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首页> 外文期刊>International journal of quality, statistics, & reliability >Bayesian Prediction of the Overhaul Effect on a Repairable System with Bounded Failure Intensity
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Bayesian Prediction of the Overhaul Effect on a Repairable System with Bounded Failure Intensity

机译：具有有限失效强度的可修复系统的大修影响的贝叶斯预测

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摘要

This paper deals with the Bayes prediction of the future failures of a deteriorating repairable mechanical system subject to minimal repairs and periodic overhauls. To model the effect of overhauls on the reliability of the system a proportional age reduction model is assumed and the 2-parameter Engelhardt-Bain process (2-EBP) is used to model the failure process between two successive overhauls. 2-EBP has an advantage over Power Law Process (PLP) models. It is found that the failure intensity of deteriorating repairable systems attains a finite bound when repeated minimal repair actions are combined with some overhauls. If such a data is analyzed through models with unbounded increasing failure intensity, such as the PLP, then pessimistic estimates of the system reliability will arise and incorrect preventive maintenance policy may be defined. On the basis of the observed data and of a number of suitable prior densities reflecting varied degrees of belief on the failure/repair process and effectiveness of overhauls, the prediction of the future failure times and the number of failures in a future time interval is found. Finally, a numerical application is used to illustrate the advantages from overhauls and sensitivity analysis of the improvement parameter carried out.
机译：本文涉及贝叶斯对不断恶化的可修复机械系统在最少维修和定期大修的情况下未来故障的预测。为了对大修对系统可靠性的影响进行建模，我们采用了按比例减少寿命的模型，并使用2参数Engelhardt-Bain过程（2-EBP）对两次连续大修之间的故障过程进行建模。 2-EBP具有优于幂律过程（PLP）模型的优势。可以发现，当反复进行的最小维修动作和一些大修相结合时，不断恶化的可维修系统的故障强度将达到一个有限的界限。如果通过具有无限增加的故障强度的模型（例如PLP）分析此类数据，则会出现对系统可靠性的悲观估计，并且可能定义了错误的预防性维护策略。根据观察到的数据以及反映故障/修理过程和大修效果的不同程度的各种合适的先前密度，可以找到对未来故障时间和未来时间间隔内故障数量的预测。最后，通过数值应用说明了大修和对改进参数进行敏感性分析的优势。

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