A fault location strategy based on information fusion and CODAS algorithm under epistemic uncertainty

• Autorzy: Duan Rongxing , Chen Li , He Jiejun , Huang Shujuan

Identyfikatory

DOI

10.17531/ein.2022.3.9

• Języki publikacji: EN

Abstrakty

EN

Application of new technology in modern systems not only substantially improves the performance, but also presents a severe challenge to fault location of these systems. This paper presents a new fault location strategy for maintenance personnel to recover them based on information fusion and improved CODAS algorithm. Firstly, a fault tree is adopted to develop the failure model of a complex system, and failure probability of components is determined by expert evaluations to handle the uncertainty problem. Moreover, a fault tree is converted into an evidence network to obtain importance degrees, which are used to construct a diagnostic decision table together with the risk priority number. Additionally, these results are updated to optimize the maintenance process using sensor information. A novel dynamic location strategy is designed based on interval CODAS algorithm and optimal fault location strategy can be obtained. Finally, a real system is analyzed to demonstrate the feasibility of the proposed maintenance strategy.

Słowa kluczowe

EN

fault tree; expert evaluation; evidence network; information fusion; CODAS algorithm

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2022
• Tom: Vol. 24, no. 3
• Strony: 478--488
• Opis fizyczny: Bibliogr. 38 poz., rys., tab.

Twórcy

autor

Duan Rongxing

• Nanchang University, School of Information Engineering, Nanchang 330031, China

• https://orcid.org/0000-0003-3683-5870

autor

Chen Li

• Nanchang University, School of Information Engineering, Nanchang 330031, China

• https://orcid.org/0000-0002-7156-7210

autor

He Jiejun

• Nanchang University, School of Information Engineering, Nanchang 330031, China

• https://orcid.org/0000-0002-7571-5305

autor

Huang Shujuan

• Nanchang University, School of Information Engineering, Nanchang 330031, China

• https://orcid.org/0000-0002-7540-5534

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).