Abstract
In this paper, we optimize a dynamic condition-based maintenance policy for a slowly degrading system subject to soft failure and condition monitoring at equidistant, discrete time epochs. A random-coefficient autoregressive model with time effect is developed to describe the system degradation. The system age, previous state observations, and the item-to-item variability of the degradation are jointly combined in the proposed degradation model. Stochastic behavior for both the age-dependent and the statedependent term are considered, and a Bayesian approach for periodically updating the estimates of the stochastic coefficients is developed to combine information from a degradation database with real-time condition-monitoring information. Based on this degradation model, the dynamic maintenance policy is formulated and solved in a semi-Markov decision process framework. Incorporated with the same semi-Markov decision process framework is a novel approach for mean residual life estimation, which enables simultaneous residual life estimation with the optimization procedure. The effectiveness of using the proposed randomcoefficient autoregressive model with time effect rather than the existing fixed-coefficient ones to describe system degradation is demonstrated through a comparative study based on a real degradation dataset. The advantages of using a dynamic maintenance policy are also revealed
Highlights
To sustain high reliability is the goal of every system and product
In a typical Condition-based maintenance (CBM) policy, the health status of the system monitored throughout its operating life
To relax the Markovian assumption, [17] developed a degradation model based on a non-homogeneous semiMarkov process to de- scribe the deterioration of wear process in the turbofan engines
Summary
To sustain high reliability is the goal of every system and product. no matter how good the system design is, the performance of every system and product will deteriorate due to wear, fatigue, environmental conditions and other causes. To relax the Markovian assumption, [17] developed a degradation model based on a non-homogeneous semiMarkov process to de- scribe the deterioration of wear process in the turbofan engines Using these Markovian degradation models, the CBM optimization problem can be trans- formed into determining maintenance actions for all system states with different maintenance objectives considered, e.g., [4, 14, 18, 19]. The model coefficient updates have explicit formulas to allow fast computation in each update, which is an advantage of this model, and currently cannot be achieved by other existing age- and state-dependent degradation models Using this model, the procedure of estimating the mean residual life in [23] is no longer applicable, due to the fact that the explicit form for the failure time distribution is quite complicated to obtain in mathematical point of view.
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