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Failure Pattern Recognition for Predictive Maintenance with Changepoint Detection and Process Mining

Author

Listed:
  • Alexandros Bousdekis

    (University of Piraeus, Department of Industrial Management and Technology)

  • Georgia Theodoropoulou

    (University of West Attica, Department of Informatics and Computer Engineering)

Abstract

Predictive maintenance aims to enhance equipment availability and minimize unplanned downtime by leveraging both real-time sensor data and data-at-rest from enterprise and operational systems. While recent advances in IoT have enabled high-frequency data collection, a significant volume of legacy data, such as records from Computerized Maintenance Management Systems (CMMS), Enterprise Resource Planning (ERP), and Manufacturing Execution Systems (MES), remains underutilized. Motivated by the P–F curve, this paper proposes a failure pattern recognition approach that integrates Bayesian Online Changepoint Detection (BOCD) with process mining. The approach is validated in a real-world cold rolling process in the steel industry.

Suggested Citation

  • Alexandros Bousdekis & Georgia Theodoropoulou, 2026. "Failure Pattern Recognition for Predictive Maintenance with Changepoint Detection and Process Mining," Lecture Notes in Operations Research,, Springer.
    • Handle: RePEc:spr:lnopch:978-3-032-23493-3_26
    DOI: 10.1007/978-3-032-23493-3_26
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