external use tm layered intelligence for machine monitoring sensors expo 2014 michael stanley mark...

External Use

TM

Layered Intelligence for Machine MonitoringSensors Expo 2014

Michael Stanley

Mark Pedley

Maureen Helm

Freescale Semiconductor

TM 2Freescale, the Freescale logo, AltiVec, C-5, CodeTEST, CodeWarrior, ColdFire, ColdFire+, C-Ware, the Energy Efficient Solutions logo, Kinetis, mobileGT, PEG, PowerQUICC, Processor Expert, QorIQ, Qorivva, StarCore, Symphony and VortiQa are trademarks of Freescale Semiconductor, Inc., Reg. U.S. Pat. & Tm. Off. Airfast, BeeKit, BeeStack, CoreNet, Flexis, Layerscape, MagniV, MXC, Platform in a Package, QorIQ Qonverge, QUICC Engine, Ready Play, SafeAssure, the SafeAssure logo, SMARTMOS, Tower, TurboLink, Vybrid and Xtrinsic are trademarks of Freescale Semiconductor, Inc. All other product or service names are the property of their respective owners. © 2013 Freescale Semiconductor, Inc.

Quick Introduction• Employed at Motorola / Freescale Semiconductor from

June 1980 to the present, where I’ve had multiple careers. Most recently:

− SoC Integration / MCU Architecture

− Sensors Architecture / Algorithms / Product Definition

− basically, solving systems level problems

• I blog on sensor related topics at

http://www.freescale.com/blogs/mikestanley and http://memsblog.wordpress.com/

• [email protected]

• Download the Xtrinsic Sensor Fusion Library for Kinetis MCUs from http://www.freescale.com/sensorfusionMike Stanley

http://www.freescale.com/blogs/mikestanley

http://memsblog.wordpress.com/

mailto:[email protected]

http://www.freescale.com/sensorfusion


Freescale SemiconductorSensors Solutions Division - Market Focus

Safe & Efficient AutomobilesSafe driving, front/side impact

Tire information system

Active driver assistance

Passenger occupancy detection

Industrial Sensor NetworksConnected intelligence

Fault monitoring/prognostication

High precision modules

Extreme Portable ElectronicsLow profile packages

Low power consumption

Submersible sensors

Automotive

Consumer

• Standalone sensing systems• Ruggedized packages• Wide sensing ranges

• Accelerometer• Gyroscope• Pressure• Magnetometer• MCU integrated sensors


Courtesy of Volvo Construction Equipment (mages.volvoce.com)

Credit: IBM Research (http://www-03.ibm.com/press/uk/en/photo/43250.wss)

A common thread for all these scenarios is that downtime is very, very expensive.

Predictive maintenance is a must when you cannot afford downtime.


Topics

• Condition-Based Maintenance (CBM)• Condition-Based Monitoring• Prognostics and Health Management (PHM) systems• Machine Monitoring• Predictive Maintenance

In this talk, we will focus on vibration monitoring for CBM

Lowered costs in sensors and communications imply that CBM techniques may be poised to extend into new areas – IF we can simplify the software side of things.


• General Electric• Techenomics International• Vibrotech reliability services• Condition Monitoring Services

Inc.• Vikon• STI Vibration Monitoring• ALS Limited• Fluke• GeoSonics / Vibra-Tech• Timken• Vipac Engineers and Scientists

Ltd.• Wagner Equipment Co.• KIM Gruppen• Allied Reliability Group• Tezzco Inc.• Sterling SIHI BmbH

• Bentley Nevada Services• Monition Limited• ROZH• GasTOPS Ltd.• Critical Software• Azima DLI• Balmac Inc.• Diagnostic Solutions• Siemens• Prosig• Condition Analyzing Corporation• Metso Corporation• Dresser-Rand• dB Prűftechnik• KCF technologies• Schenck USA• Machine Monitoring Systems

There are lots of players in this field. Many are service companies.

7TM

Freescale, the Freescale logo, AltiVec, C-5, CodeTEST, CodeWarrior, ColdFire, ColdFire+, C-Ware, the Energy Efficient Solutions logo, Kinetis, mobileGT, PEG, PowerQUICC, Processor Expert, QorIQ, Qorivva, StarCore, Symphony and VortiQa are trademarks of Freescale Semiconductor, Inc., Reg. U.S. Pat. & Tm. Off. Airfast, BeeKit, BeeStack, CoreNet, Flexis, Layerscape, MagniV, MXC, Platform in a Package, QorIQ Qonverge, QUICC Engine, Ready Play, SafeAssure, the SafeAssure logo, SMARTMOS, Tower, TurboLink, Vybrid and Xtrinsic are trademarks of Freescale Semiconductor, Inc. All other product or service names are the property of their respective owners. © 2013 Freescale Semiconductor, Inc.

figure source; http://en.wikipedia.org/wiki/File:Centrifugal_Pump-mod.jpg

This machine includes:• rotating motor• centrifugal pump• linkage between the

two

Each is subject to its own array of problems. These might include:• Bearing failures• load imbalance• shaft misalignment• looseness• gearbox faults• drive belts• resonance


TangentialAxial

Radial

Aligned

Angular misalignment causes axial vibration at1X running frequency

Parallel misalignment causes radial vibration at2X running frequency

Shaft Misalignment


Pd = pitch diameterBd = ball diameterNb = number of ballsS = speed (revolutions/sec) = contact angleBSF = Ball Spin FrequencyBPFO = Ball Pass Frequency of Outer TraceBPFI = Ball Pass Frequency of Inner Trace

For ball defects:

BSF = ½ (Pd/Bd) x S x [1 – (Bd/Pd x cos)2]

For outer trace defects:

BPFO = ½ Nb x S x [1 – (Bd/Pd x cos)]

For inner trace defects:

BPFI = ½ Nb x S x [1 + (Bd/Pd x cos)]

Bearing faults have specific frequency signatures

Defect signals may be swamped by other noise in the system, in which case enveloping or wavelet techniques may be used to extract the signature.

Pd

Bd


10

http://commons.wikimedia.org/wiki/File:NonSynchronousGearBoxSF.jpg

gear mesh speed = shaft speed X # of teeth

So = Si X Ti/To

where:Ti = number of input teethTo = number of output teethSi = input speedSo = output speed

Gears also have specific frequency signatures

11TM


CBM Breakdown

Condition based maintenance

diagnostics prognostics

what went wrong?includes estimation for remaining useful life

data driven

physics-based

require sufficient samples that were run to failure

must understand the physics of expected failure progression and how to get parameters required for the model

more mature than prognostics

statistical

machine learning

We should differentiate between:• rotating vs linear motion• constant rotation versus step

oriented


Maintenance Scenarios

maintenancepreparation

actualmaintenance

scheduledmaintenance

maintenancepreparation

continuous RUL estimation

Fault DetectionDown Time

Down Time

Unscheduled Maintenance

Managed Maintenance

From “Major Challenges in Prognostics: Study on Benchmarking Prognostics Datasets” by Eker, Camci and Jennions

Preventive maintenance can be scheduled when downtime is cheapest.Notice that in the 2nd scenario, we can minimize inventory and do prep work while still “line-up”


ISO 13374 Provides a Standard Architecture for Condition Monitoring & Diagnostics

Sensor / Transducer / Manual Entry

Data Acquisition (DA)

Data Manipulation (DM)

State Detection (SD)

Health Assessment (HA)

Prognostic Assessment (PA)

Advisory Generation (AG)

External systems, data archiving and

block configuration

Technical displays and information presentation

DA Basically a “server of calibrated digitized sensor data records”. Outputs include digitized data, timestamps, data quality indicators

DM Extracts features from digitized sensor data. Examples: FFT, wavelet, virtual sensor, filtered data, normalized data, etc.

SD Compares DA & DM outputs against expected baselines / operational limits to determine health indicators. Outputs might include enumerated state values, threshold alerts, rate of change alerts, deviation severities, etc.

HA Determine the current health of the system and diagnose fault conditions

PA Estimate remaining useful lifePredict faults / failuresGenerate recommendations

AG Integrate outputs from all other blocks and provide optimized recommendations, courses of action, advisories, prioritized operational & maintenance actions, etc.


Let’s explore a very simple example…a basic servomotor

Motor Control Electronics

1ms to 2ms pulse every

20ms

Accelerometer Sensor Board

Laptop with interface program and Matlab

samples at 400Hz rate

and REPEAT

accelerometer readings over time


340 350 360 370 380 390 400 410 420-6000

-4000

-2000

0

2000

4000

6000Z acceleration vs time

340 350 360 370 380 390 400 410 4200

200

400

600

800

1000

1200

1400

1600

1800

2000

time (seconds)

rms

rms of accZ moving window of size 89

Looking at actual Z-axis accelerometer readings

These spots are where the motor does the wide swing back to its original position

motor is stationary for 1 second between movements of ~15 degrees at each step


Compare a Good vs “Marginal” motor

20 40 60 80 100 120 140-1.5

-1

-0.5

0

0.5

1

1.5x 10

4 Z acceleration vs time

20 40 60 80 100 120 1400

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

time (seconds)

rms


340 350 360 370 380 390 400 410 420-6000

-4000

-2000

0

2000

4000

6000Z acceleration vs time

340 350 360 370 380 390 400 410 4200

200

400

600

800

1000

1200

1400

1600

1800

2000

time (seconds)

rms


Good

Marginal

Motor sometimes “chatters”


How does a computer tell a healthy motor from a sick one?

Steps are:

1. Sample data

2. Filter

3. Extract features

4. Segment data stream

5. Extract states

0 20 40 60 80 100 120 140 160 180 200

5

10

15

20

25

Frequency

FFT(

accZ

)

accZ FFT

0 20 40 60 80 100 120 140 160 180 200

100

200

300

400

500

600

accZ PSD

Frequency

PSD(

accZ

)

Possible features include:• FFT• PSD• AutoCorrelation• Wavelet• RMS value• Peak to RMS• Mean• Crossing Rate• Standard Deviation• Variance• Skew Factor• Kurtosis• Shape Factor


Variances Plots for Good vs Marginal MotorNote the differences in scale

20 40 60 80 100 120 1400

0.5

1

1.5

2

2.5x 10

7

time (seconds)

varia

nce

variance of accZ moving window of size 89

340 350 360 370 380 390 400 410 4200

0.5

1

1.5

2

2.5

3

3.5

4x 10

6

time (seconds)

varia

nce

variance of accZ moving window of size 89

A Good Motor A Marginal Motor

red line = threshold used to break continuous data stream up into “frames”


20 40 60 80 100 120 1400

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

time (seconds)

std

std of accZ moving window of size 89

340 350 360 370 380 390 400 410 4200

200

400

600

800

1000

1200

1400

1600

1800

2000

time (seconds)

std

std of accZ moving window of size 89

A Good Motor A Marginal Motor

Standard Deviation Plots for Good vs Marginal MotorNote the differences in scale

red line = threshold used to break continuous data stream within frames into a sequence of “symbols”


0 20 40 60 80 100 120 140 160 180 200

5

10

15

20

25

Frequency

FFT(

accZ

)

accZ FFT

0 20 40 60 80 100 120 140 160 180 200

100

200

300

400

500

600

accZ PSD

Frequency

PSD(

accZ

)

A Good Motor

0 20 40 60 80 100 120 140 160 180 200

5

10

15

20

Frequency

FFT(

accZ

)

accZ FFT

0 20 40 60 80 100 120 140 160 180 200

100

200

300

400

accZ PSD

Frequency

PSD(

accZ

)

A Marginal Motor

FFT and PSD Plots for Good vs Marginal Motor

These motors are made by two different manufacturers – the difference is clear when viewing the spectrum


Auto-Correlation Plots

-80 -60 -40 -20 0 20 40 60 80-0.2

0

0.2

0.4

0.6

0.8

1

1.2

lag

auto

corre

latio

n of

accZ

A Good Motor

-150 -100 -50 0 50 100 150-0.2

0

0.2

0.4

0.6

0.8

1

1.2

lag

auto

corre

latio

n of

accZ

A Marginal Motor

It makes sense the that device on the left would show better auto-correlation, as it has significantly less “chatter”.


After segmentation and feature extraction

350 360 370 380 390 400 410

-5000

0

5000

time (seconds)

accZ

Z Acceleration vs Time

350 360 370 380 390 400 410-2

-1

0

1

2Computed States

Time

1=hi

gh n

oise

, 0=u

nkno

wn, -

1=lo

w no

ise

A Good Motor

30 40 50 60 70 80 90 100 110 120 130

-1

-0.5

0

0.5

1

x 104

time (seconds)

accZ


30 40 50 60 70 80 90 100 110 120 130-2

-1

0

1

2Computed States

Time

1=hi

gh n

oise

, 0=u

nkno

wn, -

1=lo

w no

ise

A Marginal Motor

For lower plots:1 = high noise or start of frame0 = not computed (partial frames)-1 = low noise


Close-up on the Marginal Motor

30 40 50 60 70 80 90 100 110 120 130

-1

-0.5

0

0.5

1

x 104

time (seconds)

accZ


30 40 50 60 70 80 90 100 110 120 130-2

-1.5

-1

-0.5

0

0.5

1

1.5

2Computed States

Time

1=hi

gh n

oise

, 0=u

nkno

wn, -

1=lo

w no

ise

We could also use “# of recognized symbols/frame” as a figure of merit


Scaling it up to real life

• We are consulting with our in-house wafer fab to use these techniques to provide early warning indicators of “maintenance required” for wafer handling equipment (shown to the right).

• Initial results are promising. Primary limitation is getting production engineers to take time out of their day for data collection.

0 30 60 90 120 150 180 210 240 270

-2000

0

2000

FG10PLT-CP6 GLACIER-FXOSAccY-1

Time (s)

Sens

or O

utpu

t (AD

C Co

unts

)

Hote

ntry

PLT

1PL

T2

PLT

4

Lift

Spin

off

Rins

eSp

in

FG10PLT-CP6 GLACIER-FXOSAccY-1

Time (s)

Freq

uenc

y (H

z)

Hote

ntry

PLT

1PL

T2

PLT

4

Lift

Spin

off

Rins

eSp

in

0 30 60 90 120 150 180 210 2400

100

200

300

400


Freescale is developing collateral designed to simplify implementation of condition monitoring systems.

Data Acquisition (DA)

Data Manipulation (DM)

State Detection (SD)

Health Assessment (HA)

Prognostic Assessment (PA)

Advisory Generation (AG)

Sensor / Transducer / Manual Entry

DevelopmentBoard

Matlab

Choices include: • Sensor Type• Axis (X, Y, Z, temp, etc)• Sample Rate

Feature Extraction choices include:• FFT• Wavelets• Entropy• RMS noise• peak value• etc.

Use Freescale supplied GUIs and embedded apps to experiment with data capture and feature selection, then utilize Matlab machine learning algorithms to develop higher level ISO 13374 functions.


In real time, the user can change:• Sensor• Axis• Window• Filter

All FFT computations are done on the sensor board.

Data can be logged, replayed, and transferred to Matlab.

Freescale Xtrinsic Vibration Monitoring


Also under development – Wavelet Analysis


Conclusions

• Useful vibration analysis work can be done with consumer grade MEMS given the right enablement tools and libraries.

• Some problems will require wider dynamic range and bandwidth sensors.• We don’t have to re-invent the wheel. There is a mature industry doing

machine monitoring. Re-use of existing techniques can bootstrap new applications.

• Freescale Semiconductor is actively working to bring hardware and software components together to make adoption of these techniques easier.

29TM


To Learn More

Organizations:• Open Operations &

Maintenance Initiative (http://www.openoandm.org)

• Machinery Information Management Open System Alliance (http://www.mimosa.org)

• International Organization for Standardization (http://www.iso.org)

• Center for Intelligent Maintenance Systems (http://www.imscenter.net)

References:

1. “Commercialization of Prognostics Systems Leveraging Commercial Off-The Shelf Instrumentation, Analysis and Data Base Technologies”, Preston Johnson

2. “Vibration Diagnostic Guide”, SKF Reliability Systems

3. “Harris Shock and Vibration Handbook”, 6th edition, Allan G. Piersol & Thomas L. Paez, McGraw Hill

4. “A Review of PHM System’s Architectural Frameworks”, Surya Kunch, Chaochao Chen & Michael Pecht.

5. “Review of Vibration Analysis Methods for Gearbox Diagnostics and Prognostics”, Mitchell Lebold, et. al.

http://www.openoandm.org/

http://www.mimosa.org/

http://www.iso.org/

http://www.imscenter.net/

external use tm layered intelligence for machine monitoring sensors expo 2014 michael stanley mark...

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