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Reliability analysis of repairable systems with recurrent misuse-induced failures and normal-operation failures

机译:具有反复滥用引起的故障和正常运行故障的可修复系统的可靠性分析

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摘要

Failure of a repairable system may be attributed to operators' misuse or system deterioration. The misuse may further deteriorate the system under normal operating conditions. Motivated by a real-world data set that records the recurrence times of misuse-induced failures and the normal-operation failures, this study proposes a stochastic process model for recurrence data analysis, where one type of failures is affected by the other. A non-homogeneous Poisson process and a trend-renewal process are separately used as the baseline event process models for the misuse-induced failures and the normal-operation failures, respectively. These two models are then combined by treating the event count of misuse-induced failures as covariate of the event process of normal-operation failures. A Bayesian framework is developed for parameter estimation and dependence tests of the two failure modes. A simulation study and the recurrence data from a manufacturing system are used to demonstrate the proposed method. (C) 2017 Elsevier Ltd. All rights reserved.
机译:可修复系统的故障可能归因于操作员的滥用或系统性能下降。在正常操作条件下,滥用可能会使系统进一步恶化。受记录错误使用引起的故障和正常运行故障的重复发生时间的真实数据集的启发,本研究提出了一种随机过程模型用于重复数据分析,其中一种故障类型受到另一种故障的影响。非均质泊松过程和趋势更新过程分别用作滥用事件导致的故障和正常操作故障的基线事件过程模型。然后,通过将误用引起的故障的事件计数视为正常操作故障的事件过程的协变量,将这两个模型组合在一起。贝叶斯框架被开发用于两种失效模式的参数估计和依赖性测试。仿真研究和制造系统的重复数据被用来证明该方法。 (C)2017 Elsevier Ltd.保留所有权利。

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