SS06 - Machine learning in condition monitoring and prognostics of engineering systems

Special Session Organized by

Miguel Delgado Prieto, Universitat Politecnica de Catalunya, Spain and Juan Jose Saucedo Dorantes, Universidad Autonoma de Queretaro, Mexico

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The massive digitalization of industrial assets carried out in the last decade, allows now the deployment of industrial cyber-physical systems and IIoT architectures including advanced analytics capabilities to improve decision-making processes. In this regard, machine learning (ML) is being included as part of industrial maintenance procedures to improve anomaly detection, fault diagnosis and remaining useful lifetime prediction over assets. However, two main challenges have to be faced for an effective deployment of such ML-based procedures: first, the scalability, that is, hyperparameters selection procedures and interpretable training methods avoiding overfitted algorithms, and, second the reliability, that is, continuous learning approaches avoiding deviations between the supervision model and the operational drifts during the assets useful life.

Topics under this session include (but not limited to)

  • Data-based industrial processes characterization
  • Automated novelty detection in industrial systems
  • Decision support systems and new information fusion structures
  • Condition monitoring and pattern recognition
  • Transfer learning and incremental learning methodologies
  • Forecasting and remaining useful life estimation
  • Data governance and regulations on machine learning in the industrial maintenance