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An integrated framework for health measures prediction and optimal maintenance policy for mechanical systems using a proportional hazards model

机译：使用比例风险模型的机械系统健康措施预测和最佳维护策略的集成框架

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摘要

This paper considers an integrated framework for health measures prediction and optimal maintenance policy for mechanical systems subject to condition monitoring (CM) and random failure. We propose the proportional hazards model (PHM) to consider CM information as well as the age of the mechanical systems. Although the form of health prediction for the mechanical systems under periodic monitoring in the PHM with Markov chain was developed previously, the case of the continuous-state degradation process allowing possible degradation between the inspections still has not appeared. To this aim, the paper allows the use of Gamma process with non-constant degradation, which broadens the application area of PHM. A matrix-based approximation method is employed to compute health measures of the machine, such as condition reliability, mean residual life, residual life distribution. Based on the health measures, the optimal maintenance policy, which considers both hazard rate control limit and age control limit, is proposed and the optimization problem is formulated and solved in a semi-Markov decision process (SMDP) framework. The objective is to minimize the long-run expected average cost. The method is illustrated using two real data sets obtained from feed subsystem of a boring machine and GaAs lasers collected at regular time epochs, respectively. A comparison with other methods is given, which illustrates the effectiveness of our approach.
机译：本文考虑了适用于状态监测（CM）和随机故障的机械系统的健康措施预测和最佳维护策略的集成框架。我们提出比例风险模型（PHM），以考虑CM信息以及机械系统的寿命。尽管先前已开发了具有马尔可夫链的PHM中定期监视下的机械系统的健康预测的形式，但仍未出现允许在两次检查之间可能发生退化的连续状态退化过程。为此，本文允许使用具有非恒定降解的Gamma工艺，从而扩大了PHM的应用范围。采用基于矩阵的近似方法来计算机器的健康状况，例如状态可靠性，平均剩余寿命，剩余寿命分布。基于健康指标，提出了同时考虑危险率控制限度和年龄控制限度的最优维修策略，并在半马尔可夫决策过程框架下提出并解决了优化问题。目的是使长期预期平均成本最小化。使用分别从镗床的进料子系统获得的两个真实数据集和在规则时间周期收集的GaAs激光器说明了该方法。与其他方法进行了比较，说明了我们方法的有效性。

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