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Reliability modeling of correlated competitions and dependent components with random failure propagation time
Author(s) -
Xing Liudong,
Zhao Guilin,
Wang Yujie,
Xiang Yisha
Publication year - 2020
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2609
Subject(s) - probabilistic logic , component (thermodynamics) , reliability (semiconductor) , reliability engineering , isolation (microbiology) , uncorrelated , function (biology) , computer science , common cause failure , statistical model , engineering , mathematics , statistics , artificial intelligence , common cause and special cause , power (physics) , physics , microbiology and biotechnology , quantum mechanics , evolutionary biology , biology , thermodynamics
Function dependence takes place in systems where the malfunction of certain trigger component(s) causes other system components (referred to as dependent components) to become unusable or inaccessible. Systems undergoing function dependence often exhibit diverse statuses due to competitions in the time domain between propagated failure from a dependent component and local failure of the corresponding trigger component. If the former wins (ie, occurring first), a failure propagation effect is induced, crashing the entire system. If the latter wins, a failure isolation effect may be induced quarantining the damage from the propagated failure (the isolation effect can occur in a deterministic or probabilistic manner depending on applications). Existing models addressing such competitions have restrictive assumptions such as uncorrelated competitions from multiple function dependence groups, zero or negligible failure propagation time, and deterministic failure isolation effect. This paper advances the state of the art by proposing a combinatorial reliability model for systems undergoing correlated, probabilistic competitions and random failure propagation time for dependent components. A case study of a wireless body area network system for patient monitoring is performed to illustrate the proposed methodology and effects of different model parameters on the system reliability.