Reliability of components of coherent systems: estimates in presence of masked data
Reliability of components of coherent systems: estimates in presence of masked data
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发表时间：
2017-07
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通讯作者：
A. Rodrigues;C. A. B. Pereira;A. Polpo
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作者：
A. Rodrigues;C. A. B. Pereira;A. Polpo
组件系统的可靠性取决于每个组件的可靠性。因此，最初的统计工作应该是估计系统每个组成部分的可靠性。这不是一件容易的事，因为当系统失效时，给定部件的失效时间是无法观测的，即删失数据。Rodrigues等人（2017）提出了一种组件可靠性估计的解决方案，当系统故障时间和系统故障时每个组件的状态可用时（如果它在系统故障之前，之后或它负责系统故障）。然而，在某些情况下，可能难以在系统故障时识别组件的状态。这种情况下，系统的故障原因被掩盖。由于并联和串联系统是最简单的系统，在文献中已经出现了无数的替代解决方案，这两个系统。据我们所知，这似乎是第一个考虑相干系统一般情况的工作。三参数威布尔分布被认为是组件的故障时间模型。相同分布的故障时间不需要限制。此外，对先验分布的主观选择没有限制，但优先考虑连续先验分布;这些先验很好地代表了系统运行环境的细微差别。Gibbs算法中的大都会支持获得后验分布量的统计工作。通过多次仿真实验，验证了该模型的有效性。我们还考虑了计算机硬盘驱动器的真实的数据集，以提出的模型的实际相关性。
The reliability of a system of components depends on reliability of each component. Thus, the initial statistical work should be the estimation of the reliability of each component of the system. This is not an easy task because when the system fails, the failure time of a given component can not be observed, that is, censored data. Rodrigues et al. (2017) presented a solution for reliability estimation of components when it is avaliable the system failure time and the status of each component at the time of system failure (if it had failed before, after or it is responsible for system failure). However, there are situations it may be difficult to identify the status of components at the moment of system failure. Such cases are systems with masked causes of failure. Since parallel and series systems are the simplest systems, innumerous alternative solutions for these two systems have been appeared in the literature. To the best of our knowledge, this seems to be the first work that considers the general case of coherent systems. The three-parameter Weibull distribution is considered as the component failure time model. Identically distributed failure times is not required restrictions. Furthermore, there is no restriction on the subjective choice of prior distributions but preference has been given to continuous prior distributions; these priors represent well the nuances of the environment that the system operates. The statistical work of obtaining quantities of the posterior distribution is supported by the Metropolis within Gibbs algorithm. With several simulations, the excellent performance of the model was evaluated. We also consider a computer hard-drives real dataset in order to present the practical relevance of the proposed model.