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Machine Learning Impact on IoT - Part 2

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How to Introduce Machine Learning in a IoT scenario:
- Map of ML algorithms and uses cases
- Case Study: Jet engines predictive maintenance.

Published in: Data & Analytics
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Machine Learning Impact on IoT - Part 2

  1. 1. • Contact us for 1 free consultation: giuseppe@valueamplify.com • Twitter: @giuseppeHighTec • Linkedin: www.linkedin.com/in/giuseppemascarella
  2. 2. What Is Machine Learning for IoT?
  3. 3. IoT Predictive Maintenance Concepts 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)
  4. 4. Example Predictive Maintenance Use Cases
  5. 5. IoT Predictive Maintenance – Qantas Airways ~24,000 sensors Qantas A380 Fleet Technical Delays 12 $65M+ per A380 50% Technical Delays 400-700 Fault/warning messages/day have potential for predictive modelling Develop ML model (MATLAB) alongside local university Optimise code Reduce runtime Develop user web front endBuild evaluation module Refine model parameters Configure model in AML PM template Evaluate & refine model data & parameters Visualize results in Power BI Months /year Orchestrate data pipeline in Azure Data Factory Source: www.microsoft.com
  6. 6. Stay ahead of the curve with Cortana Intelligence Suite Business apps Custom apps Sensors and devices People Automated systems Data Intelligence Cortana Intelligence Action Apps
  7. 7. The IoT Ecosystem Around ML Intelligence Dashboards & Visualizations Information Management Big Data Stores Machine Learning and Analytics CortanaEvent Hubs HDInsight (Hadoop and Spark) Stream Analytics Data Intelligence Action People Automated Systems Apps Web Mobile Bots Bot Framework SQL Data WarehouseData Catalog Data Lake Analytics Data Factory Machine Learning Data Lake Store Cognitive Services Power BI Data Sources Apps Sensors and devices Data
  8. 8. Source: www.microsoft.com
  9. 9. Define scope/Preparation/Source/Labeling/Feature Engineering
  10. 10. Scope Question is sharp. Data measures what they care about. Data is connected. Data is accurate. A lot of data. The better the raw materials, the better the product. E.g. Predict whether component X will fail in the next Y days; clear path of action with answer E.g. Identifiers at the level they are predicting E.g. Will be difficult to predict failure accurately with few examples E.g. Failures are really failures, human labels on root causes; domain knowledge translated into process E.g. Machine information linkable to usage information
  11. 11. Data Sources The failure history of a machine or component within the machine. The repair history of a machine, e.g. previous maintenance records, components replaced, maintenance activities performed. Maintenance types. The operation conditions of a machine, e.g. data collected from sensors. FAILURE HISTORY REPAIR HISTORY MACHINECONDITIONS The features of machine or components, e.g. production date, technical specifications. Environmental features that may influence a machine’s performance, e.g. location, temperature, other interactions. The attributes of the operator who uses the machine, e.g. driver. MACHINE FEATURES OPERATING CONDITIONS OPERATORATTRIBUTES
  12. 12. Sample training data ~20k rows, 100 unique engine id Sample testing data ~13k rows, 100 unique engine id Sample ground truth data 100 rows Please refer to following link of doc for Data description section https://gallery.cortanaintelligence.com/Experiment/df7c518dcba 7407fb855377339d6589f
  13. 13. Classes •Regression models: How many more cycles an in- service engine will last before it fails? •Binary classification: Is this engine going to fail within w1 cycles? •Multi-class classification: Is this engine going to fail within the window [1, w0] cycles or to fail within the window [w0+1, w1] cycles, or it will not fail within w1 cycles?
  14. 14. Feature Engineering The process of creating features that provide better or additional predictive power to the learning algorithm. a1 a2 … a21 sd1 sd2 … sd21 RUL label1 label2 40+ engineered features
  15. 15. Data Labeling How far ahead of time the alert of failure should trigger before the actual failure event.
  16. 16. Feature Engineering 1. Selected raw features 2. Aggregate features
  17. 17. Modelling Techniques Predict failures within a future period of time BINARY CLASSIFICATION Predict failures with their causes within a future time period. Predict remaining useful life within ranges of future periods MULTICLASSCLASSIFICATION Predict remaining useful life, the amount of time before the next failure REGRESSION Identify change in normal trends to find anomalies ANOMALYDETECTION
  18. 18. Data Labeling 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 Regression Binary classification Multi-class classification
  19. 19. Evaluation • Time dependent split • Train in the past, validate in the future • Class imbalance • A few failure events • sampling, cost-sensitive learning • Metrics • Recall, Precision, F1 • Random Guess, Weighted Guess
  20. 20. “Most IoT data are not used currently… the data that are used today are mostly for anomaly detection and control, not optimization and prediction, which provide the greatest value.”1
  21. 21. Acknowledgements • We utilized the following publically available data to help us generate realistic data for the demo shown. We received assistance in creating this solution as a result of this repository and the donators of the data: “A. Saxena and K. Goebel (2008). "PHM08 Challenge Data Set", NASA Ames Prognostics Data Repository (http://ti.arc.nasa.gov/project/prognostic-data-repository), NASA Ames Research Center, Moffett Field, CA.” • McKinskey Global Institute, The Internet of Things: Mapping the Value beyond the hype • Microsoft Cortana Gallery Experiments
  22. 22. Learn and try yourself! • Learn from Cortana Analytics Gallery • Solution package material – deploy by hand to learn here • Try Cortana Analytics Solution Template – Predictive Maintenance for Aerospace in private preview • Try Azure IOT pre-configured solution for Predictive Maintenance • Read the Predictive Maintenance Playbook for more details on how to approach these problems • Run the Modelling Guide R Notebook for a DS walk- through
  23. 23. • Contact us for 1 free consultation: giuseppe@valueamplify.com • Twitter: @giuseppeHighTec • Linkedin: www.linkedin.com/in/giuseppemascarella

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