A novel reliability estimation method of multi-state system based on structure learning algorithm

Li, Zhifeng; Wang, Zili; Ren, Yi; Yang, Dezhen; Lv, Xing

doi:10.17531/ein.2020.1.20

Artykuł - szczegóły

Tytuł artykułu

A novel reliability estimation method of multi-state system based on structure learning algorithm

Autorzy

Li Zhifeng , Wang Zili , Ren Yi , Yang Dezhen , Lv Xing

Treść / Zawartość

Pełne teksty:

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Identyfikatory

DOI

10.17531/ein.2020.1.20

Warianty tytułu

Nowatorska metoda oceny niezawodności systemów wielostanowych w oparciu o algorytm uczenia struktury

Języki publikacji

Abstrakty

Traditional reliability models, such as fault tree analysis (FTA) and reliability block diagram (RBD), are typically constructed with reference to the function principle graph that is produced by system engineers, which requires substantial time and effort. In addition, the quality and correctness of the models depend on the ability and experience of the engineers and the models are difficult to verify. With the development of data acquisition, data mining and system modeling techniques, the operational data of a complex system considering multi-state, dependent behavior can be obtained and analyzed automatically. In this paper, we present a method that is based on the K2 algorithm for establishing a Bayesian network (BN) for estimating the reliability of a multi-state system with dependent behavior. Facilitated by BN tools, the reliability modeling and the reliability estimation can be conducted automatically. An illustrative example is used to demonstrate the performance of the method.

Tradycyjne modele niezawodności, takie jak analiza drzewa błędów (FTA) czy schemat blokowy niezawodności (RBD), buduje się zazwyczaj w oparciu o tworzone przez inżynierów systemowych schematy zasad działania systemu, których przygotowanie wymaga dużych nakładów czasu i pracy. Jakość i poprawność tych modeli zależy od umiejętności i doświadczenia inżynierów, a same modele są trudne do zweryfikowania. Dzięki rozwojowi technik akwizycji i eksploracji danych oraz modelowania systemów, dane operacyjne złożonego systemu uwzględniające jego zależne, wielostanowe zachowania mogą być pozyskiwane i analizowane automatycznie. W artykule przedstawiono metodę konstrukcji sieci bayesowskiej (BN) opartą na algorytmie K2, która pozwala na ocenę niezawodności systemu wielostanowego o zachowaniach zależnych. Dzięki narzędziom BN, modelowanie i szacowanie niezawodności może odbywać się automatycznie. Działanie omawianej metody zilustrowano na podstawie przykładu.

Słowa kluczowe

reliability analysis Bayesian network structure learning multi-state system (MSS) dependent failure

analiza niezawodności sieć bayesowska uczenie struktury system wielostanowy uszkodzenie zależne

Wydawca

Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne

Czasopismo

Eksploatacja i Niezawodność

Rocznik

2020

Tom

Vol. 22, no. 1

Strony

170--178

Opis fizyczny

Bibliogr. 32 poz., rys., tab.

Twórcy

autor

Li Zhifeng

lizhifeng@buaa.edu.cn

School of Reliability and Systems Engineering Beihang University No.37 Xueyuan RD. Haidian, 100191, Beijing, China

autor

Wang Zili

wangzili@buaa.edu.cn

School of Reliability and Systems Engineering Beihang University No.37 Xueyuan RD. Haidian, 100191, Beijing, China

autor

Ren Yi

renyi@buaa.edu.cn

School of Reliability and Systems Engineering Beihang University No.37 Xueyuan RD. Haidian, 100191, Beijing, China

autor

Yang Dezhen

dezhenyang@buaa.edu.cn

School of Reliability and Systems Engineering Beihang University No.37 Xueyuan RD. Haidian, 100191, Beijing, China

autor

Lv Xing

lvxing@buaa.edu.cn

School of Reliability and Systems Engineering Beihang University No.37 Xueyuan RD. Haidian, 100191, Beijing, China

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-bedebeaf-5976-432d-ad85-ada0d02ed5d8