S Ve

loci

ty in

mm

/Se

• There are indications of

Route Waveform 1 01.5

0 10 20 30 40 50 60 70 80

0

1

Frequency in Orders

RM

S There are indications of bearing frequencies showing high frequency– These may be missed due to

29-Oct-02 11:19:26 RMS = .3837 PK(+/-) = 1.19/1.05 CRESTF= 3.11

1 0

-0.5

0

0.5

1.0

ccel

erat

ion

in G

-s

ythe amplitude of the 1x peak

0 1 2 3 4 5

-1.5

-1.0

Revolution Number

Ac

Ordr: Freq: Spec:

1.000 13.75 3.721

Case Study 7 – Peakvue on Fan Bearingy g• The Peakvue data above is taken from the same point as the

previous data. – This particular reading is using a 1000 Hz High Pass filter.

40 - Preheater FanM4425 -F1P Fan Inboard Horz Peakvue

Route Spectrum 29-Oct-02 11:15:590.6

0.7

G-s F F F F

This particular reading is using a 1000 Hz High Pass filter.

H th d t i h i th 29 Oct 02 11:15:59 (PkVue-HP 1000 Hz) OVERALL= 1.10 A-DG RMS = 1.10 LOAD = 100.0 RPM = 830. (13.84 Hz)

0 1

0.2

0.3

0.4

0.5

MS

Acc

eler

atio

n in

>SKF 22240CC F=BPFO -IO

• Here the data is showing there is stress wave activity at 8.176 orders. – This is not non‐synchronous

d d h f

Route Waveform 29-Oct-02 11:15:59

78

0 5 10 15 20 25 30 35 40

0

0.1

Frequency in Orders

RM data and the frequency

matches that of the BPFO for the bearing.

• The waveform data is i 7 G f f 29-Oct-02 11:15:59

(PkVue-HP 1000 Hz) RMS = 3.31 PK(+) = 7.47 CRESTF= 2.25 DCoff = -3.08

123456

Acc

eler

atio

n in

G-s measuring over 7 G‐s of force

as oppose to the 1G from the previous data.

0 10 20 30 40 50

01

Revolution Number

A

Ordr: Freq: Spec:

8.176 113.14 .194

Case Study 7 – Peakvue on Fan Bearing• Conclusion• There is significant bearing damage relating the outer race ofThere is significant bearing damage relating the outer race of

the bearing. • As the machine was critical to the process, the bearing was

changed on the next available opportunity that tied in with process requirements.

Electrical DefectsElectrical Defects

Electrical DefectsElectrical Defects• A motor can be simply broken down into two key components

– Rotor – Stator • The stator is stationaryStator The stator is stationary

– Consists of wire wound in coils and placed in slots of an iron core.

– The stator produces a rotating magnetic field.

The rotor is not stationary– Consists laminations with solid conductors called rotor barsConsists laminations with solid conductors called rotor bars– A circular flow of current through these rotor bars causes

the rotor to become an electromagnet which will rotate in a magnetic filed.

Electrical Defects – Spectral DataElectrical Defects Spectral Data• The most common electrical frequency that materialises in the

spectral data is the 2 x Line Frequency.– For most industrial applications the line frequency used to supply

Ex7 - Example 7

For most industrial applications the line frequency used to supply motors is 50Hz (Europe).

– Therefore the frequency of concern for most electrical faults would be 100Hz (2xLf [Lf=line frequency])

Ex7 Example 7Ex7 -M1H Motor Outboard Horizontal

Route Spectrum 08-Nov-00 14:27:35

OVERALL= .5613 V-DG RMS = .5607 LOAD = 100.0 RPM= 2967 (49 44 Hz)

0.5

0.6

• The spectral plot is showing a peak at 100Hz RPM = 2967. (49.44 Hz)

0.3

0.4

Velo

city

in m

m/S

ec

showing a peak at 100Hz (6000cpm)– 2xLf

0.1

0.2RM

S V – This can be mistaken for

misalignment

0 500 1000 1500 2000

0

Frequency in Hz

Freq: Ordr: Spec:

100.00 2.023 .386

Electrical Defects – Waveform DataElectrical Defects Waveform Data• The waveform data from a 100Hz peak will show a sinusoidal

pattern like the waveform shown below

Ex7 - Example 7Ex7 -M1H Motor Outboard Horizontal

Route Waveform 08-Nov-00 14:27:35

1 0

1.5 • Again this type of pattern can be associated with RMS = .5291

LOAD = 100.0 RPM = 2967. (49.44 Hz)

PK(+) = 1.50 PK(-) = 1.77 CRESTF= 3.31

0

0.5

1.0

/Sec

can be associated with misalignment. – Usually misalignment would

-1.0

-0.5

0

Velo

city

in m

m produce higher force (Higher waveform levels) than those from electrical defects due to the stress being applied to

-2.0

-1.5

the stress being applied to the machine

0 1 2 3 4 5 6Revolution Number

Electrical Defects ‐ CausesElectrical Defects Causes• Common fault types that can produce the 2xLf peak are as

follows:• Dynamic Eccentricity – Usually Rotor Related• Static Eccentricity – Usually Stator Related• Loose Iron or Slot Defect – Rotor or Stator• Open or Shorted Windings

I l i B kd I b l d Ph• Insulation Breakdown or Imbalanced Phase• Loose Connectors

Electrical Defects ‐ Peakvue• Peakvue data also shows electrical defects at the 2xLf peak.

– This may be due to the rotor or stator bowing; due to heat build up.y g p

• The spectral plot below is indicating a 100Hz peak using Peakvue with a 1000Hz filter.

Case Study – Electrical DefectCase Study Electrical Defect• The following case study was taken from a glass manufacturer.

The data was from the ‘Electric Front Wall Cooling Fan’. – This fan unit is a critical fan to the process and has no standby unit.This fan unit is a critical fan to the process and has no standby unit. – In this particular instance the motor failed shortly after the data was

collected.

• The Peakvue data taken on the motor non‐drive end is showing a dominant 100Hzshowing a dominant 100Hz peak. – This frequency is at 2xLf and

is associated with electrical problems

Case Study – Electrical DefectCase Study Electrical Defect• The multi‐plot above shows the same measurement point

going back over the last 5 route readings. – This particular plot is useful for determining rate of change.This particular plot is useful for determining rate of change.– It is quite clear how this particular frequency suddenly appeared

• Conclusion– As the motor failed shortly after– As the motor failed shortly after

data collection no action was taken to prevent failure.

– The investigation in the motor showed one of the connectors had come loose causing the motor to burn out.

Belt DefectsBelt Defects

V‐BeltsV BeltsTiming Belts

Belt DefectsBelt Defects• Belts are the most common low cost way to transmit power

from one shaft to another. – Belt drives rely on friction between the belt and pulley to transmit

power between drive and driven shafts

• The ability of belt to transmit power depends upon• The ability of belt to transmit power depends upon1. Belt Tension (tension on the belt holds it tightly against the sheave)2. Friction between the belt and sheave3. The arc of contact between the belt and sheave (Wrap)4. The speed of the belt

• However, belts can be easily damaged by heat, oil and grease and since belts slip with in the sheaves they can not b d h t d h i d ( t fbe used where exact speed changes are required (except for timing belts)

Belt DefectsBelt Defects• Belt defects can be considered non‐critical faults by many

maintenance groups due to the relative ease of replacement requiring minimum downtime. equ g u do t e– But belt defects are a major contributor to the overall vibration of the

machine resulting in premature failure of other machine components.

Sources of belt drive defects

Poor MaintenanceEnviromental FactorsPoor InstallationPoor DesigngOther Defects

Belt Defects – Belt TypesBelt Defects Belt Types• There are many different types of belt drive systems. This

section covers the most commonly used types of belt in industry today.dust y today

• V‐Belts• V‐belts are the most common type of belts used. They are ‘V’ shaped in

cross‐section this allowing the belt to wedge against the side of thecross section, this allowing the belt to wedge against the side of the sheave. – This design allows the belt to be run faster than most other type of belt

applications with power transmission efficiencies as high as 95%

Belt Defects• Timing Belts• These are flat belts with equally spaced teeth that meshThese are flat belts with equally spaced teeth that mesh

with notches on the pulley. Timing belts are different from other belt drives as they do not induce any slip.

M l d h l i d i i i– Most commonly used where constant velocity and strict timing application is required.

Belt Defects – Fault CharacteristicsBelt Defects Fault Characteristics• Belt defects, such as cracks, broken or missing pieces, hard and

soft spots can generate vibration at the turning speed of the belt (1xbelt) and harmonics – Due to the length of the belt in relation to the pulleys (sheaves) the

1xbelt frequency is sub‐synchronous and very often the 2xbelt1xbelt frequency is sub synchronous and very often the 2xbelt frequency may be sub‐synchronous as well

• The predominant harmonic is typically the 2xBelt frequency and can be seen in the radial plain in‐line with the belts.– Severity is judged by the number and amplitude of the harmonics

seen in the spectral data

Belt Defects – Fault CharacteristicsBelt Defects Fault Characteristics• Just like two mating shafts, belt drive systems can also be

misaligned in both angular and offset directions. – When misalignment is induced into a belt drive system then the life ofWhen misalignment is induced into a belt drive system then the life of

the belt is significantly reduced as well as the overall vibration of the system increases.

Offset Misalignment

Angular Misalignment

• Pulley misalignment results in high axial vibration at the shaft turning speed. – If the belt is also defected then 1xbelt frequency and harmonics may– If the belt is also defected then 1xbelt frequency and harmonics may

also show in the axial direction

Belt Defects – CalculationsBelt Defects Calculations • The fundamental belt frequency can be calculated using the

following equation:Belt Freq. = (3.142 * Pulley Ts * Pulley PCD)

Belt (Length)– Where:

• Ts = Turning Speed• PCD = Pitch Circle Diameter• Note: The PCD and belt length must be in the same units

• A timing will belt will also have a specific frequency related to the number of teeth on the pulley

Timing Belt Freq. = (Pulley Ts) * (# Pulley Teeth)

Belt Defects – Calculation ExampleBelt Defects Calculation Example• Belt Frequency Calculation• Belt Frequency = (3.142 * 1480 * 300) / (2000)Belt Frequency (3.142 1480 300) / (2000)• Belt Frequency = (1395048) / (2000)• Belt Frequency = 697.524 CPM

– This is sub‐synchronous to the 1xTs of the pulley

Motor RPM 1480 RPMMotor RPM = 1480 RPMPulley Diameter = 300 mmBelt Length = 2000mm

Belt Defects – Spectral DataBelt Defects Spectral Data• The spectral data above is data taken of a motor from an Air

Handling Unit. – The frequency highlighted by the primary cursor is showing the 1xTs ofThe frequency highlighted by the primary cursor is showing the 1xTs of

the motor (1 Order)

• There are a lot of sub‐synchronous peaks showing in this datathis data. – The first peak is the

fundamental frequency of the belt rotation.

– The second peak is the 2xbelt

1 x Belt Frequency showing with harmonics

The second peak is the 2xbelt frequency suggesting there is damage to the belt

– As the harmonics of the belt increase in number they surpass the 1xTs of the motor

Dominant 2 x Belt Frequency

surpass the 1xTs of the motor and in this case the third harmonic becomes non‐synchronous data.

Case Study 9 – Belt DefectCase Study 9 Belt Defect• The following data was taken on an Air Handling Unit. The Air

Handling Unit is a supply fan from shared services. This is a stand alone unit with no stand by capabilitysta d a o e u t t o sta d by capab ty

• The data shows the t t i d

BL31 - 559 AHU Supply559S -M2H Motor Inboard Horizontal

Route Spectrum* 22-Feb-05 13:53:33

0.5J J J J J J J J J J

motor turning speed along with a sub‐synchronous peak of the belt frequency.

• The primary cursor is

OVERALL= 1.22 V-DG RMS = .7701 LOAD = 100.0 RPM = 1272. (21.21 Hz)

0.3

0.4

mm

/Sec

>Belt Freqs J=Belt 1 Freq

• The primary cursor is highlighting the 1xbelt with several harmonics.

• The 2xbelt is very

0.2

RM

S Ve

loci

ty in

- M

otor

spe

ed

Fan

spe

ed

The 2xbelt is very dominant suggesting there is damage to the belts.

0 4000 8000 12000 16000

0

0.1

Freq: 835.69

X x -

Label: Belt defect/worn belts & sheaves

0 4000 8000 12000 16000Frequency in CPM

qOrdr: Spec:

.657 .04393

Case Study 9 – Belt DefectCase Study 9 Belt Defect• As this is a critical machine it was recommended on the next

available opportunity that the belts needed to be checked for damage and re‐aligned. da age a d e a g ed

• The machine was stopped and the belts were inspected based upon the recommendation.

• Significant damage was found to several of the belts during this inspection as well as worn pulleys. Both the belts and pulleys were replaced and correctly aligned before re‐starting the machine.

ResonanceResonance

ResonanceResonance• Resonance is defined as:

An excitation of a natural frequency by a periodic forcingAn excitation of a natural frequency by a periodic forcing function.

• All assets contain natural frequencies that vary depending upon the stiffness and mass. – Resonance can be considered to be a vibration amplifier that takes theResonance can be considered to be a vibration amplifier, that takes the

force level of the periodic forcing function and amplifies it; which significantly increases the movement of the asset.

If Vibration is a Fire The Resonance is a FuelIf Vibration is a Fire, The Resonance is a Fuel

Example of ResonanceExample of Resonance• The example shown represents the effect on amplitude of the forcing

function when in resonance.– In plot 1 the 1xts is running below the natural frequency (Fn).– Fn can be seen in plot 2. – Plot 3 shows the increase in amplitude of the forcing function when run at the

t l f thi inatural frequency – this is resonance

Before Excitation

1

FrequencyResonance Curve

2

FrequencyAmplified Signal

3

Frequency

3

ResonanceResonance• There are two factors that determine the natural frequency of

an asset these are;1. Mass – The heavier an object the lower the natural frequency2. Stiffness – The more rigid a structure the higher the natural frequency

• Resonance is becoming more of a problem in industry in recent years due to:ece t yea s due to– Older equipment having to run faster to meet current production

demands (often above what it was designed for)– Equipment is being built cheaper and lighter

• This is resulting in amplification of the forcing function creating excessive machine movement resulting premature machine failure.

Effects of Resonance• The ODS data is showing a steel frame structure deflecting at

one corner in the vertical direction due to a resonant condition.co d t o

Characteristics of ResonanceCharacteristics of Resonance• Characteristics of Resonance

– Resonance is very directional in nature (Movement may be greater in y ( y gone plain than the other)

– Vastly different amplitudes of the forcing function from one direction to the other (between Horizontal and Vertical – Rule of thumb ratio is 3:1 difference)

– Resonance is very speed sensitive (small changes in speed can show large differences in amplitude of the forcing function)

– Resonance can occur at any frequency but most commonly associated with the 1xTs

Resolving a ResonanceResolving a Resonance• There are a number of alterations to the system that can be

made to resolve a resonance condition. – However if structural changes are to be made you need to be careful

you don’t excite another natural frequency once the change has been made?

• Once you are sure you have a resonant condition it can be corrected by one of the following methods:

Ch th M– Change the Mass– Change the Stiffness– Remove the forcing function– Dampen the structure Dampening is a method used to convert mechanical energy into

thermal energy. It does not remove the resonant condition only gy ycontrols the amount of movement.

Resonance – Spectral DataResonance Spectral Data• The spectrum is showing the 1xTs peak of the motor with amplitudes

reaching 19mm/sec. – This is high for the 1xTs.

• Very often this type of data can be mistaken for Imbalance as this defect can also produce a high 1xTs peak. – However Imbalance is a centrifugal force and should show similar amplitudes in

both radial plains where as resonance is very directional.

• In order to help resolve this issue 40 - No 1 GCT Compressor

M4551 -M2H Motor Inboard HorizontalRoute Spectrum 13-Feb-03 10:14:46

24

27

we need to check the amplitude of the 1xTs 90 degrees to this point (horizontal to vertical)


15

18

21

y in

mm

/Sec

– This can easily be done by using the ‘multi point plot’ in the software6

9

12

RM

S Ve

loci

t

0 500 1000 1500 2000

0

3

Frequency in Hz

Freq: Ordr: Spec:

24.72 1.000 19.50

Resonance – Multi PlotResonance Multi Plot• The multi point plot allows the analyst to display several measurement

points on the same plot. Here we are showing all the radial points from the motormotor.– It is very clear that the amplitudes of the 1xTs peak are excessive in the

horizontal direction when compared to the vertical. This is a characteristic of a resonant condition. 40 No 1 GCTCompressor40 - No 1 GCT Compressor

M4551 - Multiple Points (13-Feb-03)

8

12

16

2024

Max Amp 22.0

eloc

ity in

mm

/Sec

0

4

8

M2V 10:15

RM

S Ve

M1V 10:14

M2H 10:14

13-Feb-03Point= M2H

Frequency in Hz0 500 1000 1500 2000

M1H 10:14RPM= 1484. 10:14:46 13-Feb-03

Freq: Ordr: Sp 3:

25.00 1.011 19.35

Case Study 10 – ResonanceCase Study 10 Resonance• The following case study is taken from a motor and a

reciprocating compressor. The unit is mounted on a steel frame which, in turn sits on spring mounts designed for dampening c , tu s ts o sp g ou ts des g ed o da pe g

• Recently the motor had been replaced due to bearing defect; however the new motor was smaller and lighter butdefect; however the new motor was smaller and lighter but delivered the same power as the previous motor.

• When the compressor was put back into service it was noted there was excessive vibration coming from the unit. The unit was left to run like this for several months until the vibration became to excessivevibration became to excessive.

Case Study 10 – ResonanceCase Study 10 Resonance• Data was taken across the unit using route based data

collection.SL - Compressor

CP1 -M1H Motor OutboardHorizontalCP1 -M1H Motor Outboard HorizontalRoute Spectrum 02-Feb-04 15:09:54

OVERALL= 45.58 V-DG PK = 45.32 LOAD = 100.0 RPM = 1490. (24.83 Hz)

40

50

60

20

30

40

PK V

eloc

ity in

mm

/Sec

0

10

20P

• The plot above is taken from the motor showing a 1xTs peak in excess of 40mm/sec

0 300 600 900 1200 1500 1800

0

Frequency in Hz

Freq: Ordr: Spec:

24.83 1.000 45.19

in excess of 40mm/sec.

Case Study 10 – ResonanceCase Study 10 Resonance• This data is very high in amplitude. • The data was then displayed in a multi plot format to showThe data was then displayed in a multi plot format to show

how the amplitude was across the radial plains.• Due to the vastly different amplitudes at the 1xTs frequency

the defect on this motor was Resonance.SL - Compressor

CP1 - Multiple Points (02-Feb-04)

40

50Max Amp 44.1

city

in m

m/S

ec

10

20

30Amplitude differences between radial plains

PK V

eloc

0

10

M2H 15:26

Frequency in CPM0 4000 8000 12000 16000

M2V 15:26

Case Study 10 – ResonanceCase Study 10 Resonance• Recommendation• It was determined that the change in motor size may be theIt was determined that the change in motor size may be the

cause of the resonance as the mass had been altered. A visual inspection of the frame work also revealed that one of the support beams had cracked along the weld this altering thesupport beams had cracked along the weld – this altering the stiffness of the structure. The support was welded and strengthened and more data was acquired to determine if any effect on the resonance had occurred.

Case Study 10 – ResonanceCase Study 10 Resonance• The spectra, shows the ‘Before’ and ‘After’ plot of the motor

inboard horizontal. It shows a significant drop in amplitude of the 1xTs peak. – By stiffening the structure the natural frequency had increased moving

it away from the 1xTs peak thus resulting in a significant drop init away from the 1xTs peak thus resulting in a significant drop in amplitude. SL - Compressor

CP1 -M2H Motor Inboard Horizontal

40

50Max Amp 44.1

city

in m

m/S

ec

10

20

30

PK V

eloc

0

10:08:0507-May-04

Frequency in Hz0 1000 2000 3000 4000

15:26:3802-Feb-04

Summary of FaultsSummary of Faults

Early Bearing Wear

Gearmesh Frequency

Advanced Bearing Wear

G

Looseness

Electrical

Misalignm

e

Electrical

Imbalance

Resonance

Electrical Slot Pass FrequencyLower Gearmesh Frequencies

Severe Looseness

Severe Misalignment

ent

Belt

Frequency

c c cc c cc

F re q u e n c yIn Te rm sO f R P M M o s t L ik e ly C a u s e s O t h e r P o s s ib le C a u s e s & R e m a rk sO f R P M M o s t L ik e ly C a u s e s O t h e r P o s s ib le C a u s e s & R e m a rk s

1 x R P M U n b a la n c e 1 ) E c c e n t r ic jo u rn a ls , g e a rs o r p u l le y s2 ) M is a l ig n m e n t o r b e n t s h a ft - If h ig h a x ia l vib ra t io n3 ) B a d B e lt s - If R P M o f b e lt4 ) R e s o n a n c e5 ) R e c ip ro c a t in g fo rc e s) p g6 ) E le c t r ic a l p ro b le m s7 ) L o o s e n e s s8 ) D is t o rt io n - s o ft fe e t o r p ip in g s t ra in

2 x R P M M e c h a n ic a l 1 ) M is a l ig n m e n t - i f h ig h a x ia l vib ra t io nL o o s e n e s s 2 ) R e c ip ro c a t in g fo rc e s

3 ) R e s o n a n c e4 ) B a d b e l t s - i f 2 x R P M o f b e l t

3 x R P M M is a l ig n m e n t U s u a l ly a c o m b in a t io n o f m is a l ig n m e n t a n d e x c e s s ive a x ia l c le a ra n c e s ( lo o s e n e s s ).

L e s s t h a n O i l W h ir l ( le s s 1 ) B a d d r ive b e l t s1 x R P M t h a n 1/ 2 R P M 2 ) B a c k g ro u n d vib ra t io n

3 ) S u b -h a rm o n ic re s o n a n c e4 ) " B e a t " V ib ra t io n

S y n c h ro n o u s E le c t r ic a l C o m m o n e le c t r ic a l p ro b le m s in c lu d e b ro k e n ro t o r b a rs , e c c e n t r ic(A . C . L in e P ro b le m s ro t o r u n b a la n c e d p h a s e s in p o ly -p h a s e s y s t e m s , u n e q u a lF re q u e n c y ) a ir g a p .2 x S y n c h . To rq u e P u ls e s R a re a s a p ro b le m u n le s s re s o n a n c e is e x c i t e dF re q u e n c yM T i R P M B d G G t t h t i R P M f b dM a n y T im e s R P M B a d G e a rs G e a r t e e t h t im e s R P M o f b a d g e a r(H a rm o n ic a l ly A e ro d y n a m ic F o rc e s N u m b e r o f fa n b la d e s t im e s R P MR e la t e d F re q . ) H y d ra u l ic F o rc e s N u m b e r o f im p e l le r va n e s t im e s R P M

M e c h a n ic a l L o o s e n e s s M a y o c c u r a t 2 , 3 , 4 a n d s o m e t im e s h ig h e r h a rm o n ic s i fs e ve re lo o s e n e s s

R e c ip ro c a t in g F o rc e sH ig h F re q u e n c y B a d A n t i F r ic t io n 1 ) B e a rin g vib ra t io n m a y b e u n s t e a d y a m p l i t u d e a n d fre q u e n c yH ig h F re q u e n c y B a d A n t i -F r ic t io n 1 ) B e a rin g vib ra t io n m a y b e u n s t e a d y - a m p l i t u d e a n d fre q u e n c y(N o t H a rm o n ic a l ly B e a rin g s 2 ) C a vi t a t io n , re c irc u la t io n a n d flo w t u rb u le n c e c a u s e ra n d o m ,R e la t e d ) h ig h fre q u e n c y vib ra t io n

3 ) Im p ro p e r lu b r ic a t io n o f jo u rn a l b e a rin g s (F r ic t io n e x c i t e d vib ra t io n )4 ) R u b b in g

Useful References• Simplified Handbook of Vibration Analysis Volume 1 – Arthur

R. Crawford • Simplified Handbook of Vibration Analysis Volume 2 – Arthur

R. Crawford • BS ISO 13373‐1 2002 – Condition Monitoring and Diagnostics

of Machines – General Procedures• BS ISO 13373‐2 – Condition Monitoring and Diagnostics ofBS ISO 13373 2 Condition Monitoring and Diagnostics of

Machines – Processing, Presentation and Analysis of Vibration Data

vibration analysis level- 1 updated [compatibility mode]

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