Abstract Maintenance improvement is affected by many uncertain factors such as maintainer experience, machine deterioration state, maintenance tools and equipment, it is very hard to determine each restoration degree ahead of repair operation. Therefore, this paper considers a maintenance problem in the presence of competing risks (degradation and sudden failure) under stochastic maintenance quality for a ship pump. The degradation process of ship pump is characterized by a non-homogeneous Gamma process and the hazard rate of sudden failure is described by a proportional hazards model. We propose a two-level condition-based maintenance (CBM) policy with stochastic maintenance quality. The policy combines degradation control limit and age control limit under stochastic maintenance quality of Beta distribution, normal distribution and uniform distribution, respectively, in maintenance decision-making. Maintenance optimization problem is formulated and solved by a computational algorithm in a semi-Markov decision process (SMDP) framework, with the objective of minimizing the long-run expected average cost. The proposed model is illustrated using a numerical study and a real case study of ship pump, respectively, and comparative results show the effectiveness and superiority of our approach.

中文翻译：

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随机维修质量下受竞争风险影响的船泵状态维修

摘要 维修改进受维修人员经验、机器老化状态、维修工具和设备等诸多不确定因素的影响，在维修操作前很难确定每个恢复程度。因此，本文考虑了船舶泵随机维修质量下存在竞争风险（退化和突然故障）的维修问题。船用泵的退化过程以非均匀伽马过程为特征，突发故障的危险率用比例危险模型描述。我们提出了一种具有随机维护质量的两级状态维护 (CBM) 策略。该策略结合了 Beta 分布随机维护质量下的退化控制限制和年龄控制限制，在维修决策中分别采用正态分布和均匀分布。维护优化问题由半马尔可夫决策过程 (SMDP) 框架中的计算算法制定和解决，目标是最小化长期预期平均成本。分别使用数值研究和船舶泵的真实案例研究说明了所提出的模型，比较结果表明了我们方法的有效性和优越性。