Simulation-Based Diagnosis for Cyber-Physical Systems - A General Approach and Case Study on a Dual Three-Phase E-Machine

Kaufmann, David; Kozovský, Matúš; Wotawa, Franz

Simulation-Based Diagnosis for Cyber-Physical Systems - A General Approach and Case Study on a Dual Three-Phase E-Machine

Files

OASIcs.DX.2024.18.pdf(16.88 MB)

Date

2024-11-26

Authors

Kaufmann, David

Kozovský, Matúš

Wotawa, Franz

ORCID

0000-0002-1547-1003

Publisher

Schloss Dagstuhl – Leibniz-Zentrum für Informatik

Altmetrics

Abstract

This paper presents a simulation-based approach for fault diagnosis in cyber-physical systems. We utilize simulation models to generate training data for machine learning classifiers to detect faults and identify the root cause. The presented processing pipeline includes simulation model validation, training data generation, data preprocessing, and the implementation of a diagnosis method. A case study with a dual three-phase e-machine highlights the results and challenges of the simulation-based diagnosis approach. The e-machine simulation model provides a complex and robust system representation, including the capability to inject inter-turn short-circuit faults. The introduced validation procedures of the simulation model revealed limitations in signal similarity and distinguishability compared to real system behavior. Based on the discovered limitations, the overall best results are achieved by applying an Autoencoder model for anomaly detection, followed by a Random Forest classifier to identify the specific anomalies. Further, the focus is on identifying the affected e-machine phase rather than the exact number of faulty winding turns. The paper shows the challenges when applying a simulation-based diagnosis approach to time-series data and underlines the required analysis of simulation models. In addition, the flexible adaption in the diagnosis strategies enhances the efficient utilization of cyber-physical system models in fault diagnosis and root cause identification.

Keywords

Cyber-Physical System, Fault diagnosis, Root cause analysis, Simulation-Based Diagnosis, Machine Learning, Artificial Neural Networks

Citation

OpenAccess Series in Informatics (OASIcs). 2024, vol. 125, issue 18, p. 18:1-18:21.
https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2024.18

Document type

Peer-reviewed

Document version

Published version

Language of document

en

Document licence

Creative Commons Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/