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

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Treść / Zawartość
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Warianty tytułu
PL
Zastosowanie dynamicznych sieci bayesowskich do oceny niezawodności elektrycznego systemu trakcyjnego
Języki publikacji
EN
Abstrakty
EN
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.
PL
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.
Rocznik
Strony
349--357
Opis fizyczny
Bibliogr. 35 poz., rys.
Twórcy
autor
  • 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
  • State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University 100044, China
  • Jinan Locomotive Depot Jinan Railway Bureau 250001, China
autor
  • 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
  • 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
  • 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
Bibliografia
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  • 16. Khakzad N. Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures. Reliability Engineering & System Safety 2015; 138: 263-272, https://doi.org/10.1016/j.ress.2015.02.007.
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  • 22. Marquez D, Neil M, Fenton N. Improved reliability modeling using Bayesian networks and dynamic discretization. Reliability Engineering & System Safety 2010; 95(4): 412-425, https://doi.org/10.1016/j.ress.2009.11.012.
  • 23. Portinale L, Bobbio A. Bayesian Networks for Dependability Analysis: an Application to Digital Control Reliability. Computer Science 2013: 551—558.
  • 24. Portinale L, Raiteri D C, Montani S. Supporting reliability engineers in exploiting the power of Dynamic Bayesian Networks. International Journal of Approximate Reasoning 2010; 51(2): 179-195, https://doi.org/10.1016/j.ijar.2009.05.009.
  • 25. Seo S I, Park C S, Choi S H, et al. Reliability management and assessment for the electric traction system on the Korea High-Speed Train. Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail & Rapid Transit 2010; 224(3): 179-188, https://doi.org/10.1243/09544097JRRT297.
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  • 30. Wang S, Ji Y, Dong W. Reliability Analysis for High-Speed Train Control System by Means of Stochastic Automata. Advanced Science Letters 2012; 6(1): 619-624, https://doi.org/10.1166/asl.2012.2248.
  • 31. Weber P, Jouffe L. Complex system reliability modelling with dynamic object oriented Bayesian networks (DOOBN). Reliability Engineering & System Safety 2006; 91(2): 149-162, https://doi.org/10.1016/j.ress.2005.03.006.
  • 32. Weber P, Medina-Oliva G, Simon C, et al. Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas. Engineering Applications of Artificial Intelligence 2012; 25(4): 671-682, https://doi.org/10.1016/j.engappai.2010.06.002.
  • 33. Wu X, Liu H, Zhang L, et al. A dynamic Bayesian network based approach to safety decision support in tunel construction. Reliability Engineering & System Safety 2015; 134: 157-168, https://doi.org/10.1016/j.ress.2014.10.021.
  • 34. Y. Tian, Research on extreme small sample reliability evaluation method and its application on center sill and body bolster of C70 gondola car, Beijing: Beijing Jiaotong University, 2008.
  • 35. Zhu D, Liu H. Reliability evaluation of high-speed train bearing with minimum sample. Journal of Central South University, 2013.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d7e9a339-ac31-4f6d-be9c-0450a1ea2aa1
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