Methods for predicting the remaining useful life of equipment in consideration of the random failure threshold

doi:10.23919/JSEE.2020.000018

Journal of Systems Engineering and Electronics ›› 2020, Vol. 31 ›› Issue (2): 415-431.doi: 10.23919/JSEE.2020.000018

• Reliability • Previous Articles Next Articles

Methods for predicting the remaining useful life of equipment in consideration of the random failure threshold

Zezhou WANG(), Yunxiang CHEN(), Zhongyi CAI(), Yangjun GAO*(), Lili WANG()

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

Received:2019-03-13 Online:2020-04-30 Published:2020-04-30
Contact: Yangjun GAO E-mail:350276267@qq.com;cyx87793@163.com;afeuczy@163.com;greisy2008@gmail.com;8574886@qq.com
About author:WANG Zezhou was born in 1992. He received his B.S. degree in automation and M.S. degree in management science and engineering from Air Force Engineering University, in 2014 and 2016, respectively. Now he is a doctoral student in management science and engineering at Equipment Management & UAV Engineering College, Air Force Engineering University. His research interests include data-driven remaining useful life prediction, reliability assessment and equipment maintenance decision. E-mail: 350276267@qq.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 & UAV Engineering College, Air Force Engineering University. His research interests include reliability assessment, material maintenance support, and material development & demonstration. E-mail: cyx87793@163.com|CAI Zhongyi was born in 1988. He received his B.S. degree in management engineering in 2010, and M.S. and Ph.D. degrees of management science and engineering in 2012 and 2016 from Air Force Engineering University, respectively. Now he is a lecturer of Equipment Management & UAV Engineering College, Air Force Engineering University. His research interests include reliability assessment and remaining life prediction. E-mail: afeuczy@163.com|GAO Yangjun was born in 1988. He received his B.S. degree in measurement and control technology and instrumentation program from Wuhan University of Technology in 2011 and M.S. degree in control theory and control engineering from Air Force Engineering University in 2014. He is pursuing his Ph.D. degree in systems engineering at Air Force Engineering University. His research interests include the analysis of controllability of complex networks, swarm intelligence algorithm and equipment maintenance decision. E-mail: greisy2008@gmail.com|WANG Lili was born in 1981. She received her B.S. degree in computer science from Air Force Engineering University in 2003, and M.S. and Ph.D. degrees of management science and engineering from Air Force Engineering University in 2006 and 2009, respectively. Now she is a vice professor of Equipment Management & UAV Engineering College, Air Force Engineering University. Her research interests include reliability design, reliability assessment, and system engineering. E-mail: 8574886@qq.com
Supported by:
the China Postdoctoral Science Foundation(2017M623415);This work was supported by the China Postdoctoral Science Foundation (2017M623415)

Abstract

Abstract:

The value range of the failure threshold will generate an uncertain influence on the prediction results for the remaining useful life (RUL) of equipment. Most of the existing studies on the RUL prediction assume that the failure threshold is a fixed value, as they have difficulty in reflecting the random variation of the failure threshold. In connection with the inadequacies of the existing research, an in-depth analysis is carried out to study the effect of the random failure threshold (RFT) on the prediction results for the RUL. First, a nonlinear degradation model with unit-to-unit variability and measurement error is established based on the nonlinear Wiener process. Second, the expectation-maximization (EM) algorithm is used to solve the estimated values of the parameters of the prior degradation model, and the Bayesian method is used to iteratively update the posterior distribution of the random coefficients. Then, the effects of three types of RFT constraint conditions on the prediction results for the RUL are analyzed, and the probability density function (PDF) of the RUL is derived. Finally, the degradation data of aero-turbofan engines are used to verify the correctness and advantages of the method.

Key words: remaining useful life (RUL) prediction, random failure threshold (RFT), nonlinear Wiener process, measurement error, unit-to-unit variability

Zezhou WANG, Yunxiang CHEN, Zhongyi CAI, Yangjun GAO, Lili WANG. Methods for predicting the remaining useful life of equipment in consideration of the random failure threshold[J]. Journal of Systems Engineering and Electronics, 2020, 31(2): 415-431.

Figures/Tables 13

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References 51

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Unit	FT
Item 01	2.253 2
Item 02	2.405 8
Item 03	2.867 9
Item 04	2.441 6
Item 05	2.511 1
Item 06	2.493 7
Item 07	2.034 7
Item 08	2.247 9
Item 09	2.286 8
Item 10	2.173 2
Item 11	2.506 8

Distribution assumption	p	H₀
Normal distribution	0.743 2	0
Weibull distribution	0.509 5	0
Index distribution	0.000 6	1
Poisson distribution	0.000 6	1
Rayleigh distribution	0.003 1	1

Parameter estimation method	${\hat \mu _\omega }$	$\hat \sigma _\omega ^2$
MLE	2.383 9	0.049 3
LSE1	2.383 9	0.075 8
LSE2	2.383 9	0.062 8
LSE3	2.383 9	0.053 9

Engine	CM time	Actual RUL	M0	M1	M2	M3
	155	15	[12.8, 22.8]	[9.1, 24.0]	[9.5, 30.3]	[10.0, 29.3]
No.1	160	10	[10.5, 19.0]	[6.8, 27.8]	[7.1, 26.8]	[7.5, 25.8]
	165	2	[7.8, 15.0]	[4.1, 31.3]	[4.3, 23.0]	[4.5, 22.0]
	120	28	[31.0, 49.3]	[21.4, 53.85]	[22.4, 52.8]	[23.5, 51.8]
No.4	130	18	[23.8, 39.3]	[14.6, 44.3]	[15.2, 43.3]	[16.0, 42.3]
	140	8	[13.0, 25.5]	[4.3, 31.8]	[4.5, 30.8]	[4.8, 29.3]
	155	23	[20.0, 30.5]	[12.1, 34.0]	[12.6, 33.0]	[13.3, 32.0]
No.11	165	13	[13.1, 21.3]	[5.5, 25.0]	[5.7, 24.0]	[6.0, 23.0]
	175	3	[7.5, 13.8]	[1.4, 20.0]	[1.4, 19.0]	[1.5, 16.0]

[1]	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.
[2]	Zhongyi Cai, Yunxiang Chen, Qiang Zhang, and Huachun Xiang. Residual lifetime prediction model of nonlinear accelerated degradation data with measurement error [J]. Systems Engineering and Electronics, 2017, 28(5): 1028-1038.

Methods for predicting the remaining useful life of equipment in consideration of the random failure threshold

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