Parameter estimation for a repairable system under imperfect maintenance

2008 Annual Reliability and Maintainability Symposium Pub Date : 2008-01-28 DOI:10.1109/RAMS.2008.4925834

Pingjian Yu, J. Song, C. R. Cassady

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引用次数: 14

Abstract

Estimation of reliability and maintainability parameters is essential in modeling repairable systems and determining maintenance policies. However, because of the aging of repairable systems under imperfect maintenance, failure times are neither identically nor independently distributed, which makes parameter estimation difficult. In this paper, we apply Bayesian methods for estimation of reliability and maintainability parameters based on historical reliability and maintainability (RAM) data. We assume the first failure of the repairable system follows a Weibull probability distribution. The repairable system experiences Kijima Type I imperfect corrective maintenance and Kijima Type I imperfect preventive maintenance. Using a Bayesian perspective, we estimate four parameters for this repairable system: the shape parameter of the Weibull probability distribution (beta), the scale parameter of the Weibull distribution (eta), the imperfect maintenance factor for corrective maintenance (alphar) and the imperfect maintenance factor for preventive maintenance (alphap). The proposed method is illustrated with simulated RAM data.

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不完全维护下可修系统的参数估计

可靠性和可维护性参数的估计是建模可修复系统和确定维护策略的关键。然而，由于可修系统在不完全维护下的老化，故障时间既不相同也不独立分布，这给参数估计带来了困难。本文将贝叶斯方法应用于基于历史可靠性和可维护性数据的可靠性和可维护性参数估计。假设可修系统的第一次故障服从威布尔概率分布。可修系统经历木岛I型不完全纠正性维修和木岛I型不完全预防性维修。利用贝叶斯的观点，我们估计了这个可修系统的四个参数:威布尔概率分布的形状参数(beta)、威布尔分布的尺度参数(eta)、纠正性维修的不完善维修因子(alpha)和预防性维修的不完善维修因子(alpha)。最后用RAM数据进行了仿真验证。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2008 Annual Reliability and Maintainability Symposium

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