Evaluation Methods for the Reliability of a Linear Connected-(1, ?2)-or-(2, ?1)-Out-of-(m, n):F Lattice System
Evaluation Methods for the Reliability of a Linear Connected-(1, ?2)-or-(2, ?1)-Out-of-(m, n):F Lattice System
复制标题
线性连通(1, ?2) 或(2, ?1)-Out-of-(m, n):F 格子系统的可靠性评估方法
DOI：
10.1007/978-3-030-94683-8_5
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Akiba Tomoaki
中科院分区：
文献类型：
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作者：
Nakamura Taishin;Yamamoto Hisashi;Shinzato Takashi;Akiba Tomoaki
研究了线性连通-(1，2)-或-(2，1)-out-of-(m，n)：F格子系统的可靠性评估方法。为了计算系统的可靠性，以往的研究提出了递推方程和有限马尔可夫链嵌入方法。然而，没有研究直接比较这两种方法的效率。因此，本文在文献中首次对两种方法的效率进行了全面的比较。相应地，推断他们的效率取决于情况。这一比较将提供指导方针，以帮助决定应采取的方法。此外，在不需要获得精确的系统可靠性的情况下，还采用了上下界。已经报道了几个有用和简单的界限；然而，需要计算负担的更严格的界限还没有得到充分的讨论。因此，我们得到了系统可靠性的上下界。数值实验的结果表明，与现有的边界相比，我们以牺牲计算量为代价得到了更紧的边界。这些更严格的界限可以充分评估大型系统的可靠性。本文的理论成果将为工业制造提供有益的启示，有助于实际系统的稳定运行，促进工业4.0的发展。
This study focuses on system reliability evaluation methods for the linear connected-(1, 2)-or-(2, 1)-out-of-(m,n):F lattice systems. To compute system reliability, previous studies have proposed the recursive equation and finite Markov chain imbedding approaches. However, no study directly compared the efficiency of both approaches. Hence, for the first time in the literature, this paper comprehensively compares the efficiency of both approaches. Accordingly, infer that their efficiency depends on the situation. This comparison will provide a guideline to help decide the approach that should be adopted. Furthermore, upper and lower bounds are also employed, provided it is unnecessary to obtain the exact system reliability. Several useful and simple bounds were reported; however, tighter bounds that require the computational burden have not been sufficiently discussed. Therefore, we derive the upper and lower bounds for the system reliability. The results of the numerical experiment indicate that we obtained the tighter bounds at the expense of computational effort, compared with existing bounds. These tighter bounds can sufficiently evaluate the reliability of large systems. The theoretical results in this paper would provide useful insights into industrial manufacturing, contribute to the stable operation of practical systems, and promote Industry 4.0.