Remaining useful lifetime prediction for equipment based on nonlinear implicit degradation modeling

doi:10.21629/JSEE.2020.01.19

Journal of Systems Engineering and Electronics ›› 2020, Vol. 31 ›› Issue (1): 194-205.doi: 10.21629/JSEE.2020.01.19

• Reliability • Previous Articles Next Articles

Remaining useful lifetime prediction for equipment based on nonlinear implicit degradation modeling

Zhongyi CAI(), Zezhou WANG*(), Yunxiang CHEN(), Jiansheng GUO(), Huachun XIANG()

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

Received:2018-12-24 Online:2020-02-20 Published:2020-02-25
Contact: Zezhou WANG E-mail:afeuczy@163.com;wzz_4202@qq.com;cyx87793@163.com;amisc@163.com;xhc09260926@163.com
About author:CAI Zhongyi was born in 1988. He received his B.S. degree of management engineering in 2010 and 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 of Equipment Management and UAV Engineering College, Air Force Engineering University. His research interests are reliability assessment and remaining lifetime prediction. He has published two books and more than 20 research papers. E-mail: afeuczy@163.com|WANG Zezhou was born in 1992. He received his B.S. degree of management engineering in 2014 and M.S. degree of management science and engineering in 2016 from Air Force Engineering University. Now he is a doctoral student of Equipment Management and UAV Engineering College, Air Force Engineering University. His research interest is data-driven remaining useful lifetime prediction. He has published eight research papers. E-mail: wzz_4202@qq.com|CHEN Yunxiang was born in 1962. He received his M.S. degree from Air Force Engineering College 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. His research interests are reliability assessment, materiel maintenance support and materiel development & demonstration. He has published five books and more than 50 research papers. He is an expert of air force in reliability, maintenance and support. E-mail: cyx87793@163.com|GUO Jiansheng was born in 1965. He received his M.S. degree from Air Force Engineering College in 1991 and Ph.D. degree from Northwestern Polytechnical University in 2009. Now he is a professor of Equipment Management and UAV Engineering College, Air Force Engineering University. His research interests are system engineering, and complex system modeling. He has published three books and more than 40 research papers. E-mail: amisc@163.com|XIANG Huachun was born in 1980. He received his M.S. degree of management science and engineering in 2005 and Ph.D. degree of system engineering in 2009 from Air Force Engineering University. Now he is a vice professor and the director of Equipment Management and UAV Engineering College, Air Force Engineering University. His research interests are reliability design, reliability assessment and system engineering. He has published two books and 10 research papers. E-mail: xhc09260926@163.com
Supported by:
the National Defense Foundation of China(71601183);the National Defense Foundation of China(71901216);the China Postdoctoral Science Foundation(2017M623415);This work was supported by the National Defense Foundation of China (71601183; 71901216) and the China Postdoctoral Science Foundation (2017M623415)

Abstract

Abstract:

Nonlinearity and implicitness are common degradation features of the stochastic degradation equipment for prognostics. These features have an uncertain effect on the remaining useful life (RUL) prediction of the equipment. The current data-driven RUL prediction method has not systematically studied the nonlinear hidden degradation modeling and the RUL distribution function. This paper uses the nonlinear Wiener process to build a dual nonlinear implicit degradation model. Based on the historical measured data of similar equipment, the maximum likelihood estimation algorithm is used to estimate the fixed coefficients and the prior distribution of a random coefficient. Using the on-site measured data of the target equipment, the posterior distribution of a random coefficient and actual degradation state are step-by-step updated based on Bayesian inference and the extended Kalman filtering algorithm. The analytical form of the RUL distribution function is derived based on the first hitting time distribution. Combined with the two case studies, the proposed method is verified to have certain advantages over the existing methods in the accuracy of prediction.

Key words: remaining useful life (RUL) prediction, Wiener process, dual nonlinearity, measurement error, individual difference

Zhongyi CAI, Zezhou WANG, Yunxiang CHEN, Jiansheng GUO, Huachun XIANG. Remaining useful lifetime prediction for equipment based on nonlinear implicit degradation modeling[J]. Journal of Systems Engineering and Electronics, 2020, 31(1): 194-205.

Figures/Tables 10

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Model	$u_\lambda $	$\sigma _\lambda ^2 $	$b$	$\beta $	$\sigma_B^2 $	$\sigma ^2$	$ - \ln L(\cdot)$	AIC	TMSE
Actual value	1	0.062 5	1.5	0.02	0.04	0.09	/	/	/
M1	0.945	0.058 7	1.51	0.019	0.068	0.084	19.24	50.48	0.108
M2	0.845	0.361 1	1	0.025	0.106	0.139	29.07	68.14	0.890
M3	0.921	0.102 0	1.47	/	0.056	0.154	23.76	57.52	0.318

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Remaining useful lifetime prediction for equipment based on nonlinear implicit degradation modeling

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