1© 2016 The MathWorks, Inc.

Predictive Maintenance Prognostics and Health Monitoring

David Willingham – Senior Application Engineer

david.willingham@mathworks.com.au

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Fleet Analytics

Health Monitoring

Asset Analytics

Process Analytics

Prognostics

Condition

MonitoringRetail Analytics

Supply Chain

Operational

Analytics

Healthcare Analytics

Risk Analysis

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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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Aircraft Maintenance

Report Link

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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/

View Presentation

Enterprise

Integration

• 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 know

the 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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Overview – Machine Learning

Machine

Learning

Supervised

Learning

Unsupervised

Learning

Group and interpretdata based only

on input data

Develop predictivemodel based on bothinput 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

Engine1

Engine2

Engine3

Engine1

Engine2

Engine3

Engine1

Engine2

Engine3

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

Machine

Learning

Supervised

Learning

Classification

Regression

Unsupervised

Learning

Group and interpretdata based only

on input data

Develop predictivemodel based on bothinput and output data

Type of Learning Categories of Algorithms

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How Data was Recorded

?

His

torica

lL

ive

Engine1

Engine2

Engine100

Initial Use/

Prior Maintenance

Time

(Flights)

Engine200

Recording Starts Failure Maintenance

?

?

?

?

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Integrate analytics with your enterprise systemsMATLAB Compiler and MATLAB Coder

.exe .lib .dll

MATLAB

Compiler SDK

MATLAB

Compiler

MATLAB

Runtime

MATLAB Coder

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MathWorks Services

Consulting– Integration

– Data analysis/visualization

– Unify workflows, models, data

Training

– Classroom, online, on-site

– Data Processing, Visualization, Deployment, Parallel Computing

www.mathworks.com/services/consulting/

www.mathworks.com/services/training/

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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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Why MATLAB for

Engineering Analytics

Data Analytics

Smart 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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Learn More

www.mathworks.com/discovery/predictive-maintenance.html

22© 2016 The MathWorks, Inc.

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