[tutorial] building machine learning models for predictive maintenance applications - yan zhang

Sample ScenarioPredictive maintenance in IoT applications vs. traditional predictive maintenance concepts

Predictive problem: “When an in-service machine will fail?”

Machine learning approach

Problem formulation

Use caseInput data – publicly available aircraft engine run-to-failure data

Data labeling and feature engineering

Tools to build end-to-end solution from data to web serviceAzure ML

Predictive Maintenance Template in Azure ML

Demo: desktop app to predict machine’s remaining useful life

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DATA

Business apps

Custom apps

Sensors and devices

ACTION

People

Automated Systems

Data Science Process

DATA

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DATADesktop app

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Predictive Maintenance in IoT Traditional Predicative Maintenance

GoalImprove production and/or maintenance

efficiency

Ensure the reliability of machine

operation

DataData stream (time varying features), Multiple

data sourcesVery limited time varying features

Scope Component level, System level Parts level

Approach Data driven Model driven

Tasks

Failure prediction, fault/failure detection &

diagnosis, maintenance actions

recommendation, etc. Essentially any task

that improves production/maintenance

efficiency

Failure prediction (prognosis),

fault/failure detection & diagnosis

(diagnosis)

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1 1 5 4 3

7 5 3 5 3

5 5 9 0 6

3 5 2 0 0

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http://ti.arc.nasa.gov/tech/dash/pcoe/prognostic-data-repository/

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Sample training data~20k rows,

100 unique engine id

Sample testing data~13k rows,

100 unique engine id

Sample ground truth data100 rows

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Sample training data~20k rows,

100 unique engine id

Sample testing data~13k rows,

100 unique engine id

Sample ground truth data100 rows

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RUL label1 label2

?

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id cycle … RUL label1 label2

1 1 191 0 0

1 2 190 0 0

1 3 189 0 0

1 4 188 0 0

… … … …

1 160 32 0 0

1 161 31 0 0

1 162 30 1 1

1 163 29 1 1

1 164 28 1 1

1 165 27 1 1

1 166 26 1 1

1 167 25 1 1

1 168 24 1 1

1 169 23 1 1

1 170 22 1 1

1 171 21 1 1

1 172 20 1 1

1 173 19 1 1

1 174 18 1 1

1 175 17 1 1

1 176 16 1 1

1 177 15 1 2

1 178 14 1 2

1 179 13 1 2

1 180 12 1 2

1 181 11 1 2

1 182 10 1 2

1 183 9 1 2

1 184 8 1 2

1 185 7 1 2

1 186 6 1 2

1 187 5 1 2

1 188 4 1 2

1 189 3 1 2

1 190 2 1 2

1 191 1 1 2

1 192 0 1 2

Predefined window size

for classification models

w1 = 30

w0 = 15

w1

w0

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a1 a2 … a21 sd1 sd2 … sd21 RUL label1 label2

Other potential features: change from initial value, velocity of change, frequency count over a

predefined threshold

http://gallery.azureml.net (search “predictive maintenance”)

http://azure.com/mlfree tier & standard tier

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Accessible through a web browser, no software to install

Best ML algorithms

Extensible, support for R & Python

Collaborative work with anyone, anywhere via Azure workspace

Visual composition with end2end support for data science workflow

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Step #2B

Train and evaluate binary

classification models

Step #1 Data preparation and

feature engineering

Step #2A

Train and evaluate regression

models

Step #3A

Deploy web service with a

regression model

Step #3B

Deploy web service with a

binary classification model

Step #3C

Deploy web service with a

multi-class classification

model

Step #2C

Train and evaluate multi-class

classification models

Step 1 Step 2 Step 3

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Step #2B

Train and evaluate

binary classification

models

Step #1 Data

preparation and

feature engineering

Step #2A

Train and evaluate

regression models

Step #3A

Deploy web service

with a regression

model

Step #3B

Deploy web service

with a binary

classification model

Step #3C

Deploy web service

with a multi-class

classification model

Step #2C

Train and evaluate

multi-class

classification

models

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Decision Forest Regression

Boosted Decision Tree Regression

Poisson Regression

Neural Network Regression

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Saved Transform

Web service input/output

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Desktop app

Azure ML Model (Deployed Web

Service)

ML predictions consumed through the RRS web service interfaceData input

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DATA INTELLIGENCE ACTION

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using three machine learning models: regression, binary classification, multi-class classification

Introduced how to build end-to-end data pipeline with Azure ML

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Microsoft Azure Machine Learninghttp://azure.com/ml

http://gallery.azureml.net (search “predictive maintenance”)

Register for the Cortana Analytics Workshop hosted in Redmond on September 10-11, 2015. https://analyticsworkshop.azurewebsites.net