The growing technological capability for real-time condition monitoring (CM) of industrial equipment has spurred significant interest in methods for optimal maintenance planning using CM signals. Existing approaches for maintenance policy development consider degradation to be either fully or partially observable. For the more general case of partial observability, it is usually assumed that the relationship between the underlying degradation process and the observed condition is time-invariant. In this paper, we address this major shortcoming by modeling observed CM signals through an underlying failure process wherein the linkage is time-dependent piecewise linear with jumps, and then utilizing a Partially Observed Markov Decision Process (POMDP) to determine the optimal maintenance strategy. We investigate the structure of the policy and show that, under certain conditions, a control-limit policy exists, i.e., a belief threshold exists beyond which the optimal action is to preventively maintain the unit. Finally, we present a case study based on battery resistance data and demonstrate that our modeling procedure offers a maintenance policy that is superior to those from other competing models.

工业设备实时状态监测 (CM) 技术能力的不断增强，激发了人们对使用 CM 信号进行最佳维护计划的方法的极大兴趣。现有的维护政策制定方法认为退化是完全或部分可观察的。对于部分可观测性的更一般情况，通常假设潜在的退化过程与观测条件之间的关系是时不变的。在本文中，我们通过底层故障过程对观察到的 CM 信号进行建模来解决这一主要缺点，其中链接是时间相关的分段线性跳跃，然后利用部分观察马尔可夫决策过程 (POMDP) 来确定最佳维护策略。我们研究了策略的结构，并表明，在某些条件下，存在控制限制策略，即存在信念阈值，超过该阈值，最佳行动是预防性维护该单元。最后，我们提出了一个基于电池电阻数据的案例研究，并证明我们的建模程序提供了优于其他竞争模型的维护策略。