基于多源信息的隐含非线性维纳退化过程剩余寿命预测

doi:10.7527/S1000-6893.2022.27662

Abstract

Abstract:

Accurate remaining useful life prediction is of huge significance in improving the reliability and safety of the system. Practical engineering often encounters imperfect or scarce prior degradation information， with the nonlinear degradation characteristics difficult to track by the linear degradation models. To solve this problem， we propose a remaining useful life prediction method for nonlinear degradation systems with measurement errors by reasonably fusing failure time data and multi-source information， based on the implicit nonlinear Wiener degradation process model. Firstly， we obtain the relationship between the degradation data and the nature of parameter estimation based on the implicit nonlinear Wiener degradation process by theoretical derivation， providing a theoretical basis for reasonable fusion of multi-source information. Secondly， according to the nature of parameter estimation， we use the field degradation data and historical degradation data to estimate the model fixed parameters of the two prediction cases respectively and fuse the failure time data into the degradation model by the Expectation Maximization （EM） algorithm. Then， the Kalman filtering algorithm is used to update online the drift parameter based on the field degradation data. Finally， simulation experiments are conducted to further verify the nature of parameter estimation， and two practical case studies are used to verify the superiority of the proposed method.

Key words: remaining useful life prediction, measurement error, implicit nonlinear Wiener process, multi-source information, Kalman filtering

CLC Number:

Jiaxin YANG, Shengjin TANG, Liang LI, Xiaoyan SUN, Shuai QI, Xiaosheng SI. Remaining useful life prediction of implicit nonlinear Wiener degradation process based on multi-source information[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(12): 227662-227662.

Figures/Tables 23

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Table 1

Prior parameters of M0 at different times

循环次数/10⁴	3	4	5	6	7	8	9
$μ λ$	7.275 4	7.388 4	6.743 9	6.991 0	7.287 8	8.631 9	10.147 6
$σ λ 2$	1.511 0	1.578 9	1.215 1	1.347 8	1.518 3	2.450 6	3.839 9
$σ B 2$	$4.7 × 10 - 16$	$8.5 × 10 - 13$	$6.3 × 10 - 16$	$1.8 × 10 - 17$	$7.8 × 10 - 15$	$7.7 × 10 - 14$	$1.7 × 10 - 16$
$b$	1.105 6	1.112 8	1.070 0	1.086 9	1.106 4	1.185 9	1.261 7
$σ ε 2$	$5.9 × 10 - 5$	$4.4 × 10 - 5$	$3.8 × 10 - 5$	$3.2 × 10 - 5$	$2.9 × 10 - 5$	$5.7 × 10 - 9$	$8.7 × 10 - 5$

Table 1

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References 46

1	YAN B， MA X， YANG L， et al. A novel degradation-rate-volatility related effect Wiener process model with its extension to accelerated ageing data analysis［J］. Reliability Engineering & System Safety， 2020， 204： 107138.
2	TANG S J， GUO X S， YU C Q， et al. Real time remaining useful life prediction based on nonlinear Wiener based degradation processes with measurement errors［J］. Journal of Central South University， 2014， 21（12）： 4509-4517.
3	HU J W， SUN Q Z， YE Z S， et al. Joint modeling of degradation and lifetime data for RUL prediction of deteriorating products［J］. IEEE Transactions on Industrial Informatics， 2021， 17（7）： 4521-4531.
4	李天梅，司小胜，张建勋. 多源传感监测线性退化设备数模联动的剩余寿命预测方法［J］. 航空学报， 2023， 44（8）： 227190.
	LI T M， SI X S， ZHANG J X. Data-model interactive remaining useful life prediction method for multi-sensor monitored linear stochastic degrading devices［J］. Acta Aeronautica et Astronautica Sinica， 2023， 44（8）： 227190 （in Chinese）.
5	HU G， XU Z Q， WANG G R， et al. Forecasting energy consumption of long-distance oil products pipeline based on improved fruit fly optimization algorithm and support vector regression［J］. Energy， 2021， 224： 120153.
6	LIU D， WANG S Q. Reliability estimation from lifetime testing data and degradation testing data with measurement error based on evidential variable and Wiener process［J］. Reliability Engineering & System Safety， 2021， 205： 107231.
7	WANG X， WANG B X， JIANG P H， et al. Accurate reliability inference based on Wiener process with random effects for degradation data［J］. Reliability Engineering & System Safety， 2020， 193： 106631.
8	ZHENG Z X， SI X S， HU C H， et al. Degradation data analysis and remaining useful life estimation： A review on Wiener-process-based methods［J］. European Journal of Operational Research， 2018， 271（3）： 775-796.
9	SI X S， WANG W B， HU C H， et al. A Wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation［J］. Mechanical Systems and Signal Processing， 2013， 35（1-2）： 219-237.
10	李天梅，司小胜，刘翔，等.大数据下数模联动的随机退化设备剩余寿命预测技术［J］.自动化学报，2022，48（9）：2119-2141.
	LI T M， SI X S， LIU X， et al. Data-model interactive remaining useful life prediction technologies for stochastic degrading devices with big data［J］. Acta Automatica Sinica，2022，48（9）：2119-2141 （in Chinese）.
11	GEBRAEEL N Z， LAWLEY M A， LI R， et al. Residual-life distributions from component degradation signals： A Bayesian approach［J］. IIE Transactions， 2005， 37（6）： 543-557.
12	BIAN L K， GEBRAEEL N. Computing and updating the first-passage time distribution for randomly evolving degradation signals［J］. IIE Transactions， 2012， 44（11）： 974-987.
13	GEBRAEEL N， ELWANY A， PAN J. Residual life predictions in the absence of prior degradation knowledge［J］. IEEE Transactions on Reliability， 2009， 58（1）： 106-117.
14	TSAI C C， TSENG S T， BALAKRISHNAN N. Mis-specification analyses of gamma and Wiener degradation processes［J］. Journal of Statistical Planning and Inference， 2011， 141（12）： 3725-3735.
15	PENG W， LI Y F， YANG Y J， et al. Inverse Gaussian process models for degradation analysis： A Bayesian perspective［J］. Reliability Engineering & System Safety， 2014， 130： 175-189.
16	任子强，司小胜，胡昌华，等. 融合多传感器数据的发动机剩余寿命预测方法［J］. 航空学报， 2019， 40（12）： 223312.
	REN Z Q， SI X S， HU C H， et al. Remaining useful life prediction method for engine combining multi-sensors data［J］. Acta Aeronautica et Astronautica Sinica， 2019， 40（12）： 223312 （in Chinese）.
17	LIU D， WANG S， ZHANG C. Reliability estimation by fusing multiple-source information based on evidential variable and Wiener process［J］. Computers & Industrial Engineering， 2021， 162： 107745.
18	董青，郑建飞，胡昌华，等.考虑随机冲击影响的自适应Wiener过程剩余寿命预测方法［J］.航空学报，2022，43（9）： 225914.
	DONG Q， ZHENG J F， HU C H， et al. Remaining useful life prediction for adaptive Wiener process method with random shock ［J］. Acta Aeronautica et Astronautica Sinica， 2022， 43（9）： 225914 （in Chinese）.
19	杨家鑫，唐圣金，李良，等. 基于隐含非线性维纳退化过程的剩余寿命预测［J/OL］. 北京航空航天大学学报，（2022-05-30）［2022-06-22］. .
	YANG J X， TANG S J， LI L， et al. Remaining useful life prediction based on implicit nonlinear Wiener degradation process ［J/OL］. Journal of Beijing University of Aeronautics and Astronautics，（2022-05-30）［2022-06-22］. （in Chinese）.
20	郑建飞，胡昌华，司小胜，等. 考虑不确定测量和个体差异的非线性随机退化系统剩余寿命估计［J］. 自动化学报， 2017， 43（2）： 259-270.
	ZHENG J F， HU C H， SI X S， et al. Remaining useful life estimation for nonlinear stochastic degrading systems with uncertain measurement and unit-to-unit variability［J］. Acta Automatica Sinica， 2017， 43（2）： 259-270 （in Chinese）.
21	SI X S， WANG W B， CHEN M Y， et al. A degradation path-dependent approach for remaining useful life estimation with an exact and closed-form solution［J］. European Journal of Operational Research， 2013， 226（1）： 53-66.
22	WANG W， CARR M， XU W， et al. A model for residual life prediction based on Brownian motion with an adaptive drift［J］. Microelectronics Reliability， 2011， 51（2）： 285-293.
23	WANG X， HU C H， SI X S， et al. An adaptive prognostic approach for newly developed system with three-source variability［J］. IEEE Access， 2019， 7： 53091-53102.
24	SI X S. An adaptive prognostic approach via nonlinear degradation modeling： Application to battery data［J］. IEEE Transactions on Industrial Electronics， 2015， 62（8）： 5082-5096.
25	HUANG Z Y， XU Z G， WANG W H， et al. Remaining useful life prediction for a nonlinear heterogeneous Wiener process model with an adaptive drift［J］. IEEE Transactions on Reliability， 2015， 64（2）： 687-700.
26	FENG L， WANG H L， SI X S， et al. A state-space-based prognostic model for hidden and age-dependent nonlinear degradation process［J］. IEEE Transactions on Automation Science and Engineering， 2013， 10（4）： 1072-1086.
27	TANG S J， YU C Q， WANG X， et al. Remaining useful life prediction of lithium-ion batteries based on the Wiener process with measurement error［J］. Energies， 2014， 7（2）： 520-547.
28	CAI Z Y， CHEN Y X， ZHANG Q， et al. Residual lifetime prediction model of nonlinear accelerated degradation data with measurement error［J］. Journal of Systems Engineering and Electronics， 2017， 28（5）： 1028-1038.
29	TANG S J， XU X D， YU C Q， et al. Remaining useful life prediction with fusing failure time data and field degradation data with random effects［J］. IEEE Access， 2019， 8： 11964-11978.
30	WANG L， PAN R， LI X， et al. A Bayesian reliability evaluation method with integrated accelerated degradation testing and field information［J］. Reliability Engineering & System Safety， 2013， 112： 38-47.
31	PANG Z N， SI X S， HU C H， et al. A Bayesian inference for remaining useful life estimation by fusing accelerated degradation data and condition monitoring data［J］. Reliability Engineering & System Safety， 2021， 208： 107341.
32	ZHANG Y， JIA X， GUO B. Bayesian framework for satellite rechargeable lithium battery synthesizing bivariate degradation and lifetime data［J］. Journal of Central South University， 2018， 25（2）： 418-431.
33	TANG S J， WANG W F， SUN X Y， et al. Unbiased parameters estimation and mis-specification analysis of Wiener process-based degradation model with random effects［J］. Applied Mathematical Modelling， 2022， 109： 134-160.
34	王凤飞，唐圣金，孙晓艳，等. 考虑随机效应的多源信息融合剩余寿命预测［J/OL］. 北京航空航天大学学报，（2022-03-01）［2022-06-22］. .
	WANG F F， TANG S J， SUN X Y， et al. Remaining useful life prediction based on multi source information with considering random effects ［J/OL］. Journal of Beijing University of Aeronautics and Astronautics，（2022-03-01）［2022-06-22］. （in Chinese）.
35	CAI Z Y， WANG Z Z， CHEN Y X， et al. Remaining useful lifetime prediction for equipment based on nonlinear implicit degradation modeling［J］. Journal of Systems Engineering and Electronics， 2020， 31（1）： 194-205.
36	SI X S， WANG W B， HU C H， et al. Estimating remaining useful life with three-source variability in degradation modeling［J］. IEEE Transactions on Reliability， 2014， 63（1）： 167-190.
37	TANG S J， GUO X S， ZHOU Z J. Mis-specification analysis of linear Wiener process-based degradation models for the remaining useful life estimation［J］. Proceedings of the Institution of Mechanical Engineers， Part O： Journal of Risk and Reliability， 2014， 228（5）： 478-487.
38	YANG J X， TANG S J， FANG P Y， et al. Remaining useful life prediction of implicit linear Wiener degradation process based on multi-source information［J/OL］. Proceedings of the Institution of Mechanical Engineers， Part O： Journal of Risk and Reliability，（2022-11-19）［2022-11-20］， .
39	YE Z S， WANG Y， TSUI K L， et al. Degradation data analysis using Wiener processes with measurement errors［J］. IEEE Transactions on Reliability， 2013， 62（4）： 772-780.
40	HAN Y Y， MA C L， TANG S J， et al. Residual life estimation of lithium-ion batteries based on nonlinear Wiener process with measurement error［J］. Proceedings of the Institution of Mechanical Engineers， Part O： Journal of Risk and Reliability， 2023， 237（1）： 133-151.
41	万昌豪，刘志国，唐圣金，等. 基于不完美先验信息的随机系数回归模型剩余寿命预测方法［J］. 北京航空航天大学学报， 2021， 47（12）： 2542-2551.
	WAN C H， LIU Z G， TANG S J， et al. Remaining useful life prediction method based on random coefficient regression model with imperfect prior information［J］. Journal of Beijing University of Aeronautics and Astronautics， 2021， 47（12）： 2542-2551 （in Chinese）.
42	PENG C Y， TSENG S T. Mis-specification analysis of linear degradation models［J］. IEEE Transactions on Reliability， 2009， 58（3）： 444-455.
43	HONG S， YUE T Y， LIU H. Vehicle energy system active defense： A health assessment of lithium-ion batteries［J］. International Journal of Intelligent Systems， 2022， 37（12）： 10081-10099.
44	JIN G， MATTHEWS D E， ZHOU Z. A Bayesian framework for on-line degradation assessment and residual life prediction of secondary batteries inspacecraft［J］. Reliability Engineering & System Safety， 2013， 113： 7-20.
45	LU C J， MEEKER W O. Using degradation measures to estimate a time-to-failure distribution［J］. Technometrics， 1993， 35（2）： 161-174.
46	WANG X， BALAKRISHNAN N， GUO B. Residual life estimation based on a generalized Wiener degradation process［J］. Reliability Engineering & System Safety， 2014， 124： 13-23.

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Remaining useful life prediction of implicit nonlinear Wiener degradation process based on multi-source information

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