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Image Credit: Arxiv

Process mining-driven modeling and simulation to enhance fault diagnosis in cyber-physical systems

  • A novel unsupervised fault diagnosis methodology has been developed to enhance fault diagnosis in Cyber-Physical Systems (CPSs) by integrating collective anomaly detection, process mining, and stochastic simulation.
  • The methodology starts by detecting collective anomalies in sensor data through multivariate time-series analysis, then transforms them into structured event logs for process mining to create interpretable process models.
  • Incorporating timing distributions into the extracted Petri nets allows for stochastic simulation of faulty behaviors, improving root cause analysis and behavioral understanding in CPSs.
  • Experimental validation using the Robotic Arm Dataset showed the methodology's effectiveness in modeling, simulating, and classifying faulty behaviors, facilitating the development of fault dictionaries for predictive maintenance in industrial settings.

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