Tampa Convention Center • Tampa, Florida
Advanced Pattern Recognition for Anomaly Detection
Advanced Software Technologies
Chance Kleineke/Michael SantucciEngineering Consultants Group Inc.
August 16, 2017
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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
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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?
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• 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
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• 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
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• 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
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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.
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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
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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
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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
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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
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• 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
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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
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Asset Network
Fault cases/Rules
Observations/Symptoms
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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
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• 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!