Importance measure of probabilistic common cause failures under system hybrid uncertainty based on bayesian network

• Autorzy: Mi Jinhua , Li Yan-Feng , Beer Michael , Broggi Matteo , Cheng Yuhua

Identyfikatory

DOI

10.17531/ein.2020.1.13

Warianty tytułu

PL

Oparta na sieci bayesowskiej miara ważności probabilistycznych uszkodzeń spowodowanych wspólną przyczyną w warunkach niepewności hybrydowej systemu

• Języki publikacji: EN

Abstrakty

EN

When dealing with modern complex systems, the relationship existing between components can lead to the appearance of various dependencies between component failures, where multiple items of the system fail simultaneously in unpredictable fashions. These probabilistic common cause failures affect greatly the performance of these critical systems. In this paper a novel methodology is developed to quantify the importance of common cause failures when hybrid uncertainties are presented in systems. First, the probabilistic common cause failures are modeled with Bayesian networks and are incorporated into the system exploiting the α factor model. Then, probability-boxes (bound analysis method) are introduced to model the hybrid uncertainties and quantify the effect of uncertainties on system reliability. Furthermore, an extended Birnbaum importance measure is defined to identify the critical common cause failure events and coupling impact factors when uncertainties are expressed by probability-boxes. Finally, the effectiveness of the method is demonstrated through a numerical example.

PL

W przypadku nowoczesnych systemów złożonych, relacje zachodzące między komponentami mogą prowadzić do pojawienia się różnych zależności między ich uszkodzeniami, a tym samym do sytuacji w których kilka składowych systemu ulega uszkodzeniu jednocześnie w nieprzewidywalny sposób. Tego typu probabilistyczne uszkodzenia wywołane wspólną przyczyną (PCCF) mają ogromny wpływ na wydajność tych kluczowych systemów. W przedstawionym artykule opracowano nową metodę szacowania ważności PCFF w sytuacjach, gdy w systemie występują niepewności hybrydowe. W pierwszej kolejności, PCFF zamodelowano za pomocą sieci bayesowskich i włączono do systemu wykorzystującego model współczynnika α. Następnie, wprowadzono przedziały prawdopodobieństwa, tzw. probability boxes (bound analysis method), w celu zamodelowania niepewności hybrydowych i kwantyfikacji wpływu tych niepewności na niezawodność systemu. Ponadto zdefiniowano rozszerzoną miarę ważności Birnbauma, która pozwala zidentyfikować krytyczne zdarzenia PCCF oraz czynniki, które je wywołały, w przypadkach, gdy niepewności wyrażone są za pomocą probability boxes. Skuteczność metody wykazano na przykładzie numerycznym.

Słowa kluczowe

EN

probabilistic common cause failure; Bayesian network; α factor model; extended Birnbaum importance

PL

probabilistyczne uszkodzenie spowodowane wspólną przyczyną; sieć bayesowska; model współczynnika α; rozszerzona miara ważności Birnbauma

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2020
• Tom: Vol. 22, no. 1
• Strony: 111--120
• Opis fizyczny: Bibliogr. 34 poz., rys., tab.

Twórcy

autor

Mi Jinhua

• School of Automation Engineering, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan, 611731, P.R. China

autor

Li Yan-Feng

• Center for System Reliability and Safety, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan, 611731, P.R. China

autor

Beer Michael

• Institute for Risk and Reliability, Leibniz University Hannover, Callinstr. 34, Hannover, 30167, Germany

autor

Broggi Matteo

• Institute for Risk and Reliability, Leibniz University Hannover, Callinstr. 34, Hannover, 30167, Germany

autor

Cheng Yuhua

• School of Automation Engineering, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan, 611731, P.R. China

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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).