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

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dc.contributor.authorKaufmann, Davidcs
dc.contributor.authorKozovský, Matúšcs
dc.contributor.authorWotawa, Franzcs
dc.coverage.issue18cs
dc.coverage.volume125cs
dc.date.accessioned2025-04-11T10:56:37Z
dc.date.available2025-04-11T10:56:37Z
dc.date.issued2024-11-26cs
dc.description.abstractThis 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.en
dc.formattextcs
dc.format.extent18:1-18:21cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationOpenAccess Series in Informatics (OASIcs). 2024, vol. 125, issue 18, p. 18:1-18:21.en
dc.identifier.doi10.4230/OASIcs.DX.2024.18cs
dc.identifier.isbn978-3-95977-356-0cs
dc.identifier.issn2190-6807cs
dc.identifier.orcid0000-0002-1547-1003cs
dc.identifier.other193457cs
dc.identifier.researcheridE-2371-2018cs
dc.identifier.urihttps://hdl.handle.net/11012/250886
dc.language.isoencs
dc.publisherSchloss Dagstuhl – Leibniz-Zentrum für Informatikcs
dc.relation.ispartofOpenAccess Series in Informatics (OASIcs)cs
dc.relation.urihttps://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2024.18cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2190-6807/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectCyber-Physical Systemen
dc.subjectFault diagnosisen
dc.subjectRoot cause analysisen
dc.subjectSimulation-Based Diagnosisen
dc.subjectMachine Learningen
dc.subjectArtificial Neural Networksen
dc.titleSimulation-Based Diagnosis for Cyber-Physical Systems - A General Approach and Case Study on a Dual Three-Phase E-Machineen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-193457en
sync.item.dbtypeVAVen
sync.item.insts2025.04.11 12:56:37en
sync.item.modts2025.04.11 12:33:24en
thesis.grantorVysoké učení technické v Brně. Středoevropský technologický institut VUT. Kybernetika a robotikacs
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