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

doi:10.23919/JSEE.2022.000047

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (2): 474-488.doi: 10.23919/JSEE.2022.000047

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

Jingfeng LI(), Yunxiang CHEN(), Zhongyi CAI*(), Zezhou WANG()

¹ Equipment Management and UAV Engineering College, Air Force Engineering University, Xi’an 710051, China

Received:2020-11-26 Accepted:2022-02-24 Online:2022-05-06 Published:2022-05-06
Contact: Zhongyi CAI E-mail:ljf653483717@163.com;653483717@qq.com;afeuczy@163.com;350276267@qq.com
About author:|LI Jingfeng was born in 1993. He received his B.S. degree in management engineering in 2016 and M.S. degree in management science and engineering from Air Force Engineering University in 2018. He is currently pursuing his Ph.D. degree in management science and engineering at Equipment Management and UAV Engineering College, Air Force Engineering University. His research interests include remaining useful lifetime prediction, reliability assessment, and equipment maintenance decision. E-mail: ljf653483717@163.com||CHEN Yunxiang was born in 1962. He received his M.S. degree from Air Force Engineering University in 1989 and Ph.D. degree from Northwestern Polytechnical University in 2005. Now he is a professor of Equipment Management and UAV Engineering College, Air Force Engineering University, Xi’an, China. His research interests include reliability assessment, material maintenance support, and material development and demonstration. E-mail: 653483717@qq.com||CAI Zhongyi was born in 1988. He received his B.S. degree of management engineering in 2010, M.S. degree of management science and engineering in 2012, and Ph.D. degree of management science and engineering in 2016 from Air Force Engineering University. Now he is a lecturer at Equipment Management and UAV Engineering College, Air Force Engineering University, Xi’an, China. His research interests include reliability assessment and remaining lifetime prediction. E-mail: afeuczy@163.com||WANG Zezhou was born in 1992. He received his B.S. degree in automation in 2014 and M.S. degree of management science and engineering in 2016 from Air Force Engineering University. He is currently pursuing his Ph.D. degree in management science and engineering at Equipment Management and UAV Engineering College, Air Force Engineering University, Xi’an, China. His research interests include data-driven remaining useful lifetime prediction, reliability assessment, and equipment maintenance decision. E-mail: 350276267@qq.com
Supported by:
This work was supported by the National Natural Science Foundation of China (71901216).

Abstract

Abstract:

An effective maintenance policy optimization model can reduce maintenance cost and system operation risk. For mission-oriented systems, the degradation process changes dynamically and is monotonous and irreversible. Meanwhile, the risk of early failure is high. Therefore, this paper proposes a dynamic condition-based maintenance (CBM) optimization model for mission-oriented system based on inverse Gaussian (IG) degradation process. Firstly, the IG process with random drift coefficient is used to describe the degradation process and the relevant probability distributions are obtained. Secondly, the dynamic preventive maintenance threshold (DPMT) function is used to control the early failure risk of the mission-oriented system, 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 renewal 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 effectiveness and application value of the optimization model.

Key words: inverse Gaussian (IG) process, imperfect preventive maintenance (PM), mission-oriented system, dynamic preventive maintenance threshold (DPMT), maintenance optimization

Jingfeng LI, Yunxiang CHEN, Zhongyi CAI, Zezhou WANG. A dynamic condition-based maintenance optimization model for mission-oriented system based on inverse Gaussian degradation process[J]. Journal of Systems Engineering and Electronics, 2022, 33(2): 474-488.

Figures/Tables 9

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References 39

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Parameter		Initial value (0%)	?80%	?60%	?40%	?20%	+20%	+40%	+60%	+80%
Optimal expected cost ratio/(yuan·h^?1)	${C_I}$	7.52	7.06	7.18	7.29	7.41	7.64	7.75	7.87	7.98
	${C_P}$		6.55	6.80	7.04	7.28	7.76	8.01	8.25	8.49
	${C_R}$		2.93	4.08	5.23	6.37	8.67	9.82	10.96	12.11
	${C_C}$		3.51	6.44	7.52	7.52	7.52	7.52	7.52	7.52
Difference from initial value/(yuan·h^?1)	${C_I}$	?	?0.46	?0.34	?0.23	?0.11	0.12	0.23	0.35	0.46
	${C_P}$		?0.97	?0.72	?0.48	?0.24	0.24	0.49	0.73	0.97
	${C_R}$		?4.59	?3.44	?2.29	?1.15	1.15	2.30	3.44	4.59
	${C_C}$		?4.01	?1.08	0	0	0	0	0	0
SC /%	${C_I}$	?	7.65	7.54	7.65	7.31	7.98	7.65	7.76	7.65
	${C_P}$		16.12	15.96	15.96	15.96	15.96	16.29	16.18	16.12
	${C_R}$		76.30	76.24	76.13	76.46	76.46	76.46	76.24	76.30
	${C_C}$		66.66	23.94	0	0	0	0	0	0

Parameter		Initial value (0%)	?80%	?60%	?40%	?20%	+20%	+40%	+60%	+80%	+100%	+200%	+300%	+400%	+500%	+600%	+700%	+800%	+900%
Optimal expected cost ratio/(yuan·h^?1)	$\mu _\beta ^0$	?	7.52	7.52	7.52	7.52	7.52	7.51	7.50	7.49	7.47	7.29	6.76	5.29	4.63	4.46	4.36	4.26	4.21
Optimal expected cost ratio/(yuan·h^?1)	${\sigma _\beta }$	7.52	7.49	7.50	7.51	7.52	7.52	7.52	7.52	7.52	7.52	7.52	7.52	7.52	7.52	7.52	7.52	7.52	7.52
Difference from initial value/(yuan·h^?1)	$\mu _\beta ^0$	?	0	0	0	0	0	?0.01	?0.02	?0.03	?0.05	?0.23	?0.76	?2.23	?2.89	?3.06	?3.16	?3.26	?3.31
Difference from initial value/(yuan·h^?1)	${\sigma _\beta }$	?	?0.03	?0.02	?0.01	0	0	0	0	0	0	0	0	0	0	0	0	0	0
SC /%	$\mu _\beta ^0$	?	0	0	0	0	0	?0.33	?0.44	?0. 50	?0. 66	?1.53	?3.37	?7.41	?7.69	?6.78	?6.00	?5.42	?4.89
SC /%	${\sigma _\beta }$	?	0.50	0. 44	0. 33	0	0	0	0	0	0	0	0	0	0	0	0	0	0
c	$\mu _\beta ^0$	?	6.2	6.2	6.2	6.2	6.2	6.2	6.2	6.2	6.2	6.2	5.2	4.5	3.0	2.5	2.0	1.5	1.0
c	${\sigma _\beta }$	6.2	6.2	6.2	6.2	6.2	6.2	6.2	6.2	6.2	6.2	6.2	6.2	6.2	6.2	6.2	6.2	6.2	6.2
Difference from c	$\mu _\beta ^0$	?	0	0	0	0	0	0	0	0	0	0	?1.0	?1.7	?3.2	?3.7	?4.2	?4.7	?5.2
Difference from c	${\sigma _\beta }$	?	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
SC/%	$\mu _\beta ^0$	?	0	0	0	0	0	0	0	0	0	0	?5.38	?6.86	?10.32	?9.95	?9.68	?9.48	?9.32
SC/%	${\sigma _\beta }$	?	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0

Parameter	Initial value (0%)	?4%	?3%	?2%	?1%	+1%	+2%	+3%	+4%
Optimal expected cost ratio/(yuan·h^?1)	7.52	10.21	10.21	10.21	7.83	7.37	7.18	6.91	6.75
Difference from initial value/(yuan·h^?1)	?	2.69	2.69	2.69	0.31	?0.15	?0.34	?0.61	?0.77
SC /%	?	?849.57	?1132.76	?1699.14	?391.62	?189.49	?214.76	?256.87	?243.18
c	6.2	9.5	9.5	9.5	7.5	5.5	4.5	3.0	2.0
Difference from c	?	3.3	3.3	3.3	1.3	?0.7	?1.7	?3.2	?4.2
SC /%	?	?1264.11	?1685.48	?2528.23	?1991.94	?1072.58	?1302.42	?1634.41	?1608.87

[1]	Weining MA, Fei ZHAO, Xin LI, Qiwei HU, Bingcong SHANG. Joint optimization of inspection-based and age-based preventive maintenance and spare ordering policies for single-unit systems [J]. Journal of Systems Engineering and Electronics, 2022, 33(5): 1268-1280.
[2]	Changyou Li, Minqiang Xu, Song Guo, and Rixin Wang. Multiobjective maintenance optimization of the continuously monitored deterioration system [J]. Journal of Systems Engineering and Electronics, 2010, 21(5): 791-795.

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

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