Download - XMPLR Data Analytics in Power Generation
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Opportunities for Data Analytics in Power Generation
Scott Affelt
XMPLR Energy
PowerGen Natural Gas 2016
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XMPLR Energy
• Consulting
– Business Strategy
– Disruptive Technology Introduction
– M&A
• Technology Liaison
– Licensing & Technology Transfer
– Foreign Market Introduction
• Data Analytics
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Key Opportunities for Data Analytics
• Efficiency – Fuel Costs
– Capacity/Output
• Reliability – Availability
– Capacity/Output
– Load Factor
– Maintenance
• Emissions – Compliance
– Optimization
• Flexibility – Operational
– Economic
Focus of Today’s Talk Data Analytics & Prognostics
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Why is this Important?
Source: Duke Energy. 2007-2012
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Why is this Important?
• MIT Study: Bearing Failures in Rotating Equipment
cause $240B in downtime and repair costs EVERY YEAR!
• DOE Study on Predictive Maintenance: Reduce equipment downtime by 35% Reduce unplanned breakdowns by 70% Reduce plant maintenance costs by 25%
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Domain Expertise
Computer Science
Math & Statistics
How can Data Analytics Help?
Machine Learning
Data Processing
Traditional Software
DATA ANALYTICS
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Approaches to Data Analytics
Difficulty
Value Descriptive Analytics
What happened?
Diagnostic Analytics
Why did it
happen?
Predictive Analytics
When will it
happen?
Prescriptive Analytics
What should I do about it?
Source: Gartner
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Diagnostics vs Prognostics
• Failure mode detection
• Fault Location • Detection • Isolation
• RUL Estimation • Degradation
prediction
• Health assessment • Severity detection • Degradation detection • Advanced Pattern
Recognition
DIAGNOSTICS PROGNOSTICS
Source: IVHM Center
What Why
Where How
When
Phase 1 Diagnose
Phase 2 Predict
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Traditional Failure Curve
• Failure rates are high in the early life – “infant mortality” • Failure rates are high at the end of life • Remaining useful life can be predicted. • Based on assets that have regular & predictable wear • The Rise of Predictive Maintenance!
Source: EPRI
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Predictive Maintenance
Time
Equ
ipm
en
t H
eal
th I
nd
ex
Total Cost
Reactive: Run to Failure
Preventative: Schedule Base
Predictive: Condition-based
Advanced Patterns Prognostics
Co
st
Fault
Equipment Fails Here
Degradation Starts Here
Fault Occurs Here
Do Maintenance WHEN it is needed. BEFORE Failures Occur.
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Predicting Failures is Hard
Only 18% of Failures are age-related
82% of Failures are
random
“It’s hard to make predictions, especially about the future!”
Yogi Berra
Sources: RCM Guide, NASA, Sept. 2008, and U.S.. Navy Analysis of Submarine Maintenance Data 2006
„
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Using Predictive Analytics
• Condition Monitoring – Identifies anomalies in data trends – Typically, provides little warning of a fault (hrs to days) – Little information regarding diagnostics or prognostics
Detect
Diagnose
Predict
• Advanced Pattern Recognition – Specific signal patterns (features) linked to faults – Gives indication of likely future fault . – Focused on diagnosis or cause of the fault not Remaining Useful Life.
• Prognostics – Predicts Remaining Useful Life (RUL) of asset or component
– RUL is time at which it will no longer perform its intended function
– Includes a confidence level associated with the time prediction. • (i.e. RUL of 3 months at 60% confidence level
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Turbofan Engine RUL Example
13
Source: Expert MicroSystems
Trending Data Provides Little Insight into Faults
Residual (Actual – Expected) Can Identify Small Anomalies
RUL Predictions Begin at Anomaly Detection & Updated
Over Time
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Prognostic Approaches
Physics Based Models
Data-based Models
Classification, fuzzy logic, NN, regression
Experience Based Models
Generic, statistical life, mean time to failure
Range of Applicability
Incr
eas
ing
Co
st a
nd
Acc
ura
cy
Experienced-based Models
Advantages
• Based on actual failure experience • Rules-based • Simple • Little data required
Disadvantages
• Little prediction capability • Requires subject matter experts • Needs continued observations • Difficult to scale to other assets
Data-based Models
Advantages
• Relatively simple and fast to implement
• Physical cause/effects understanding not necessary
• Gain understanding of physical behaviors from large datasets
• Good for complex processes
Disadvantages
• Physical cause/effects not utilized • Difficult to balance generalizations
and specific learning trends • Requires large datasets to
characterize fault features
Physics-based Models
Advantages
• Prediction results based on intuitive cause/effects relationship
• Calibration only needed for different cases
• Drives sensor requirements • Accurate
Disadvantages
• Model development may be hard • Requires complete knowledge of
physical process • Large datasets may be unpractical
for real-time predictions • Getting the “right” data may be
difficult
Hybrid Models
Physics + Data-based
Hybrid Models • Use components of both Physics
and Data-based models • Can provide more robust and
accurate RULs • Can address disadvantages of each
type of model
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Power Generation Applications for APR & Prognostics
Rotary Equipment • Turbines
• Pumps
• Generators
• Compressors
• Gearbox
• Bearings
• Fans
Other Possibilities • Boiler Tubes?
• HRSG?
• Condensers?
• Heaters?
• Valves?
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APR & Prognostics Implementation Challenges
• Data – Measuring the right things – Cost of New Sensors – Access to multiple data
sources – Clean data
• People – M&D centers: leverage
expertise – In-house vs outsource
• Accuracy – Confidence in predictions – Uncertainty in predictions – Validation and verification
• Data Security – Various data sources – Cloud-based or local server – Sharing data/information
within company
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Conclusions
• Data analytics can help operators manage and improve reliability of generation assets.
• Advance Pattern Recognition can be used to detect and diagnose anomilies BEFORE they are a problem.
• Prognostics can be used to predict the RUL with a time element and confidence level
• Operators can use the RUL to actively manage maintenance schedule and operating conditions in order to maintain reliability.