A dynamic condition-based maintenance optimization model for mission-oriented system based on inverse Gaussian degradation process

An effective maintenance policy optimization model can reduce maintenance cost and system operation risk. For mission-oriented systems, the degradation process changes dy-namically and is monotonous and irreversible. Meanwhile, the risk of early failure is high. Therefore, this paper proposes a dy-namic condition-based maintenance (CBM) optimization model for mission-oriented system based on inverse Gaussian (IG) de-gradation process. Firstly, the IG process with random drift coef-ficient is used to describe the degradation process and the re-levant probability distributions are obtained. Secondly, the dy-namic preventive maintenance threshold (DPMT) function is used to control the early failure risk of the mission-oriented sys-tem, and the influence of imperfect preventive maintenance (PM) on the degradation amount and degradation rate is analysed comprehensively. Thirdly, according to the mission availability requirement, the probability formulas of different types of rene-wal policies are obtained, and the CBM optimization model is constructed. Finally, a numerical example is presented to verify the proposed model. The comparison with the fixed PM threshold model and the sensitivity analysis show the effective-ness and application value of the optimization model.