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What is predictive maintenance?


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An introduction into what predictive maintenance is, and what it is not. The emphasis is on Big Data and Data Science. Set your expectations right.

Published in: Data & Analytics

What is predictive maintenance?

  1. 1. What is Predictive Maintenance?
  2. 2. Production depends on machines, and they need to work well.
  3. 3. When a machine breaks, this costs time, money,
  4. 4. …lives.
  5. 5. That is why we preventively maintain them. Better to fix one hundred parts that could break than to have one of them broken at a critical time.
  6. 6. In a perfect world, we could predict which part will break and exactly when.
  7. 7. But in most cases prediction is only stochastic. Out of 1000 items, we know about 10 will break. However, we don’t know which ones.
  8. 8. Predictive (PM): replace the 10 lightbulbs that will actually break. But how to find those 10? Preventive: replace all 1000 lightbulbs. MAINTENANCE
  9. 9. Traditional predictive maintenance using the most versatile sensor in the world: human eye
  10. 10. …and human ear: vibration analysis
  11. 11. Today: Sensors + Big Data + Analytics
  12. 12. One hope from Big Data: Massive amounts of information will substitute for poor quality. Not necessarily! Data science will find the 10 lightbulbs.
  13. 13. Can’t get something out of nothing This is what data science is not.
  14. 14. Prediction is about having correct information. Place relevant sensors at right places. find the crack when it is small
  15. 15. Each PM problem is unique. There is no one-approach-fits-all in PM.
  16. 16. But PM is not only about predicting.
  17. 17. PM is a combination of: Understanding the economics of the industrial process Discovering the proper sources of information Creating a predictive model
  18. 18. Economics: costs increase with PM effort; number of sensors, quality of prediction, etc. as a result savings and profits rise, but not linearly sweet spot PM costs are low, savings are large PM can be more expansive than savings
  19. 19. The person to askSourcesof information
  20. 20. Reserve time for experimenting. You will encounter failures before success.
  21. 21. Finally, a data scientist can create a model. Remember: machine learning is a tool, not a solution.
  22. 22. Putting all aspects of PM together is a collaborative work.
  23. 23. in conclusion
  24. 24. Predictive Maintenance is as much research as it is data handling. It is rather a process and a mindset. It has no single best practice.
  25. 25. Data Science Team Prepared by: Danko Nikolic & Davor Andric