关键词: 状态监控, 故障预测, 损伤标尺, 维修优化, 更新过程, Monte Carlo仿真

Abstract: Predictive maintenance based on prognostic information is an emerging maintenance mode which can decrease life cycle cost and increase operational availability efficiently. This paper focuses on prognostic approach based on canaries which can be divided into two categories: LRU-independent canaries and LRU-dependent canaries. Under perfect replacement assumption, a predictive maintenance decision model is proposed based on renewal reward theorem, which can evaluate the benefit of the use of canary devices from long-run average cost rate, average operational availability and average effectiveness-cost ratio. For LRU-independent canaries, prognostic distance and shape parameter of Weibull distribution are chosen to optimize maintenance decision, similarly for LRU-dependent canaries, accumulated damage factor and stochastic standard deviation are chosen as decision variables. Finally, this model is demonstrated with a numerical implementation example using Monte Carlo simulation. The results show that the predictive maintenance policy with LRU-independent canaries is better than corrective replacement policy but worse than age replacement policy, which is an essential characteristic. And the predictive maintenance policy with LRU-dependent canaries is better than age replacement policy in some conditions and the optimal parameters of canaries for maintenance decision are chosen.

Key words: condition monitoring, prognostics and health management, canary, maintenance optimization, Renewal process , Monte Carlo simulation