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

收稿日期:2020-11-26 接受日期:2022-02-24 出版日期:2022-05-06 发布日期:2022-05-06

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).

摘要/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

. [J]. Journal of Systems Engineering and Electronics, 2022, 33(2): 474-488.

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.

图/表 9

参考文献 39

1	WINOKUR H S, GOLDSTEIN L J Analysis of mission-oriented systems. IEEE Trans. on Reliability, 1969, 18 (4): 144- 148. doi: 10.1109/TR.1969.5216341
2	LIU B, XIE M, XU Z G, et al An imperfect maintenance policy for mission-oriented systems subject to degradation and external shocks. Computers & Industrial Engineering, 2016, 102, 21- 32.
3	ZHAO X, SUN J L, QIU Q A, et al Optimal inspection and mission abort policies for systems subject to degradation. European Journal of Operational Research, 2021, 292 (2): 610- 621. doi: 10.1016/j.ejor.2020.11.015
4	ZHAO X, FAN Y, QIU Q A, et al. Multi-criteria mission abort policy for systems subject to two-stage degradation process. European Journal of Operational Research, 2021, 295(1): 233–245.
5	QIU Q A, CUI L R Gamma process based optimal mission abort policy. Reliability Engineering & System Safety, 2019, 190, 106496.
6	QIU Q A, KOU M, CHEN K, et al Optimal stopping problems for mission oriented systems considering time redundancy. Reliability Engineering & System Safety, 2021, 205, 107226.
7	CHENG G Q, ZHOU B H, LI L Integrated production, quality control and condition-based maintenance for imperfect production systems. Reliability Engineering & System Safety, 2018, 175, 251- 264.
8	ABDELHAKIM K, CLAVER D, EL-HOUSSAINE A, et al Integrated production quality and condition-based maintenance optimisation for a stochastically deteriorating manufacturing system. International Journal of Production Research, 2019, 57 (8): 2480- 2497. doi: 10.1080/00207543.2018.1521021
9	WU Z Y, GUO B, AXITA, et al A dynamic condition-based maintenance model using inverse Gaussian process. IEEE Access, 2020, 8, 104- 117. doi: 10.1109/ACCESS.2019.2958137
10	GUO C M, WANG W B, GUO B, et al A maintenance optimization model for mission-oriented systems based on Wiener degradation. Reliability Engineering & System Safety, 2013, 111, 183- 194.
11	LI J F, CHEN Y X, XIANG H C, et al Remaining useful life prediction for aircraft engine based on LSTM-DBN. Systems Engineering and Electronics, 2020, 42 (7): 1637- 1644.
12	WANG Z Z, CHEN Y X, CAI Z Y, et al Methods for predicting the remaining useful life of equipment in consideration of the random failure threshold. Journal of Systems Engineering and Electronics, 2020, 31 (2): 415- 431. doi: 10.23919/JSEE.2020.000018
13	CAI Z Y, WANG Z Z, CHEN Y X, et al Remaining useful lifetime prediction for equipment based on nonlinear implicit degradation modeling. Journal of Systems Engineering and Electronics, 2020, 31 (1): 194- 205.
14	WANG Z Z, CHEN Y X, CAI Z Y, et al Remaining useful lifetime prediction based on nonlinear degradation processes with random failure threshold. Journal of National University of Defense Technology, 2020, 42 (2): 177- 185.
15	CAI Z Y, WANG Z Z, ZHANG X F, et al Online prediction method of remaining useful life for implicit nonlinear degradation equipment. Systems Engineering and Electronics, 2020, 42 (6): 1410- 1416.
16	WANG Z Z, CHEN Y X, CAI Z Y, et al Remaining useful lifetime online prediction based on accelerated degradation modeling with the proportion relationship. Systems Engineering and Electronics, 2021, 43 (2): 584- 592.
17	SHIN J H, JUN H B On condition based maintenance policy. Journal of Computational Design & Engineering, 2015, 2 (2): 119- 127.
18	ALASWAD S, XIANG Y S A review on condition-based maintenance optimization models for stochastically deteriorating system. Reliability Engineering & System Safety, 2017, 157, 54- 63.
19	CHEN Y X, WANG Z Z, CAI Z Y. Optimal maintenance decision based on remaining useful lifetime prediction for the equipment subject to imperfect maintenance. IEEE Access, 2020, 8: 6704–6716.
20	CHEN N, YE Z S, XIANG Y S, et al Condition-based maintenance using the inverse Gaussian degradation model. European Journal of Operational Research, 2015, 243 (1): 190- 199. doi: 10.1016/j.ejor.2014.11.029
21	YE Z S, CHEN N The inverse Gaussian process as a degradation model. Technometrics, 2014, 56 (3): 302- 311. doi: 10.1080/00401706.2013.830074
22	MA X Y, LIU B, YANG L, et al. Reliability analysis and condition-based maintenance optimization for a warm standby cooling system. Reliability Engineering & System Safety, 2020, 193: 106588.
23	WANG Z Z, CHEN Y X, CAI Z Y, et al Optimal replacement strategy considering equipment remaining useful lifetime prediction information under the influence of uncertain failure threshold. Journal of National University of Defense Technology, 2021, 43 (1): 145- 154.
24	PHUC D, VOISIN A, LEVRAT E, et al A proactive condition-based maintenance strategy with both perfect and imperfect maintenance actions. Reliability Engineering & System Safety, 2015, 133, 22- 32.
25	CABALLE N C, CASTRO I T, PEREZ C J, et al A condition-based maintenance of a dependent degradation-threshold-shock model in a system with multiple degradation processes. Reliability Engineering & System Safety, 2015, 134, 98- 109.
26	WASAN M T. On an inverse Gaussian process. Annals of Mathematical Statistics, 1967, 38(2): 638–645.
27	WANG X, XU D H An inverse Gaussian process model for degradation data. Technometrics, 2010, 52 (2): 188- 197. doi: 10.1198/TECH.2009.08197
28	DONG W J, LIU S F, BAE S J, et al A multi-stage imperfect maintenance strategy for multi-state systems with variable user demands. Computers & Industrial Engineering, 2020, 145, 106508.
29	ZHOU Y, KOU G, XIAO H, et al Sequential imperfect preventive maintenance model with failure intensity reduction with an application to urban buses. Reliability Engineering & System Safety, 2020, 198, 106871.
30	ZHANG M M, GAUDOIN O, XIE M Degradation-based maintenance decision using stochastic filtering for systems under imperfect maintenance. European Journal of Operational Research, 2015, 245 (2): 531- 541. doi: 10.1016/j.ejor.2015.02.050
31	LIU G H, CHEN S K, JIN H, et al Optimum imperfect inspection and maintenance scheduling model considering delay time theory. Journal of Zhejiang University (Engineering Science), 2020, 54 (7): 1298- 1307.
32	PEI H, HU C H, SI X S, et al Remaining life prediction information-based maintenance decision model for equipment under imperfect maintenance. Acta Automatica Sinica, 2018, 44 (4): 719- 729.
33	CAI Z Y, CHEN Y X, LI S L, et al Residual lifetime prediction method with random degradation and information fusion. Journal of Shanghai Jiao Tong University, 2016, 50 (11): 1778- 1783.
34	CAI Z Y, CHEN Y X, GUO J S, et al Remaining lifetime prediction for device with measurement error and random effect. Systems Engineering and Electronics, 2019, 41 (7): 1658- 1664.
35	PAN D H, LIU J B, CAO J D. Remaining useful life estimation using an inverse Gaussian degradation model. Neurocomputing, 2016, 185: 64–72.
36	SI X S, WANG W B, HU C H, et al Estimating remaining useful life with three-source variability in degradation modeling. IEEE Trans. on Reliability, 2014, 63 (1): 167- 190. doi: 10.1109/TR.2014.2299151
37	WANG Z Z, CHEN Y X, CAI Z Y, et al Real-time prediction of remaining useful lifetime for equipment with random failure threshold. Systems Engineering and Electronics, 2019, 41 (5): 1162- 1168.
38	ELWANY A H, GEBRAEEL N Z, MAILLART L M Structured replacement policies for components with complex degradation processes and dedicated sensors. Operations Research, 2011, 59 (3): 684- 695. doi: 10.1287/opre.1110.0912
39	LIAO H T, ELSAYED E A, CHAN L Y Maintenance of continuously monitored degrading systems. European Journal of Operational Research, 2006, 175 (2): 821- 835. doi: 10.1016/j.ejor.2005.05.017

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

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

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