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Practical Uses of Machine Learning in Industrial Automation

This comprehensive guide covers the practical applications of machine learning in industrial automation, including predictive maintenance, quality control, demand forecasting, and training industrial robots. Learn about the implementation options, main branches of machine learning, algorithms, and steps to start a machine learning project.

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Practical Uses of Machine Learning in Industrial Automation

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  1. Practical Uses of Machine Learning and Ignition Kathy Applebaum Co-Director of Sales Engineering / Inductive Automation Kevin McClusky Senior Software Engineer / Inductive Automation

  2. Machine Learning Practical uses of machine learning What is machine learning Before you start Steps to a machine learning project Conclusion Resources to learn more Agenda

  3. Practical Uses Predictive maintenance Quality control Demand forecasting Training industrial robots Self-driving vehicles Machine Learning in Industrial Automation

  4. Implementation Options More Automated More Control

  5. Implementation Options More Automated More Control

  6. What is Machine Learning? Analytics Machine Learning Artificial Intelligence Three main branches

  7. What is Machine Learning? Analytics • Knowledge discovery Three main branches

  8. What is Machine Learning? Analytics • Knowledge discovery • Descriptive Three main branches

  9. What is Machine Learning? Analytics • Knowledge discovery • Descriptive • Diagnostic Three main branches

  10. What is Machine Learning? Analytics • Knowledge discovery • Descriptive • Diagnostic • Predictive Three main branches

  11. What is Machine Learning? Analytics • Knowledge discovery • Descriptive • Diagnostic • Predictive • Prescriptive Three main branches

  12. What is Machine Learning? Analytics • Knowledge discovery Machine Learning • Learn and improve from experience Three main branches

  13. What is Machine Learning? Analytics • Knowledge discovery Machine Learning • Learn and improve from experience Artificial Intelligence • Tasks that simulate human intelligence Three main branches

  14. What is Machine Learning? Demo

  15. What is Machine Learning? Classifiers – predict a category Regression – predict a value Main types of machine learning

  16. What is Machine Learning? Algorithms

  17. What is Machine Learning? K-means - Clustering

  18. What is Machine Learning? K-means - Clustering Categorization for Defect Analysis

  19. What is Machine Learning? Decision trees Should I bring an umbrella? Cloudy? Yes Rain in forecast? Yes No

  20. What is Machine Learning? Decision trees Predictive Maintenance Should I bring an umbrella? Cloudy? Yes Rain in forecast? Yes No

  21. What is Machine Learning? Regression analysis

  22. What is Machine Learning? Regression analysis Process Tuning Production Forecasting

  23. What is Machine Learning? Neural Networks

  24. What is Machine Learning? Neural Networks Process Simplification Vision Systems

  25. Getting Started What things should we have before starting a machine learning project? Prerequisites

  26. Getting Started Data Prerequisites

  27. Getting Started Data • Sources of data Prerequisites

  28. Getting Started Data • Sources of data • Quality data Prerequisites

  29. Getting Started Data • Sources of data • Quality data • Labeled vs. unlabeled data Prerequisites

  30. Getting Started Data Statistics knowledge Prerequisites

  31. Getting Started Data Statistics knowledge • Sampling techniques Prerequisites

  32. Getting Started Data Statistics knowledge • Sampling techniques • Correlation vs. causation Prerequisites

  33. Getting Started Data Statistics knowledge • Sampling techniques • Correlation vs. causation • How good are your results? Prerequisites

  34. Getting Started Data Statistics knowledge Domain knowledge Prerequisites

  35. Getting Started Data Statistics knowledge Domain knowledge • In-depth knowledge about your process Prerequisites

  36. Getting Started Data Statistics knowledge Domain knowledge • In-depth knowledge about your process • Know what types of data are promising Prerequisites

  37. Getting Started Data Statistics knowledge Domain knowledge • In-depth knowledge about your process • Know what types of data are promising • Know when results don’t make sense Prerequisites

  38. Machine Learning Steps Pick a question to answer

  39. Machine Learning Steps Pick a question to answer • High value vs. easy

  40. Machine Learning Steps Pick a question to answer • High value vs. easy • Cost function

  41. Machine Learning Steps Pick a question to answer Use domain knowledge

  42. Machine Learning Steps Pick a question to answer Use domain knowledge • Pick useful data

  43. Machine Learning Steps Pick a question to answer Use domain knowledge • Pick useful data • Acquire missing data

  44. Machine Learning Steps Pick a question to answer Use domain knowledge • Pick useful data • Acquire missing data • Quality data

  45. Machine Learning Steps Pick a question to answer Use domain knowledge • Pick useful data • Acquire missing data • Quality data • Dependent variables

  46. Machine Learning Steps Pick a question to answer Use domain knowledge ETL

  47. Machine Learning Steps Pick a question to answer Use domain knowledge ETL • Automate steps

  48. Machine Learning Steps Pick a question to answer Use domain knowledge ETL • Automate steps • Acquire new data automatically

  49. Machine Learning Steps Pick a question to answer Use domain knowledge ETL • Automate steps • Acquire new data automatically • Clean up data

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