1© 2016 The MathWorks, Inc.

Predictive Maintenance with MATLAB: A Prognostics Case Study

Simone Lombardi, Ph.D.

Application Engineer

MathWorks

2

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

3

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

4

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

5

Benefits of Predictive Maintenance

Increase “up time” and safety Reliability

Minimize maintenance costs Cost of Ownership

Optimize supply chain Reputation

6

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

7

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/

8

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/

9

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%

AAA AA A BBB BB B CCC D

AAA

AA

A

BBB

BB

B

CCC

D

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%

AAA AA A BBB BB B CCC D

AAA

AA

A

BBB

BB

B

CCC

D

10

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

11

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

12

Principal Components Analysis – what is it doing?

13

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

14

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/

15

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

16

How Data was Recorded

?

His

tori

cal

Live

Engine1

Engine2

Engine100

Initial Use/Prior Maintenance

Time(Flights)

Engine200

Recording Starts Failure Maintenance

?

?

?

?

Schedule Maintenance

17

Integrate analytics with your enterprise systemsMATLAB Compiler and MATLAB Coder

.exe .lib .dll

MATLABCompiler SDK

MATLABCompiler

MATLABRuntime

MATLAB Coder

18

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

19

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

11

44

22

33

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.