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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F24%3APU154787" target="_blank" >RIV/00216305:26620/24:PU154787 - isvavai.cz</a>

Result on the web

<a href="https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2024.18" target="_blank" >https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2024.18</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.4230/OASIcs.DX.2024.18" target="_blank" >10.4230/OASIcs.DX.2024.18</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

20205 - Automation and control systems

Project

<a href="/en/project/9A22002" target="_blank" >9A22002: Artificial Intelligence using Quantum measured Information for realtime distributed systems at the edge</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)

ISBN

978-3-95977-356-0

ISSN

2190-6807

e-ISSN

—

Number of pages

21

Pages from-to

„18:1“-„18:21“

Publisher name

Schloss Dagstuhl – Leibniz-Zentrum für Informatik

Place of publication

neuveden

Event location

Vídeň

Event date

Nov 4, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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