The document discusses using machine learning for predictive maintenance in IoT applications compared to traditional approaches. It describes using publicly available aircraft engine data to build models in Azure ML to predict remaining useful life. Models tested include regression, binary classification, and multi-class classification. An end-to-end pipeline is demonstrated, from data preparation through deploying web services with different machine learning models.

2. Sample Scenario
Predictive maintenance in IoT applications vs. traditional predictive maintenance concepts
Predictive problem: “When an in-service machine will fail?”
Machine learning approach
Problem formulation
Use case
Input 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 service
Azure ML
Predictive Maintenance Template in Azure ML
Demo: desktop app to predict machine’s remaining useful life
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4. DATA
Business
apps
Custom
apps
Sensors
and
devices
ACTION
People
Automated
Systems
Data Science Process
DATA
4

5. DATA
Desktop app
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6. 6
Predictive Maintenance in IoT Traditional Predicative Maintenance
Goal
Improve production and/or maintenance
efficiency
Ensure the reliability of machine
operation
Data
Data stream (time varying features), Multiple
data sources
Very 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)

7. 7
1 1 5 4 3
7 5 3 5 3
5 5 9 0 6
3 5 2 0 0

8. 8

9. 9

10. http://ti.arc.nasa.gov/tech/dash/pcoe/prognostic-
data-repository/
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11. Sample training data
~20k rows,
100 unique engine id
Sample testing data
~13k rows,
100 unique engine id
Sample ground truth data
100 rows
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13. Sample training data
~20k rows,
100 unique engine id
Sample testing data
~13k rows,
100 unique engine id
Sample ground truth data
100 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

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

18. http://azure.com/ml
free 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

24. 24
Saved Transform
Web service input/output

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27. Desktop app
Azure ML Model
(Deployed Web
Service)
ML predictions
consumed
through the
RRS web service
interfaceData input
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28. DATA INTELLIGENCE ACTION
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29. 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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30.  Microsoft Azure Machine Learning
http://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