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

arXiv: https://arxiv.org/abs/2506.21502 · HTML: https://mlsanity.com/paper/2506.21502/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 69 references that are in the map.
- First posted on arXiv 26 Jun 2025.

## Builds on (most cited first, 11 of 11)

- [LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection](https://mlsanity.com/paper/1607.00148/) (2016)
- [Deep Learning for Time Series Anomaly Detection: A Survey](https://mlsanity.com/paper/2211.05244/) (2022)
- [Trustworthy AI: A Computational Perspective](https://mlsanity.com/paper/2107.06641/) (2021)
- [IoT Anomaly Detection Methods and Applications: A Survey](https://mlsanity.com/paper/2207.09092/) (2022)
- [Time-Series Pattern Recognition in Smart Manufacturing Systems: A Literature Review and Ontology](https://mlsanity.com/paper/2301.12495/) (2023)
- [Discovering Relaxed Sound Workflow Nets using Integer Linear Programming](https://mlsanity.com/paper/1703.06733/) (2017)
- [Introducing PetriRL: An Innovative Framework for JSSP Resolution Integrating Petri nets and Event-based Reinforcement Learning](https://mlsanity.com/paper/2402.00046/) (2024)
- [A Bi-LSTM Autoencoder Framework for Anomaly Detection -- A Case Study of a Wind Power Dataset](https://mlsanity.com/paper/2303.09703/) (2023)
- [A Framework for Plant Topology Extraction Using Process Mining and Alarm Data](https://mlsanity.com/paper/2005.01615/) (2020)
- [CSCAD: Correlation Structure-based Collective Anomaly Detection in Complex System](https://mlsanity.com/paper/2105.14476/) (2021)
- [Stochastic Decision Petri Nets](https://mlsanity.com/paper/2303.13344/) (2023)

## Abstract (arXiv)

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.