advanced pattern recognition for anomaly detection · tampa convention center • tampa, florida...

Tampa Convention Center • Tampa, Florida

Advanced Pattern Recognition for Anomaly Detection

Advanced Software Technologies

Chance Kleineke/Michael SantucciEngineering Consultants Group Inc.

August 16, 2017

Energy Exchange: Connect • Collaborate • Conserve

Power Plant Monitoring Scope

• Typical Power Plant has ~ 2000 I/O per Unit• Temperature, Pressure, Flow, Vibration, etc.• Units typically have 20-40 Critical Assets• Pumps, Fans, Turbines, HeatXchangers, etc• One Plant Operator may monitor 2 or 3 units• “Hard Limit” Alarm Thresholds for each sensor• Plant Reacts to Alarms – Too Late!

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Sensor Alarm limits must be set outside of normal operating range and cover all ambient conditions

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Alarm Thresholds

Hi Alarm Limit


Developing the Expected Value

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Expected Value - Classical Approach

Classical Approach:

• Use Design information – Owner Manual• Consider Influencing Factors - Ambient Temperature, Running speed, etc• Use first principals equations to calculate expected pressure; Boyle’s Law, MLR

Pressure = Function (Press_Design, Ambient Temp, Tire Speed)Expected Pressure = Constant + A1*Temp + A2* Speed + A3*Other (Mult Lin Regress)

Short comings...• Other Factors, Tread, wear, Passengers, road condition, Bias/Radial etc.• What happens if you loose one of your inputs? One sensor Calibration issue?


• New Statistical Approach Considers– What are the pressures of the other tires?– Use other correlated sensors to determine where the subject sensor should be – Expected values generated from history (PI Archive)– Includes all higher order effects

• Similarity Based Modeling: – Relies on the correlation between variables, not the variables themselves– Uses history which incorporates all the “flaws” in the data and higher order

effects– Robust - Can run with missing inputs– Precise – can detect small disturbance in process ie. “Slow Leak”

• Expected Pressure = F(History : Press_Tire1, Press_Tire2, Press_Tire3, Press_Tire4)

• Other Factors including, wear, Passengers, road condition, etc. are already included!

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Statistical Approach


• APR is an empirical modeling technique deploying algorithms used to detect process anomalies and performance degradation in real-time.

• Non parametric models are superior to other techniques in their ease of deployment, simplicity and computational overhead.

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Advanced Pattern Recognition


• APR software uses historical tag values to create models for assets based on past performance

• Powerful algorithms detect subtle changes in equipment behavior days, weeks and even months before conventional monitoring techniques

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How Does IT Work?

GetHistory from All

Operating Conditions

Create Model and Calculate

Tolerances

Select Correlated Sensors

Remove Redundant Data

and Outliers

Alarm on any Statistically Significant Deviation

Energy Exchange: Connect • Collaborate • Conserve8

Equipment and OEM Agnostic

Rotating Equipment

Nonrotating Equipment

Turbines Heat Exchangers

Pumps Cooling Towers

Fans Condensers

Pulverizers Transformers

Generators Precipitators

Motors Blowers

Compressors Reactor Vessels

Etc. Etc.


Model Sensors – Expected Value with Limits

Actual Real-time value from data historian Expected Predicted value from Predict-It EstimatorDeviation Difference between Actual and Expected


Equipment Failure Life Cycle

PredictItAlarm

Conventional Monitoring

System Alarm

Develop options: 1. Change operating conditions2. Re-sequence with other maintenance3. Better planned outage

People Making the right decisions when it matters.

Photos courtesy of Reliabilityweb.com


Case Study – Generator Winding Failure

• Generator Cooled by water flowing through slots in generator.

• This case illustrates abnormal trending of a winding temperatures was detectable over a year before the generator failed.

Three Years

Planned Outage

Anomaly Detected

1 year before Failure


Model Building


Modeling Technique – Nearest Neighbors

Training Data Difference Data SumLF RF LR RR LF RF LR RR Sum(||)25 24 25 27 -5 -5 -5 -5 20

26 25 26 28 -4 -4 -4 -4 16

27 26 26 28 -3 -3 -4 -4 14

28 27 27 29 -2 -2 -3 -3 10

29 28 28 30 -1 -1 -2 -2 6

30 29 29 31 0 0 -1 -1 2

30 30 30 32 0 1 0 0 1

31 30 31 33 1 1 1 1 4

32 31 31 34 2 2 1 2 7

33 32 32 35 3 3 2 3 11

34 33 33 36 4 4 3 4 15

Snapshot or Test DataLF RF LR RR

30 29 30 32

Minimum Difference


• A proven statistical technology solution that can be used centrally to provide an effective failure early warning system to the business across a diverse energy generation portfolio of assets

• Intuitive Graphical Representation of Model Results

• Easy to set up and use ensuring a fast road to value realization

• Fast and efficient model execution speed • Supports Real Time Causal Network Diagnostics

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APR Advantage


Demo?


Model Building - Select Process Points


Model Building - Select Training Data


Model Building - Data Remove Outliers


Model Building - Run Model


Diagnostics – Bayesian Networks

• Inputs:– Real-time Residual Deviation Alarms– Manual Tech Exam (Oil analysis)– Case Failure Database

• Output– Probability of Fault– Causation Mapping– Self Learning


Asset Network

Fault cases/Rules

Observations/Symptoms


Fault Learning and Inferrence

Database of Fault Signatures

Expert

Causal Network

Asset Variable Behavior (Alarms)

Decision Support- Diagnosis- What-If

New Faults

Asset Knowledge Real-Time Diagnostics


• Mike Santucci, ECG Inc.• [email protected]• Phone: 330-807-7661

• Chance Kleineke, ECG Inc. • [email protected]• Office: 330-869-9949

• Stop by Booth 404

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Thank You!

mailto:[email protected]

mailto:[email protected]

advanced pattern recognition for anomaly detection · tampa convention center • tampa, florida...

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