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.