Post on 16-Apr-2017
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