Reliability assessment of a system with multi‐shock sources subject to dependent competing failure processes under phase‐type distribution

Abstract

For a system affected by multiple shock sources, the system usually experiences not only one failure process. This paper develops a reliability model for systems with multi‐shock sources subject to dependent competing failure processes (DCFPs). DCFPs consist of two failure modes: soft and hard failures. Multiple shock sources act on the system as follows: assuming there are m shock sources in total, the kth shock source works on the system at first until the system fails, and then the next shock source re‐acts on the system. Unlike the methods in other papers, the phase‐type distribution is used to build the hard failure reliability model in this paper. We consider the time lag of impacts and assume that the impact magnitudes do not exceed the hard failure threshold as an element in the transition matrix. At the same time, consider these two situations to establish a reliability model. In addition, we divide shock magnitudes into three areas: dead, nondeadly, and safe zones. An application example of a micro‐engine is studied to describe the availability of the developed reliability model. And we conduct the sensitivity analysis to exemplify the influence of the performance of the micro‐engine with parameter changing. With the proposed model, the reliability analysis is more efficient with multi‐shock sources subject to DCFPs

Robust estimation of dependent competing risk model under interval monitoring and determining optimal inspection intervals

“…In such case, the system fails if any one component fails, it is indeed an example of dependent competing risk set up. In the literature, we find the study on dependent competing risk in Bai et al [2], Cai et al [13], Feizjavdian and Hashemi [23], Kundu [26], Kundu and Mondal [25], Shen and Xu [34], Lyu et al [29] and references therein.…”

Reliability modeling of systems with random failure threshold subjected to cumulative shocks using machine learning method

“…Shamstabar et al 20 introduced a closed-form solution for reliability monitoring of a system subjected to cumulative shock failure based on PH distributions. Lyu et al 21 assessed the reliability of a system subjected to cumulative shocks from multiple shock sources using PH distribution. In most existing studies of analyzing shock models, the authors consider fixed failure threshold for a system.…”

“…introduced a closed‐form solution for reliability monitoring of a system subjected to cumulative shock failure based on PH distributions. Lyu et al 21 . assessed the reliability of a system subjected to cumulative shocks from multiple shock sources using PH distribution.…”