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6 Reasons Why You Should Use Machine Learning For Predictive Maintenance


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Learn the top reasons to use predictive analytics, sensor data and machine learning to do more intelligent maintenance.

Published in: Software

6 Reasons Why You Should Use Machine Learning For Predictive Maintenance

  1. 1. Reasons Why You Should Use Machine Learning for Predictive Maintenance6 XMPro
  2. 2. 1. Only do maintenance when you need to XMPro
  3. 3. According to NASA, failure patterns that are age related only apply to 18% of assets. Using asset condition data and machine learning algorithms to predict failures will allow you to do maintenance when it matters.
  4. 4. 2. Stop wasting money on unnecessary work XMPro
  5. 5. The combined cost of excess maintenance and lost productivity in the US has been estimated at $740B.
  6. 6. 3. Keep your assets in production XMPro
  7. 7. Using predictive instead of preventative maintenance means no more shutting down assets that could be working.
  8. 8. 4. Scale without needing to hire more experienced personnel XMPro
  9. 9. Machine learning is easy to scale across different types of assets, unlike some condition monitoring techniques that are expensive and require specialists for data analysis.
  10. 10. 5. Reduce unplanned downtime by resolving issues prior to failure XMPro
  11. 11. Integrating machine learning predictions with your BPM or EAM system will help engineers respond to imminent failures faster.
  12. 12. 6. Help engineers make smarter decisions XMPro
  13. 13. Combining asset failure predictions with process mining data gives you insight into which actions create the best outcomes. A machine learning algorithm can use this data to make recommendations to engineers on the best action to take next.
  14. 14. Learn How To Get Started With Machine Learning For Predictive Maintenance Sources: 1. 2. DOWNLOAD THE EBOOK