The application of dynamic bayesian network to reliability assessment of emu traction system

Wang, Y.; Bi, L.; Wang, S.; Lin, S.; Xiang, W.

doi:10.17531/ein.2017.3.5

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Tytuł artykułu

The application of dynamic bayesian network to reliability assessment of emu traction system

Autorzy

Wang Y. , Bi L. , Wang S. , Lin S. , Xiang W.

Treść / Zawartość

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Identyfikatory

DOI

10.17531/ein.2017.3.5

Warianty tytułu

Zastosowanie dynamicznych sieci bayesowskich do oceny niezawodności elektrycznego systemu trakcyjnego

Języki publikacji

Abstrakty

The article introduces a novel application of a Dynamic Bayesian Network (DBN) in the reliability assessment with regard to the traction system of Electric Multiple Units (EMU), which focus on modeling approach to DBN construction. As a result of high complexity and growing interdependencies, it is increasingly vulnerable to the failure of components. Although many studies on the use of BN for estimating the system reliability have been conducted, there is a lack of effective modeling power regarding current tools in depicting both functional and temporal dependencies between components. In this paper, a new modeling approach to DBN generation is submitted, which can be applied to the system made up of certain components and different types of flows propagating through them. The Component-based CPT (Conditional Probability Table) and Time-dependent CPT are used to describe functional dependencies and temporal dependencies respectively. As the complexity of the system cannot be modeled in a tractable way as a DBN, a Breadth-First-Search (BFS) algorithm is introduced for the construction of the DBN model in an automated manner. With the application of the proposed DBN-based approach, the reliability of the traction system can be evaluated at any given time, which is of great significance to determine the plan of maintenance in an effort to ensure the system safety.

W artykule omówiono nowatorskie zastosowanie dynamicznej sieci bayesowskiej (DBN) do oceny niezawodności elektrycznego systemu trakcyjnego ze szczególnym uwzględnieniem metod modelowania DBN. W związku z rosnącą złożonością elektrycznych systemów trakcyjnych oraz wynikającą z niej coraz większą ilością współzależności między komponentami, systemy te narażone są coraz częściej na awarie części składowych. Chociaż istnieje wiele badań dotyczących oceny niezawodności systemów trakcyjnych, stosowane obecnie narzędzia nie mają odpowiedniej mocy modelowania koniecznej do opisu zależności funkcjonalnych i czasowych pomiędzy częściami składowymi. W niniejszej pracy zaproponowano nową metodę modelowania generowania DBN, którą można stosować w odniesieniu do systemów składających się z pewnych określonych komponentów oraz różnych typów rozchodzących się przez nie przepływów. Zależności funkcjonalne i czasowe opisano, odpowiednio, za pomocą tablicy komponentowych prawdopodobieństw warunkowych (Component-based Conditional Probability Table, CPT) oraz tablicy czasowo-zależnych prawdopodobieństw warunkowych. Ponieważ złożoność systemu nie pozwala na zamodelowanie go w prosty sposób jako DBN, do automatycznej budowy modelu DBN wykorzystano algorytm przeszukiwania wszerz (Breadth-First-Search). Oceny niezawodności systemu trakcyjnego z wykorzystaniem proponowanej metody opartej na DBN można dokonywać w dowolnym czasie, co ma ogromne znaczenie przy planowaniu konserwacji w celu zapewnienia bezpieczeństwa systemu.

Słowa kluczowe

DBN traction system reliability assessment breadth-first-search algorithm

dynamiczna sieć bayesowska system trakcyjny ocena niezawodności algorytm przeszukiwania wszerz

Wydawca

Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne

Czasopismo

Eksploatacja i Niezawodność

Rocznik

2017

Tom

Vol. 19, no. 3

Strony

349--357

Opis fizyczny

Bibliogr. 35 poz., rys.

Twórcy

autor

Wang Y.

wangyanhui@bjtu.edu.cn

State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University 100044, China
School of Traffic and Transportation Beijing Jiaotong University, Beijing 100044, China

autor

Bi L.

State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University 100044, China
Jinan Locomotive Depot Jinan Railway Bureau 250001, China

autor

Wang S.

State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University 100044, China
School of Traffic and Transportation Beijing Jiaotong University, Beijing 100044, China

autor

Lin S.

School of Traffic and Transportation Beijing Jiaotong University, Beijing 100044, China
State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University 100044, China

autor

Xiang W.

State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University 100044, China
School of Traffic and Transportation Beijing Jiaotong University, Beijing 100044, China

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Bibliografia

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