Semantic Modelling of Big Industrial Data for Predictive Maintenance – Dimitris Kiritsis, EPFL

The degradation of functional components of engineering assets represents the main need for predictive maintenance. Degradation is also a major sources of product defects. The Industrial Internet of Things and associated AI, Cloud and Edge Technologies allow today for an efficient Data Driven approach to optimise the lifecycle of engineering assets and get most of their value. There are two main technological approaches to do it: Predictive Maintenance and Zero Defect Manufacturing. In this session we will present up-to-date research developments on the proposed topic obtained in the framework H2020 projects Z-BRE4K (https://www.z-bre4k.eu/) and BOOST 4.0 (http://boost40.eu/).

Key Takeaways

  • Capturing the Meaning of Big Industrial Data
  • Industrial Ontologies for Predictive Maintenance (https://www.industrialontologies.org/)
  • Ontology Based Software Architectures for Predictive Maintenance

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