Downloaded 1,217 times
Uploaded byPAPIs.io
[Tutorial] building machine learning models for predictive maintenance applications - Yan Zhang
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.
More Related Content
Similar to [Tutorial] building machine learning models for predictive maintenance applications - Yan Zhang
[Tutorial] building machine learning models for predictive maintenance applications - Yan Zhang
- 2. Sample Scenario Predictive maintenancein 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 2
- 3.
- 4.
- 5.
- 6. 6 Predictive Maintenance inIoT 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 54 3 7 5 3 5 3 5 5 9 0 6 3 5 2 0 0
- 8.
- 9.
- 10.
- 11. Sample training data ~20krows, 100 unique engine id Sample testing data ~13k rows, 100 unique engine id Sample ground truth data 100 rows 11
- 12.
- 13. Sample training data ~20krows, 100 unique engine id Sample testing data ~13k rows, 100 unique engine id Sample ground truth data 100 rows 13 RUL label1 label2 ?
- 14. 14 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
- 15.
- 16. 16 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 “predictivemaintenance”)
- 18. http://azure.com/ml free tier &standard tier 18 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
- 19. 19 Step #2B Train andevaluate 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
- 20.
- 21.
- 22. 22 Step #2B Train andevaluate 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
- 23. 23 Decision Forest Regression BoostedDecision Tree Regression Poisson Regression Neural Network Regression
- 24. 24 Saved Transform Web serviceinput/output
- 25.
- 27. Desktop app Azure MLModel (Deployed Web Service) ML predictions consumed through the RRS web service interfaceData input 27
- 28.
- 29. using three machinelearning models: regression, binary classification, multi-class classification Introduced how to build end-to-end data pipeline with Azure ML 29
- 30. Microsoft AzureMachine 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