Process mining-driven modeling and simulation to enhance fault diagnosis in cyber-physical systems
Journal:
arXiv
Published Date:
Jun 26, 2025
Abstract
Fault diagnosis in Cyber-Physical Systems (CPSs) is essential for ensuring
system dependability and operational efficiency by accurately detecting
anomalies and identifying their root causes. However, the manual modeling of
faulty behaviors often demands extensive domain expertise and produces models
that are complex, error-prone, and difficult to interpret. To address this
challenge, we present a novel unsupervised fault diagnosis methodology that
integrates collective anomaly detection in multivariate time series, process
mining, and stochastic simulation. Initially, collective anomalies are detected
from low-level sensor data using multivariate time-series analysis. These
anomalies are then transformed into structured event logs, enabling the
discovery of interpretable process models through process mining. By
incorporating timing distributions into the extracted Petri nets, the approach
supports stochastic simulation of faulty behaviors, thereby enhancing root
cause analysis and behavioral understanding. The methodology is validated using
the Robotic Arm Dataset (RoAD), a widely recognized benchmark in smart
manufacturing. Experimental results demonstrate its effectiveness in modeling,
simulating, and classifying faulty behaviors in CPSs. This enables the creation
of comprehensive fault dictionaries that support predictive maintenance and the
development of digital twins for industrial environments.
