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

doi:10.21629/JSEE.2018.01.21

Journal of Systems Engineering and Electronics ›› 2018, Vol. 29 ›› Issue (1): 203-215.doi: 10.21629/JSEE.2018.01.21

• Reliability • Previous Articles Next Articles

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

Xin LI¹(), Jing CAI^1,*(), Hongfu ZUO¹(), Ruochen LIU²(), Xi CHEN³(), Jiachen GUO¹()

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

Received:2017-01-16 Online:2018-02-26 Published:2018-02-23
Contact: Jing CAI E-mail:lixin1990@nuaa.edu.cn;caijing@nuaa.edu.cn;rms@nuaa.edu.cn;liuruochen_nuaa@163.com;chenxi1@comac.cc;gjc@nuaa.edu.cn
About author:LI Xin was born in 1990. He received his B.S. degree in College of Mechanical and Electrical Engineering from Zhengzhou Institute of Aeronautical Industry Management in 2012. He was a Ph.D. candidate at Nanjing University of Aeronautics and Astronautics (NUAA). His research interests are maintenance, system engineering and prognostic and health management (PHM). He is a visiting Ph.D. student in the condition-based maintenance lab at the University of Toronto from October 2016 to October 2017. E-mail: lixin1990@nuaa.edu.cn|CAI Jing was born in 1976. From 1995 to 1999, he studied for his B.A. degree in Nanjing University of Aeronautics and Astronautics (NUAA). From 2001 to 2007, he studied for his Ph.D. degree in NUAA. He is currently an associate professor in NUAA. His research interests include reliability statistics, maintenance theory, prognostic and health management (PHM). E-mail: caijing@nuaa.edu.cn|ZUO Hongfu was born in 1959. He received his Ph.D. degree from China University of Mining and Technology (CUMT) in 1989. He is currently a professor and Ph.D. student supervisor in Nanjing University of Aeronautics and Astronautics (NUAA). His research interests include reliability engineering, vehicle application engineering, man-machine maintainability and so on. E-mail: rms@nuaa.edu.cn|LIU Ruochen was born in 1989. He received his B.S. and Ph.D. degrees from the Nanjing University of Aeronautics and Astronautics (NUAA), in 2011 and 2016, respectively. He is currently a lecturer with the School of Automobile and Traffic Engineering, Jiangsu University of Technology. His main research interests include advanced sensors, theoretical model and experiment, state monitoring, and fault detection. E-mail: liuruochen_nuaa@163.com|CHEN Xi was born in 1988. He received his doctor's degree at Royal Melbourne Institute of Technology (RMIT University), Australia. Currently he is a postdoctoral research fellow in Shanghai Engineering Research Center of Civil Aircraft Health Monitoring, Shanghai Aircraft Customer Service Co., Ltd. His research interests include maintenance engineering analysis, diagnosis, and prognosis and health management. E-mail: chenxi1@comac.cc|GUO Jiachen was born in 1995. He received his B.S. degree from the Nanjing University of Aeronautics and Astronautics (NUAA) in 2017. He is now a Ph.D. student at NUAA. His research interests include maintenance engineering analysis, diagnosis, and prognosis and health management. E-mail: gjc@nuaa.edu.cn
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);This work was supported by the National Natural Science Foundation of China (51705221), the China Scholarship Council (201606830028), the Fundamental Research Funds for the Central Universities (NS2015072), and the Funding of Jiangsu Innovation Program for Graduate Education (KYLX15_0313)

Abstract

Abstract:

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.

Key words: condition-based maintenance (CBM), availability maximization, Markov chain Monte Carlo (MCMC), hidden semiMarkov model (HSMM), Bayesian control, sampling interval

Xin LI, Jing CAI, Hongfu ZUO, Ruochen LIU, Xi CHEN, Jiachen GUO. Joint optimization of sampling interval and control for condition-based maintenance using availability maximization criterion[J]. Journal of Systems Engineering and Electronics, 2018, 29(1): 203-215.

Figures/Tables 9

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References 36

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Optimal control limit	Optimal sampling interval	Average availability
0.364 8	0.505 7	0.608 1

Optimal control limit	Optimal sampling interval	Average availability
0.300 0	0.705 0	0.498 3

Policy	Bayesian (HSMM)	Bayesian (HMM)	Age-based	Hotelling's $T^2$	MEWMA	MCUSUM
Control limit	0.364 8	0.32	10.12	15.70	6.320	5.054
Number of CM	3	6	17	10	11	5
Number of PM	37	34	23	30	29	35
Number of false alarms	12	14	0	21	13	16
Average availability	0.608 1	0.512 4	0.461 0	0.536 3	0.489 2	0.490 1

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

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