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

TL;DR

This paper introduces a process mining-based approach for modeling, simulating, and diagnosing faults in cyber-physical systems using interpretable stochastic Petri nets, improving fault detection accuracy and interpretability.

Contribution

The paper presents a novel unsupervised method combining process mining and Petri nets for fault diagnosis in CPSs, addressing interpretability and data utilization issues.

Findings

Achieves up to 98.93% F1 score in fault classification

Demonstrates effective modeling with 0.676 arc-degree simplicity

Maintains low conformance checking time of 0.020 seconds

Abstract

Cyber-Physical Systems (CPSs) tightly interconnect digital and physical operations within production environments, enabling real-time monitoring, control, optimization, and autonomous decision-making that directly enhance manufacturing processes and productivity. The inherent complexity of these systems can lead to faults that require robust and interpretable diagnoses to maintain system dependability and operational efficiency. However, manual modeling of faulty behaviors requires extensive domain expertise and cannot leverage the low-level sensor data of the CPS. Furthermore, although powerful, deep learning-based techniques produce black-box diagnostics that lack interpretability, limiting their practical adoption. To address these challenges, we set forth a method that performs unsupervised characterization of system states and state transitions from low-level sensor data, uses several process mining techniques to model faults through interpretable stochastic Petri nets, simulates such Petri nets for a comprehensive understanding of system behavior under faulty conditions, and performs Petri net-based fault diagnosis. The method is applied to the Robotic Arm Dataset (RoAD), a benchmark collected from a robotic arm deployed in a scale-replica smart manufacturing assembly line. The application to RoAD demonstrates the method's effectiveness in modeling, simulating, and classifying faulty behaviors in CPSs. The modeling results demonstrate that our method achieves a satisfactory interpretability-simulation accuracy trade-off with up to 0.676 arc-degree simplicity, 0.395 R^2, and 0.088 RMSE. In addition, the fault identification results show that the method achieves an F1 score of up to 98.925%, while maintaining a low conformance checking time of 0.020 seconds, which competes with other deep learning-based methods.