1© 2016 The MathWorks, Inc.
Predictive Maintenance with MATLAB: A Prognostics Case Study
Simone Lombardi, Ph.D.
Application Engineer
MathWorks
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Medical Devices
Aeronautics
Off-highwayvehicles
Automotive
Oil & Gas
Industrial Automation
Fleet Analytics
Health Monitoring
Asset Analytics
Process Analytics
Prognostics
ConditionMonitoring
Clean Energy
Retail Analytics
Mfg Process Analytics
Supply Chain
OperationalAnalytics
Healthcare Analytics
Risk Analysis
Logistics
Retail
Finance
HealthcareManagement
Internet
Railway Systems
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Why perform predictive maintenance?
Example: faulty braking system leads to windmill disaster– https://youtu.be/-YJuFvjtM0s?t=39s
Wind turbines cost millions of dollars
Failures can be dangerous
Maintenance also very expensive and dangerous
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Types of Maintenance
Reactive – Do maintenance once there’s a problem– Example: replace car battery when it has a problem
– Problem: unexpected failures can be expensive and potentially dangerous
Scheduled – Do maintenance at a regular rate– Example: change car’s oil every 5,000 miles
– Problem: unnecessary maintenance can be wasteful; may not eliminate all failures
Predictive – Forecast when problems will arise– Example: certain GM car models forecast problems with the battery, fuel pump, and
starter motor
– Problem: difficult to make accurate forecasts for complex equipment
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Benefits of Predictive Maintenance
Increase “up time” and safety Reliability
Minimize maintenance costs Cost of Ownership
Optimize supply chain Reputation
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What Does Success Look Like?Safran Engine Health Monitoring Solution
Monitor Systems– Detect failure indicators
– Predict time to maintenance
– Identify components
Improve Aircraft Availability– On time departures and arrivals
– Plan and optimize maintenance
– Reduce engine out-of-service time
Reduce Maintenance Costs– Troubleshooting assistance
– Limit secondary damage
http://www.mathworks.com/company/events/conferences/matlab-virtual-conference/
EnterpriseIntegration
• Real-time analytics• Integrated with
maintenance and service systems
• Ad-hoc data analysis• Analytics to predict failure
• Suite of MATLAB Analytics
• Shared with other teams• Proof of readiness
DesktopCompiled
Shared
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Sensor data from 100 engines of the same model
Predict and fix failures before they arise– Import and analyze historical sensor data
– Train model to predict when failures will occur
– Deploy model to run on live sensor data
– Predict failures in real time
Predictive Maintenance of Turbofan Engine
Data provided by NASA PCoEhttp://ti.arc.nasa.gov/tech/dash/pcoe/prognostic-data-repository/
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Sensor data from 100 engines of the same model
Scenario 1: No data from failures
Performing scheduled maintenance
No failures have occurred
Maintenance crews tell us most engines could run for longer
Can we be smarter about how to schedule maintenance without knowing what failure looks like?
Predictive Maintenance of Turbofan Engine
Data provided by NASA PCoEhttp://ti.arc.nasa.gov/tech/dash/pcoe/prognostic-data-repository/
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Machine LearningCharacteristics and Examples
Characteristics– Too many variables
– System too complex to knowthe governing equation(e.g., black-box modeling)
Examples– Pattern recognition (speech, images)
– Financial algorithms (credit scoring, algo trading)
– Energy forecasting (load, price)
– Biology (tumor detection, drug discovery)
– Engineering (fleet analytics, predictive maintenance)
93.68%
2.44%
0.14%
0.03%
0.03%
0.00%
0.00%
0.00%
5.55%
92.60%
4.18%
0.23%
0.12%
0.00%
0.00%
0.00%
0.59%
4.03%
91.02%
7.49%
0.73%
0.11%
0.00%
0.00%
0.18%
0.73%
3.90%
87.86%
8.27%
0.82%
0.37%
0.00%
0.00%
0.15%
0.60%
3.78%
86.74%
9.64%
1.84%
0.00%
0.00%
0.00%
0.08%
0.39%
3.28%
85.37%
6.24%
0.00%
0.00%
0.00%
0.00%
0.06%
0.18%
2.41%
81.88%
0.00%
0.00%
0.06%
0.08%
0.16%
0.64%
1.64%
9.67%
100.00%
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BBB
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93.68%
2.44%
0.14%
0.03%
0.03%
0.00%
0.00%
0.00%
5.55%
92.60%
4.18%
0.23%
0.12%
0.00%
0.00%
0.00%
0.59%
4.03%
91.02%
7.49%
0.73%
0.11%
0.00%
0.00%
0.18%
0.73%
3.90%
87.86%
8.27%
0.82%
0.37%
0.00%
0.00%
0.15%
0.60%
3.78%
86.74%
9.64%
1.84%
0.00%
0.00%
0.00%
0.08%
0.39%
3.28%
85.37%
6.24%
0.00%
0.00%
0.00%
0.00%
0.06%
0.18%
2.41%
81.88%
0.00%
0.00%
0.06%
0.08%
0.16%
0.64%
1.64%
9.67%
100.00%
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Machine Learning Workflow
Integrate Analytics with Systems
Desktop Apps
Enterprise Scale Systems
Embedded Devices and Hardware
Files
Databases
Sensors
Access and Explore Data
Develop Predictive Models
Model Creation e.g. Machine Learning
Model Validation
Parameter Optimization
Preprocess Data
Working with Messy Data
Data Reduction/ Transformation
Feature Extraction
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Overview – Machine Learning
MachineLearning
SupervisedLearning
UnsupervisedLearning
Group and interpretdata based only
on input data
Develop predictivemodel based on both
input and output data
Type of Learning
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Principal Components Analysis – what is it doing?
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Example Unsupervised Implementation
Initial Use/Prior Maintenance 125 Flights
Maintenance
135 Flights 150 Flights
Engine1Engine2Engine3
Engine1Engine2Engine3
Engine1Engine2Engine3
Ro
un
d 1
Ro
un
d 2
Ro
un
d 3
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Sensor data from 100 engines of the same model
Scenario 2: Have failure data
Performing scheduled maintenance
Failures still occurring (maybe by design)
Search records for when failures occurred and gather data preceding the failure events
Can we predict how long until failures will occur?
Predictive Maintenance of Turbofan Engine
Data provided by NASA PCoEhttp://ti.arc.nasa.gov/tech/dash/pcoe/prognostic-data-repository/
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Overview – Machine Learning
MachineLearning
SupervisedLearning
Classification
Regression
UnsupervisedLearning
Group and interpretdata based only
on input data
Develop predictivemodel based on both
input and output data
Type of Learning Categories of Algorithms
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How Data was Recorded
?
His
tori
cal
Live
Engine1
Engine2
Engine100
Initial Use/Prior Maintenance
Time(Flights)
Engine200
Recording Starts Failure Maintenance
?
?
?
?
Schedule Maintenance
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Integrate analytics with your enterprise systemsMATLAB Compiler and MATLAB Coder
.exe .lib .dll
MATLABCompiler SDK
MATLABCompiler
MATLABRuntime
MATLAB Coder
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Key Takeaways
Frequent maintenance and unexpected failures are a large cost in many industries
MATLAB enables engineers and data scientists to quickly create, test and implement predictive maintenance programs
Predictive maintenance – Saves money for equipment operators
– Increases reliability and safety of equipment
– Creates opportunities for new services that equipment manufacturers can provide
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MATLABDifferentiators
Data AnalyticsSmart Connected Systems
DATA• Engineering, Scientific, and Field• Business and Transactional
Business Systems
Analytics that increasingly require both business and engineering data
Developing embedded systems
which have increasing
analytic content
Enable Domain Experts to do Data Science
Deploying applications that run on both traditional IT and embedded platforms
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20© 2016 The MathWorks, Inc.
© 2016 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
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