Automation Of Maintenance Prediction For Rotating Assets – Renato Neves & Rerngvit Yanggratoke

Combient is a joint venture with a mission to accelerate digital transformation within the companies participating in our federated collaboration platform, with companies representing some of the largest traditional enterprises in Sweden and Finland. A significant part of this work has involved solving advanced analytics projects related to predictive maintenance. In this session, we will share challenges, methods and preliminary results from an ongoing project that Combient is carrying out together with SKF. We will describe a machine learning approach for determining the health state of rotating systems. The results are based on vibration data collected from 30,000 machines since 2001.

Learning points:

  • Key challenges with building models for real-world data
  • What can different industries learn from each other?

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