Joint optimization of sampling interval and control for condition-based maintenance using availability maximization criterion  

作　　者：LI Xin CAI Jing ZUO Hongfu LIU Ruochen CHEN Xi GUO Jiachen 

机构地区：[1]College of Civil Aviation,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China [2]School of Automobile and Traffic Engineering,Jiangsu University of Technology,Changzhou 213001,China [3]Shanghai Aircraft Customer Service Co.,Ltd.,Shanghai 200241,China

出　　处：《Journal of Systems Engineering and Electronics》2018年第1期203-215,共13页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China(51705221);the China Scholarship Council(201606830028);the Fundamental Research Funds for the Central Universities(NS2015072);the Funding of Jiangsu Innovation Program for Graduate Education(KYLX15 0313)

摘　　要：Most of the maintenance optimization models in condition-based maintenance(CBM) consider the cost-optimal criterion, but few papers have dealt with availability maximization for maintenance applications. A novel optimal Bayesian control approach is presented for maintenance decision making. The system deterioration evolves as a three-state continuous time hidden semi-Markov process. Considering the optimal maintenance policy, the multivariate Bayesian control scheme based on the hidden semi-Markov model(HSMM) is developed, the objective is to maximize the long-run expected average availability per unit time. The proposed approach can optimize the sampling interval and control limit jointly. A case study using Markov chain Monte Carlo(MCMC)simulation is provided and a comparison with the Bayesian control scheme based on hidden Markov model(HMM), the age-based replacement policy, Hotelling’s T2, multivariate exponentially weihted moving average(MEWMA) and multivariate cumulative sum(MCUSUM) control charts is given, which illustrates the effectiveness of the proposed method.Most of the maintenance optimization models in condition-based maintenance(CBM) consider the cost-optimal criterion, but few papers have dealt with availability maximization for maintenance applications. A novel optimal Bayesian control approach is presented for maintenance decision making. The system deterioration evolves as a three-state continuous time hidden semi-Markov process. Considering the optimal maintenance policy, the multivariate Bayesian control scheme based on the hidden semi-Markov model(HSMM) is developed, the objective is to maximize the long-run expected average availability per unit time. The proposed approach can optimize the sampling interval and control limit jointly. A case study using Markov chain Monte Carlo(MCMC)simulation is provided and a comparison with the Bayesian control scheme based on hidden Markov model(HMM), the age-based replacement policy, Hotelling's T^2, multivariate exponentially weihted moving average(MEWMA) and multivariate cumulative sum(MCUSUM) control charts is given, which illustrates the effectiveness of the proposed method.

关 键 词：condition-based maintenance(CBM) availability maximization Markov chain Monte Carlo(MCMC) hidden semiMarkov model(HSMM) Bayesian control sampling interval 

分 类 号：N945[自然科学总论—系统科学]