The load-sharing model has been studied since the early 1940s to account for the stochastic dependence of components in a parallel system. It assumes that, as components fail one by one, the total workload applied to the system is shared by the remaining components and thus affects their performance. Such dependent systems have been studied in many engineering applications which include but are not limited to fiber composites, manufacturing, power plants, workload analysis of computing, software and hardware reliability, etc. Many statistical models have been proposed to analyze the impact of each redistribution of the workload; i.e., the changes on the hazard rate of each remaining component. However, they do not consider how long a surviving component has worked for prior to the redistribution. We name such load-sharing models as memoryless. To remedy this potential limitation, we propose a general framework for load-sharing models that account for the work history. Through simulation studies, we show that an inappropriate use of the memoryless assumption could lead to inaccurate inference on the impact of redistribution. Further, a real-data example of plasma display devices is analyzed to illustrate our methods.

中文翻译：

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带内存的负载共享系统的可靠性分析。

自1940年代初以来就研究了负载分担模型，以解决并行系统中组件的随机依赖性。它假定，当组件一个接一个地发生故障时，其余组件将共享应用于系统的总工作负载，从而影响其性能。已经在许多工程应用中研究了这种依赖系统，这些工程应用包括但不限于纤维复合材料，制造，发电厂，计算的工作量分析，软件和硬件可靠性等。已提出了许多统计模型来分析每次重新分配的影响。工作量；即，每个剩余组件的危险率的变化。但是，他们不考虑剩余组件在重新分发之前已经工作了多长时间。我们将这样的负载共享模型命名为无记忆。为了弥补这种潜在的局限性，我们提出了一个用于说明工作历史的负载分担模型的通用框架。通过仿真研究，我们发现对无记忆假设的不当使用可能导致对重新分配的影响的推断不正确。此外，分析了等离子显示设备的实际数据示例，以说明我们的方法。