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Inference for Multicomponent Systems With Dependent Failures

机译:具有相关故障的多组件系统的推理

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Multicomponent systems may experience failures with correlations amongst failure times of groups of components, and some subsets of components may experience common cause, simultaneous failures. We present a novel, general approach to model construction and inference in multicomponent systems incorporating these correlations in an approach that is tractable even in very large systems. In our formulation, the system is viewed as being made up of independent overlapping subsystems (IOS). In these systems, components are grouped together into overlapping subsystems, and further into nonoverlapping subunits. Each subsystem has an independent failure process, and each component's failure time is the time of the earliest failure in all of the subunits of which it is a part. We apply this method to observations of an IOS model based on a multicomponent system accumulating damage due to a series of shocks, and with no repair/rectification actions. The model associates individual shock processes with each subsystem, and includes the Marshall-Olkin multivariate exponential model as a special case. We present approaches to simulation and to the estimation of the parameters of the model, given component failure data for various system configurations (series, parallel, and other arrangements).
机译:多组件系统可能会遇到故障,并且组件组的故障时间之间存在相关性,并且组件的某些子集可能会遇到常见原因,同时发生故障。我们提出了一种新颖的,通用的方法,用于在多组件系统中进行模型构建和推理,并在非常大的系统中也能很好地利用这些相关性。在我们的表述中,该系统被视为由独立的重叠子系统(IOS)组成。在这些系统中,组件被组合在一起成为重叠的子系统,再进一步分成不重叠的子单元。每个子系统都有一个独立的故障过程,每个组件的故障时间是它所属的所有子单元中最早故障的时间。我们将此方法应用于基于多组件系统的IOS模型的观测,该组件由于一系列冲击而累积损坏,并且没有维修/纠正措施。该模型将各个冲击过程与每个子系统相关联,并作为特殊情况包括Marshall-Olkin多元指数模型。我们给出了用于仿真和模型参数估计的方法,给出了各种系统配置(串联,并联和其他布置)的组件故障数据。

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