Soleimani, Morteza, Shahbeigi, Sepeedeh and Nasr Esfahani, Mohammad orcid.org/0000-0002-6973-2205 (2024) A Bayesian network development methodology for fault analysis; case study of the automotive aftertreatment system. Mechanical Systems and Signal Processing. 111459. ISSN 1096-1216
Abstract
This paper proposes a structured methodology for generating a Bayesian network (BN) structure for an engineered system and investigates the impact of integrating engineering analysis with a data-driven methodology for fault analysis. The approach differs from the state of the art by using different initial information to build the BN structure. This method identifies the cause-and-effect relationships in a system by Causal Loop Diagram (CLD) and based on that, builds the Bayesian Network structure for the system. One of the challenges in identifying the root cause for a fault is to determine the way in which the related variable causes the fault. To deal with this challenge, the proposed methodology exploits Dynamic Fault Tree Analysis (DFTA), CLD and the correlation between variables. To demonstrate and evaluate the effectiveness of the presented method, it is implemented on the data-driven methodology applied to the automotive Selective Catalytic Reduction (SCR) system and the obtained results have been compared and discussed. The proposed methodology offers a comprehensive approach to build a BN structure for an engineered system, which can enhance the system's reliability analysis.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Author(s). |
Keywords: | Bayesian network,Causal loop diagram,Dynamic fault tree analysis,Root cause identification,Selective Catalytic Reduction (SCR) |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Electronic Engineering (York) |
Depositing User: | Pure (York) |
Date Deposited: | 30 Jul 2024 11:10 |
Last Modified: | 25 Dec 2024 00:30 |
Published Version: | https://doi.org/10.1016/j.ymssp.2024.111459 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1016/j.ymssp.2024.111459 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:215510 |
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