Wang Dewei, Jiang Chendi, Park Chanseok
Department of Statistics, University of South Carolina, Columbia, SC, 29208, USA.
Department of Industrial Engineering, Pusan National University, Busan, Korea.
Lifetime Data Anal. 2019 Apr;25(2):341-360. doi: 10.1007/s10985-018-9425-8. Epub 2018 Feb 22.
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.
自20世纪40年代初以来，人们一直在研究负载分担模型，以解释并行系统中组件的随机依赖性。它假定，随着组件逐个失效，施加到系统上的总工作量由剩余组件分担，从而影响它们的性能。这种相关系统已在许多工程应用中得到研究，包括但不限于纤维复合材料、制造业、发电厂、计算工作量分析、软件和硬件可靠性等。已经提出了许多统计模型来分析每次工作量重新分配的影响；即，对每个剩余组件的故障率的影响。然而，它们没有考虑幸存组件在重新分配之前已经工作了多长时间。我们将这种负载分担模型称为无记忆模型。为了弥补这一潜在缺陷，我们提出了一个考虑工作历史的负载分担模型通用框架。通过模拟研究，我们表明，不恰当地使用无记忆假设可能导致对重新分配影响的推断不准确。此外，分析了等离子体显示设备的一个实际数据示例来说明我们的方法。
