Predictive Maintenance with MATLAB · Pump Health Monitoring System Spectral analysis and filtering...

1© 2015 The MathWorks, Inc.

Predictive Maintenance with

MATLAB

Amit Doshi,

Senior Application Engineer – Data Analytics

MathWorks India

[email protected]

mailto:[email protected]

3

Agenda:

1. What is Predictive Maintenance? Who is benefiting by doing it?

2. How can you develop a predictive maintenance algorithm using MATLAB?

3. How can you get started quickly?

4

Predictive maintenance

Preventive maintenance

Reactive maintenance

Types of maintenance

5

Time

Machine

health


xFailure

6

Time

Machine

health


xFailure

Aircraft

engine

7

Time

Machine

health



Time

Machine

health

xFailure

?

?

8

Time

Machine

health


xFailure


Time

Machine

health

x Still usable

condition

9

Time

Machine

health


xFailure


Time

Machine

health

x Still usable

condition

Predictive maintenance

Time

Machine

health

xnow

Predicted

failure

Optimum time to

do maintenance

10

Condition

Monitoring

Remaining

Useful Life

Estimation

A Predictive Maintenance Algorithm Answers

These Questions

Why is my

machine behaving

abnormally?

How much longer

can I operate my

machine ?

Anomaly

Detection

Is my machine

operating

normally?

For example:

I need help.

One of my cylinders is blocked.

I will shut down your line in 15 hours.

11

Predictive Maintenance Success Stories

Online engine health monitoring

▪ Real-time analytics integrated with enterprise

service systems

▪ Predict sub-system performance (oil, fuel,

liftoff, mechanical health, controls

Pump Health Monitoring System

▪ Spectral analysis and filtering on binary sensor data

and neural network model prediction

▪ More than $10 million projected savings

Production machinery failure warning

▪ Reduce waste and machine downtime

▪ MATLAB based HMI warns operators of

potential failures

▪ > 200,000 € savings per year

12

Link to user story

https://www.mathworks.com/videos/condition-and-performance-monitoring-of-blowout-preventer-bop-at-transocean-1545303832202.html?s_tid=srchtitle

13

Transocean uses MATLAB tools to transition from preventative

maintenance to CPM* for a critical deep sea drilling component*Condition and Performance Monitoring

Challenges

• Minimize unplanned downtime of the component

• Use as-close-to-real-time data for CPM

• Deploy CPM solution to components in other locations

Solution

▪ Monitoring data from the drill is collected in a PI archive. MATLAB is used

to create an app that takes in the data, preprocesses the data, and

generates a quality indicator.

▪ The app is deployed onto MATLAB Production Server for real-time use;

results are stored back on the PI archive.

Results

Able to move to CPM for their component(s) with improved decision-making

capabilities through faster access to data and quicker analytics deployment.

Interfacing directly with data in PI gives you increased

performance at the cost of having to write/deal with

low-level code (“getting under the hood”). It was noted

that using MATLAB parallel tools (which entails a

higher-level interface) was not only easier but also it

provided “hands-down” faster performance than

interfacing with PI directly.

Link to Expo Talk

Drilling data is stored in an OSISoft PI Archive. MATLAB

Production Server is used in the application specific

processing

https://www.mathworks.com/videos/condition-and-performance-monitoring-of-blowout-preventer-bop-at-transocean-1545303832202.html

14

Condition

Monitoring

Remaining

Useful Life

Estimation

Predictive Maintenance Toolbox for Developing Algorithms

Why is my

machine behaving

abnormally?

How much longer

can I operate my

machine ?

Anomaly

Detection

Is my machine

operating

normally?

15

Workflow for Developing a Predictive Maintenance Algorithm

Acquire

Data

Preprocess

Data

Identify

FeaturesTrain

Model

Deploy &

Integrate

Machine Learning

16

Agenda:




17

Condition

Monitoring

Remaining

Useful Life

Estimation

Develop Predictive Maintenance Algorithm: Use cases

Why is my

machine behaving

abnormally?

How much longer

can I operate my

machine ?

Anomaly

Detection

Is my machine

operating

normally?

18

Condition

Monitoring

Develop Predictive Maintenance Algorithm for -

Why is my

machine behaving

abnormally?

Predictive

maintenance

algorithm

Identify

fault type

Determine what

needs to be fixed

Condition indicators

Use case 1: Fault Classification

19

Remaining

Useful Life

Estimation

Develop Predictive Maintenance Algorithm for -

Use case 2: Remaining useful life

How much longer

can I operate my

machine ?

Estimate

remaining useful life

Time

Machine

health

Current

condition

Remaining useful life

xx x

x

Predictive

maintenance

algorithm


21

Time

Sensor data

Predictive Maintenance Algorithm

Preprocess

Data

Identify

ConditionIndicators

Train

Model

Deploy &

Integrate

Triplex pump

Temperature

Pressure

Flow

Collect data using sensorshealthy

+faulty

*For simplification purposes, healthy and faulty

operation are represented by single

measurements. In a realistic scenario, there

may be hundreds of measurements for bothtypes of operations.

Acquire Data

Sensor Data

Generated

Data

22

AcquireData


Preprocess

Data

Identify

ConditionIndicators

Train

Model

Deploy &

Integrate

Triplex pump

Temperature

Pressure

Flow


+faulty

Time

Sensor data

*Quality data->Robust Algorithms

23

AcquireData


Preprocess

Data

Identify

ConditionIndicators

Train

Model

Deploy &

Integrate

Triplex pump

Temperature

Pressure

Flow


+faulty

Time

Sensor data

24

Time

Sensor data

AcquireData


Preprocess

Data

Identify

ConditionIndicators

Train

Model

Deploy &

Integrate

Triplex pump

Temperature

Pressure

Flow


+faulty

Operating temperature: 30°F

Fluid viscosity: Low

Operating temperature: 90°F

Fluid viscosity: High

25

AcquireData


Preprocess

Data

Identify

ConditionIndicators

Train

Model

Deploy &

Integrate

Triplex pumpTime

Sensor data

What if real failure data is not available?

26

Time

Sensor data

AcquireData


Preprocess

Data

Identify

ConditionIndicators

Train

Model

Deploy &

Integrate

Triplex pumpTime

Sensor data

Inject faults1Seal leakage2Blocked inlet3Worn bearing Mathematical model

of the triplex pump

(Digital Twin)

Refine model

1

2

3

Time

Synthetic data

27

240 measurements of

flow and pressure with a

duration of 1.2 seconds

Flow Pressure

Health condition (fault code)

• Healthy (0)

• Blocked inlet (010)

• Worn bearing (001)

• Seal Leakage (100)

• Blocked inlet, worn bearing (011)

• Seal leakage, worn bearing (101)

• Seal leakage, blocked inlet (110)

• Seal leakage, blocked inlet, worn bearing (111)

Learn further: Data Ensembles

https://www.mathworks.com/help/predmaint/ug/data-ensembles-for-condition-monitoring-and-predictive-maintenance.html

28


Preprocess

Data

Identify

ConditionIndicators

Train

Model

Deploy &

Integrate

Acquire Data

Sensor Data

Generated

Data

Challenges

▪ Data clean up

– Poorly formatted files

– Irregularly sampled data

– Redundant data, outliers, missing data etc.

▪ Data specific processing

– Signals: Smoothing, resampling, denoising, Wavelet transforms, etc.

– Images: Image registration, morphological filtering, deblurring, etc.

29

0.5 1 1.5 2 2.5 3 3.5 4

Time (s)

6.5

7

7.5

8

8.5

9

9.5

10

Raw data

Pre

ssu

re (

ba

r)Spikes to sensor’s

maximum value

Offset in time

31

0.5 1 1.5 2 2.5 3 3.5 4

Time (s)

6.5

7

7.5

8

8.5

9

9.5

10

Raw data

Pre

ssu

re (

ba

r)

Time (s)

Preprocessed data

Pre

ssu

re (

ba

r)

0 0.2 0.4 0.6 0.8 1 1.26.95

7

7.05

7.1

7.15

7.2

7.25

7.3

7.35

32


Preprocess

Data

Identify

ConditionIndicators

Train

Model

Deploy &

Integrate

Acquire Data

Sensor Data

Generated

Data

• What are Condition Indicators?

• Why are they important?

• How to identify significant ones?

33

Identify condition indicators

Time

Condition indicator

Time

Raw data

Healthy

operation

Faulty operation

Healthy

operation

Faulty operation

A condition indicator can be any feature that is useful -

• for distinguishing normal from faulty operation or

• for predicting remaining useful life

34

Signal-Based Condition Indicators

Time-domainfeatures

Frequency-domainfeatures

Time-frequencydomain features

Mean

Standard deviation

Skewness

Root-mean square

. . .

Kurtosis

35

0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69 0.7 0.71 0.72

7

7.05

7.1

7.15

7.2

7.25

7.3

0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69 0.7 0.71 0.72

7

7.05

7.1

7.15

7.2

7.25

7.3

0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69 0.7 0.71 0.72

7

7.05

7.1

7.15

7.2

7.25

7.3

0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69 0.7 0.71 0.72

7

7.05

7.1

7.15

7.2

7.25

7.3

0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69 0.7 0.71 0.72

7

7.05

7.1

7.15

7.2

7.25

7.3

0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69 0.7 0.71 0.72

7

7.05

7.1

7.15

7.2

7.25

7.3

Blocked Inlet

Blocked Inlet, Worn Bearing

Seal Leakage Seal Leakage, Blocked Inlet

Seal Leakage, Worn Bearing

Worn Bearing

Pre

ssu

re (

ba

r)P

ress

ure

(b

ar)

Pre

ssu

re (

ba

r)

Pre

ssu

re (

ba

r)P

ress

ure

(b

ar)

Pre

ssu

re (

ba

r)Time (s) Time (s)

0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69 0.7 0.71 0.72

7

7.05

7.1

7.15

7.2

7.25

7.3

0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69 0.7 0.71 0.72

7

7.05

7.1

7.15

7.2

7.25

7.3

0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69 0.7 0.71 0.72

7

7.05

7.1

7.15

7.2

7.25

7.3

0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69 0.7 0.71 0.72

7

7.05

7.1

7.15

7.2

7.25

7.3

0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69 0.7 0.71 0.72

7

7.05

7.1

7.15

7.2

7.25

7.3

0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69 0.7 0.71 0.72

7

7.05

7.1

7.15

7.2

7.25

7.3

Blocked Inlet


Seal Leakage Seal Leakage, Blocked Inlet


Worn Bearing

Pre

ssu

re (

ba

r)P

ress

ure

(b

ar)

Pre

ssu

re (

ba

r)

Pre

ssu

re (

ba

r)P

ress

ure

(b

ar)

Pre

ssu

re (

ba

r)Time (s) Time (s)

Healthy

Faulty

Increasing

fault severity

36

0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69 0.7 0.71 0.72

7

7.05

7.1

7.15

7.2

7.25

7.3

Blocked Inlet

Pre

ssu

re (

ba

r)

0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69 0.7 0.71 0.72

7

7.05

7.1

7.15

7.2

7.25

7.3

Blocked Inlet

Pre

ssu

re (

ba

r)

Blocked Inlet Healthy

7.225

7.23

7.235

7.24

7.245

7.25

MEAN

No overlap

Median

25th percentile

75th percentile

Minimum

Maximum

37

7.21

7.215

7.22

7.225

7.23

7.235

7.24

7.245

7.25

MEAN

Healthy

Blocked Inlet

Seal Leakage

Worn Bearing

Blocked Inlet &

Worn Bearing

Seal Leakage

&

Worn BearingSeal Leakage

&

Blocked Inlet

Fault type

Overlap

38

MEAN VARIANCE

KURTOSIS

Healthy

Blocked Inlet

Seal

LeakageWorn Bearing

Blocked Inlet &

Worn Bearing

Seal Leakage

&

Worn BearingSeal Leakage

&

Blocked Inlet

7.21

7.215

7.22

7.225

7.23

7.235

7.24

7.245

7.25

SKEWNESS

-2

-1.8

-1.6

-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

1

2

3

4

5

6

10-3

2

3

4

5

6

7

Fault type Fault type

Fault type Fault type

Outlier

Outlier

39

Healthy

Blocked Inlet

Seal Leakage

Worn Bearing



Seal Leakage, Blocked Inlet

Mean

Va

ria

nc

e

41


Time-domainfeatures



Mean

Standard deviation

Skewness

Root-mean square

. . .

Kurtosis

Learn more about Condition Indicators

https://www.mathworks.com/help/predmaint/identify-condition-indicators.html

42

Three different

vibration sources:

- Bearing

- Motor shaft

- Disc

• In time-domain, we observe

the combined effect of

different sources of vibration.

DiscBearing Motor

shaft

DiscBearing

Motor

shaft

Machine with rotating

components

Time

Am

plit

ud

eA

mp

litu

de

Frequency

• Using frequency-domain

analysis, we can

distinguish different

sources of vibration.

NominalFaulty

43

Frequency-domain

features:

• Peaks

• Peak frequencies

0 500 1000 1500 2000 2500 3000 3500 4000 4500 50000

0.005

0.01

0.015

0.02

Blocked Inlet

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

Frequency (Cycles/min)

0

0.005

0.01

0.015

0.02


0 500 1000 1500 2000 2500 3000 3500 4000 4500 50000

0.005

0.01

0.015

0.02

Seal Leakage

0 500 1000 1500 2000 2500 3000 3500 4000 4500 50000

0.005

0.01

0.015

0.02

Seal Leakage, Blocked Inlet

0 500 1000 1500 2000 2500 3000 3500 4000 4500 50000

0.005

0.01

0.015

0.02


0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

Frequency (Cycles/min)

0

0.005

0.01

0.015

0.02

Worn Bearing

Ma

gn

itu

de

Ma

gn

itu

de

Ma

gn

itu

de

44

Time-domain Frequency-domain

46


Time-domainfeatures



Power bandwidth

Peak values

Peak frequencies

Mean frequency

Spectral entropy

Spectral kurtosis

Harmonics. . .

. . .

Mean

Standard deviation

Skewness

Root-mean square

. . .

Kurtosis

Learn more about Condition Indicators

https://www.mathworks.com/help/predmaint/identify-condition-indicators.html

47

Diagnostic Feature Designer AppPredictive Maintenance Toolbox R2019a

▪ Extract, visualize, and rank

features from sensor data

▪ Use both statistical and

dynamic modeling methods

▪ Work with out-of-memory data

▪ Explore and discover

techniques without writing

MATLAB code

49


Preprocess

Data

Identify

ConditionIndicators

Train

Model

Deploy &

Integrate

Acquire Data

Sensor Data

Generated

Data

50

MODEL

PREDICTION

Machine Learning Workflow

Train: Iterate till you find the best model using historical data

Predict: Integrate trained models into applications

MODELSUPERVISED

LEARNING

CLASSIFICATION

REGRESSION

PREPROCESS

DATA

SUMMARY

STATISTICS

PCAFILTERS

CLUSTER

ANALYSIS

HISTORICAL

DATA

PREPROCESS

DATA

SUMMARY

STATISTICS

PCAFILTERS

CLUSTER

ANALYSIS

NEW

DATA

Machine learning uses data and produces a program to perform a task

51

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

Condition

Indicator

Machine Learning: Types

Regression

53

Video showing App in action

54

Summary: Develop Predictive Maintenance Algorithm:

Use case 1: Fault Classification

Classification Learner App

Diagnostic Feature

Designer

55

Remaining

Useful Life

Estimation

Develop Predictive Maintenance Algorithm: Use case 2

How much longer

can I operate my

machine ?

Estimate

remaining useful life

Time

Machine

health

Current

condition

Remaining useful life

xx x

x

Predictive

maintenance

algorithm


56

Time

Machine deterioration

profile

Remaining useful life (RUL)

Current condition

Failure condition

[ Number of days ]

[ Miles ]

[ Cycles ]

…

Co

nd

itio

n in

dic

ato

r

What is RUL?

57

Healthy state Failure

RUL Estimator Models

Similarity model Degradation model

Survival model

Safetythreshold



58

RUL Methods and when to use themRequirement: Need to know what constitutes failure data

60

Acquire

Data


Preprocess

Data

Identify

ConditionIndicators

Train

ModelDeploy &Integrate

Edge deviceCloud

61

Deploy & Integrate analytics using MATLAB:

MATLAB

Runtime

C, C++ HDL PLC

Embedded Hardware

C/C++ ++ExcelAdd-in Java

Hadoop/

Spark.NET

MATLABProduction

Server

StandaloneApplication

Enterprise Systems

Python

62

Feature Extraction Algorithm at the Edge

Asset w. smart

sensors

▪ 1 day ~ 1.3 GB

▪ 20 sensors/pump ~26 GB/day

▪ 3 pumps ~ 78 GB/day

Pump flow sensor 1 sec ~ 1000 samples ~16kB

Challenge:

Data transmission cost is pretty high

Solution:

Extract only relevant information and send it to predictive model

63

Video showing Codegen with MATLAB Coder

64

Video showing Codegen with MATLAB Coder

65

Code Deployment for Machine Learning

▪ SVM Classification

▪ Linear Classification

▪ Linear Regression

▪ Generalized Linear Regr.

▪ Decision trees

▪ Ensembles for Class.

▪ Ensembles for Regr.

▪ SVM Regression

▪ KNN Classification

▪ Gaussian Process Regr.

▪ Discriminant Analysis

▪ Non-tree Ensembles

▪ KNN with kd-tree

Deploy trained models as standalone C/C++ code

66

What do your end users want?

Flexible Deployment

▪ Maintenance needs simple, quick

information

– Hand held devices, Alarms

▪ Operations needs a birds-eye view

– Integration with IT & OT systems

▪ Customers expect easy to digest

information

– Automated reports

Dashboards &

Hand held DevicesFleet & Inventory Analysis

Azure

Blob

Azure

SQL

Data Sources Analytics Platforms

Azure

IoT Hub

AWS

Kinesis

Streaming Data

67

Agenda:




68

MathWorks can help you get started TODAY

▪ Examples

▪ Documentation

▪ Tutorials & Workshops

▪ Consulting

▪ Tech Talk Series

https://www.mathworks.com/help/predmaint/examples.html

https://www.mathworks.com/help/predmaint/index.html

https://www.mathworks.com/services/consulting/proven-solutions/predictive-maintenance.html

https://www.mathworks.com/videos/predictive-maintenance-part-1-introduction-1545827554336.html

69

Training ServicesExploit the full potential of MathWorks products

Flexible delivery options:

▪ Public training available in several cities

▪ Onsite training with standard or

customized courses

▪ Web-based training with live, interactive

instructor-led courses

More than 48 course offerings:

▪ Introductory and intermediate training on MATLAB, Simulink,

Stateflow, code generation, and Polyspace products

▪ Specialized courses in control design, signal processing, parallel computing,

code generation, communications, financial analysis,

and other areas

www.mathworks.in/training

http://www.mathworks.com/training

70

Machine Learning with MATLAB

▪ This two-day course focuses on data analytics and machine learning techniques in MATLAB. The

course demonstrates the use of unsupervised learning to discover features in large data sets and

supervised learning to build predictive models. Topics include:

▪ Organizing and preprocessing data

▪ Clustering data

▪ Creating classification and regression models

▪ Interpreting and evaluating models

▪ Simplifying data sets

▪ Using ensembles to improve model performance

71

Statistical Methods in MATLAB

After this 2-day course you will be able to:

Import, visualize, explore, and model data

▪ Fit probability distributions to data, and perform

hypothesis tests

▪ Develop and fit regression models to data

▪ Generate random numbers

and perform simulations

72

Summary: Why MATLAB for Predictive Maintenance?

▪ Dedicated toolbox for data preprocessing and feature extraction and developing

predictive models

▪ Apps to make the task simple

▪ Support for taking these algorithm to edge and enterprise

▪ Get started quickly…examples, training and consulting

73

▪ Scan this QR Code or log onto link below

(link also sent to your phone and email)

▪ http://bit.ly/expo19-feedback

▪ Enter the registration id number displayed

on your badge

▪ Provide feedback for this session

Please provide feedback for this block of sessions

http://bit.ly/expo19-feedback

74© 2015 The MathWorks, Inc.

Speaker Details:

Email: [email protected]

LinkedIn: https://www.linkedin.com/in/amit-doshi/

mailto:[email protected]

https://www.linkedin.com/in/amit-doshi/

Predictive Maintenance with MATLAB · Pump Health Monitoring System Spectral analysis and filtering...

Documents

Transcript of Predictive Maintenance with MATLAB · Pump Health Monitoring System Spectral analysis and filtering...