On Distribution and Average Run Length of a Two-Stage Control Process

Chang, Hsing-Ming; Fu, James C.

doi:10.1007/s11009-022-09935-4

Cited by 1 publication

(1 citation statement)

References 25 publications

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“…By utilizing FMCIA, Gao et al 35 and Wang et al 36 obtained the reliability expressions of sparsely connected multi-state consecutive-k-out-of-n: G systems and k-out-of-n: F capability-balanced systems subject to shocks, respectively. Additionally, the usefulness of FMCIA in other research fields has also been adequately demonstrated by various literature, such as the distribution theory, 37 and quality control. 38 The contribution of this paper can be summed up in the following.…”

Section: Introductionmentioning

confidence: 82%

Generalized mixed shock model for multi-component systems in the shock environment with a change point

Wang

Ning

Zhao

2022

Proceedings of the Institution of Mechanical Engineers, Part O:

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The reliability of various systems under shock models has been explored extensively in the literature, where the constant mechanism of shock impact has always been studied. Nevertheless, engineering systems operate in a more complicated shock environment due to developments, and it is worth studying the reliability of such multi-component systems in a variable shock environment. Driven by the research gaps and practical situations, this paper constructs a reliability model of multi-component systems under a generalized mixed shock model with a change point. The multi-state components operate in the shock environment I initially and it may continue to run in the shock environment II after the random change point of the environment. Two novel shock impact mechanisms of the variable environment are put forward, where a series of failure criteria based on shocks are included. Four different structures of multi-component systems are considered in this paper. It can be proved that the proposed mixed shock model is a generalization of some transformed models. A multi-stage finite Markov chain imbedding approach is established to derive the probabilistic indices of the components and entire systems. Based on the engineering applications, illustrative examples are provided to verify the effectiveness of the proposed model.

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