Reliability assessment considering stress drift and shock damage caused by stress transition shocks in a dynamic environment

doi:10.21629/JSEE.2019.05.18

Journal of Systems Engineering and Electronics ›› 2019, Vol. 30 ›› Issue (5): 1025-1034.doi: 10.21629/JSEE.2019.05.18

收稿日期:2018-07-03 出版日期:2019-10-08 发布日期:2019-10-09

Reliability assessment considering stress drift and shock damage caused by stress transition shocks in a dynamic environment

Tingting HUANG(), Bo PENG*(), Yuepu ZHAO(), Zixuan YU()

School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China

Received:2018-07-03 Online:2019-10-08 Published:2019-10-09
Contact: Bo PENG E-mail:htt@buaa.edu.cn;pengb@buaa.edu.cn;zhao@buaa.edu.cn;yuzixx@buaa.edu.cn
About author:HUANG Tingting was born in 1981. She is an assistant professor for the School of Reliability and Systems Engineering, Beihang University, China. She worked as a postdoctoral for the Department of Industrial Engineering, Tsinghua University in 2011. She recieved her Ph.D. degree from the School of Reliability and Systems Engineering, Beihang University in 2010. She recieved her M.S. degree from the Department of Industrial and Systems Engineering, Virginia Tech in 2014. She was a visiting scholar in the Department of Industrial and Systems Engineering, Rutgers University, USA in 2008. Her research interests are accelerated life testing, accelerated degradation testing and other reliability and environment testing technology. Her recent work is on degradation modeling and reliability prediction of products in a dynamic environment considering shocks. E-mail: htt@buaa.edu.cn|PENG Bo was born in 1986. He is currently working towards his Ph.D. degree in the School of Reliability and Systems Engineering, Beihang University. His research interests are degradation modeling and lifetime prediction for degradation tests. E-mail: pengb@buaa.edu.cn|ZHAO Yuepu was born in 1996. He is currently working towards his Ph.D. degree in the School of Reliability and Systems Engineering, Beihang University. His research interests are degradation modeling and lifetime prediction for degradation tests. E-mail: yuepu zhao@buaa.edu.cn|YU Zixuan was born in 1993. She is currently working towards her Ph.D. degree in the School of Reliability and Systems Engineering, Beihang University. Her research interests are multivariable degradation modeling and lifetime prediction. E-mail: yuzixx@buaa.edu.cn
Supported by:
the National Natural Science Foundation of China(NSFC71601009);the Technical Foundation Program from the Ministry of Industry and Information Technology of China(JSZL2015601B010);This work was supported by the National Natural Science Foundation of China (NSFC71601009) and the Technical Foundation Program from the Ministry of Industry and Information Technology of China (JSZL2015601B010)

摘要/Abstract

Abstract:

Products are often subject to dynamic environmental conditions in field use. When stress transition occurs, products may be exposed to instantaneous shocks that result in shock damages to the products, causing a permanent change of the degradation signals. Meanwhile, under some conditions, instantaneous shocks also lead to stress drift, causing a temporary change of the degradation signals. In this paper, a degradation model is proposed to assess the reliability and predict the residual lifetime of products operating in a dynamic environment considering shock damage and stress drift. The model is established based on a Wiener process which combines a stress-dependent degradation rate function, a shock damage function and a stress drift function in response to the dynamic environment. The shock damage function is established as a linear function of the stress transition start level and the stress level increment. The stress drift function is established as the difference value of a specified function at the stress transition start and end levels. A simulation study is presented to demonstrate the application of the model, and a case study for miniature light bulbs is used to validate the effectiveness of the proposed model.

Key words: degradation modeling, dynamic environment, stress drift, shock damage, Wiener process

. [J]. Journal of Systems Engineering and Electronics, 2019, 30(5): 1025-1034.

Tingting HUANG, Bo PENG, Yuepu ZHAO, Zixuan YU. Reliability assessment considering stress drift and shock damage caused by stress transition shocks in a dynamic environment[J]. Journal of Systems Engineering and Electronics, 2019, 30(5): 1025-1034.

图/表 18

参考文献 35

1	SI X, WANG W, HU C, et al. A Wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation. Mechanical Systems & Signal Processing, 2013, 35 (1/2): 219- 237.
2	WANG X. Wiener processes with random effects for degradation data. Journal of Multivariate Analysis, 2010, 101 (2): 340- 351. doi: 10.1016/j.jmva.2008.12.007
3	WANG X, BALAKRISHNAN N, GUO B. Residual life estimation based on a generalized Wiener degradation process. Reliability Engineering & System Safety, 2014, 124, 13- 23.
4	NOORTWIJK J M. A survey of the application of gamma processes in maintenance. Reliability Engineering & System Safety, 2009, 94 (1): 2- 21.
5	LAWLESS J, CROWDER M. Covariates and random effects in a gamma process model with application to degradation and failure. Lifetime Data Analysis, 2004, 10 (3): 213- 227. doi: 10.1023/B:LIDA.0000036389.14073.dd
6	SHU Y, FENG Q, KAO E P C. Levy-driven non-gaussian Ornstein-Uhlenbeck processes for degradation-based reliability analysis. AIIE Transactions, 2016, 48 (11): 993- 1003.
7	XUE L, LI N, LEI Y. Incipient fault detection for rolling element bearings under varying speed conditions. Materials, 2017, 10 (6): 675- 684. doi: 10.3390/ma10060675
8	LIAO B, SUN B, YAN M. Time-variant reliability analysis for rubber O-ring seal considering both material degradation and random load. Materials, 2017, 10 (10): 1211- 1220. doi: 10.3390/ma10101211
9	LIAO H, TIAN Z. A framework for predicting the remaining useful life of a single unit under time-varying operating conditions. IIE Transactions, 2013, 45 (9): 964- 980. doi: 10.1080/0740817X.2012.705451
10	XU Z, HONG Y, JIN R. Nonlinear general path models for degradation data with dynamic covariates. Applied Stochastic Models in Business and Industry, 2016, 32 (2): 153- 167. doi: 10.1002/asmb.2129
11	GEBRAEEL N, PAN J. Prognostic degradation models for computing and updating residual life distributions in a timevarying environment. IEEE Trans. on Reliability, 2008, 57 (4): 539- 550. doi: 10.1109/TR.2008.928245
12	BIAN L, GEBRAEEL N. Stochastic methodology for prognostics under continuously varying environmental profiles. Statistical Analysis & Data Mining, 2012, 6 (3): 260- 270.
13	ZHAI Q Q, YE Z S. Degradation in common dynamic environments. Technometrics, 2017, 60 (4): 461- 471.
14	FLORY J, KHAROUFEH J, GEBRAEEL N. A switching diffusion model for lifetime estimation in randomly varying environments. IIE Transactions, 2014, 46 (11): 1227- 1241. doi: 10.1080/0740817X.2014.893400
15	JIN G, MATTHEWS D, FAN Y, et al. Physics of failure-based degradation modeling and lifetime prediction of the momentum wheel in a dynamic covariate environment. Engineering Failure Analysis, 2013, 28 (3): 222- 240.
16	HUANG J, GOLUBOVI D S, KOH S. Degradation modeling of mid-power white-light LEDs by using Wiener process. Optics Express, 2015, 23 (15): A966. doi: 10.1364/OE.23.00A966
17	ZHAI Q, YE Z S. RUL prediction of deteriorating products using an adaptive wiener process model. IEEE Trans. on Industrial Informatics, 2017, PP (99): 1.
18	HONG Y, DUAN Y, MEEKER W Q. Statistical methods for degradation data with dynamic covariates information and an application to outdoor weathering data. Technometrics, 2015, 47 (3): 830- 837.
19	PENG W, LI Y F, YANG Y J. Bayesian degradation analysis with inverse gaussian process models under time-varying degradation rates. IEEE Trans. on Reliability, 2017, 66 (1): 84- 96. doi: 10.1109/TR.2016.2635149
20	LI Q, GAO Z, TANG D. Remaining useful life estimation for deteriorating systems with time-varying operational conditions and condition-specific failure zones. Chinese Journal of Aeronautics, 2016, 29 (3): 662- 674. doi: 10.1016/j.cja.2016.04.007
21	LIU T, SUN Q, FENG J. Residual life estimation under timevarying conditions based on a Wiener process. Journal of Statistical Computation and Simulation, 2017, 87 (2): 211- 226. doi: 10.1080/00949655.2016.1202953
22	RAFIEE K, FENG Q, COIT D W. Reliability analysis and condition-based maintenance for failure processes with degradation-dependent hard failure threshold. Quality and Reliability Engineering International, 2017, 33 (7): 1351- 1366. doi: 10.1002/qre.2109
23	HAO S, YANG J, MA X. Reliability modeling for mutually dependent competing failure processes due to degradation and random shocks. Applied Mathematical Modelling, 2017: S0307904X17304067.
24	RAFIEE K, FENG Q, COIT D W. Reliability modeling for dependent competing failure processes with changing degradation rate. IIE Transactions, 2014, 46 (5): 483- 496. doi: 10.1080/0740817X.2013.812270
25	HAO S, YANG J. Reliability analysis for dependent competing failure processes with changing degradation rate and hard failure threshold levels. Computers&Industrial Engineering, 2018:S0360835218300871.
26	RAFIEE K, FENG Q, COIT D W. Reliability assessment of competing risks with generalized mixed shock models. Reliability Engineering&System Safety, 2017, 159, 1- 11.
27	BIAN L, GEBRAEEL N, KHAROUFEH J P. Degradation modeling for real-time estimation of residual lifetimes in dynamic environment. AIIE Transactions, 2015, 47 (5): 471- 486.
28	WANG X, WU W, FANG Z, et al. Temperature drift compensation for hemispherical resonator gyro based on natural frequency. Sensors, 2012, 12 (5): 6434- 6446. doi: 10.3390/s120506434
29	XU G P, TIAN W F, QIAN L. EMD and SVM based temperature drift modeling and compensation for a Dynamically Tuned Gyroscope (DTG). Mechanical Systems & Signal Processing, 2007, 21 (8): 3182- 3188.
30	XU G P, TIAN W F, JIN Z H, et al. Temperature drift modelling and compensation for a dynamically tuned gyroscope by combining WT and SVM method. Measurement Science & Technology, 2007, 18 (5): 1425- 1432.
31	HONG S. Compensation of nonlinear thermal bias drift of Resonant Rate Sensor using fuzzy logic. Sensors and Actuators A (Physical), 1999, 78 (2-3): 143- 148. doi: 10.1016/S0924-4247(99)00241-1
32	WANG L Z, JIANG T M, LI X Y, et al. A pre-processing method for degradation parameter. Proc. of the IEEE Prognostics and System Health Management Conference, 2012: 1-4.
33	BARAS T, JACOB A F. Temperature drift compensation technique for a hybrid LTCC oscillator at 20GHz. Proc. of the European Microwave Integrated Circuits Conference, 2009: 467-470.
34	HUANG T, PENG B, COIT D W, et al. Degradation modeling and lifetime prediction considering effective shocks in a dynamic environment. IEEE Trans. on Reliability, 2019, 68 (3): 819- 830. doi: 10.1109/TR.2019.2917058
35	DANIELS H E. Approximating the first crossing-time density for a curved boundary. Bernoulli, 1996, 2 (2): 133- 143. doi: 10.2307/3318547

$\alpha $	$\beta $	$\gamma $	$\lambda $	$a$	$b$	$\sigma $	$D$
9	3	3	2.5	0.1	1.5	2.8	2 000

$\alpha $	$\beta $	$\gamma $	$\lambda $	$a$	$b$	$\sigma $
8.86	2.98	2.93	2.56	0.098	1.491	2.73

Method	$\alpha $	$\beta $	$\gamma $	$\lambda $	$a$	$b$	$\sigma $
Bias	0.002 2	-0.006 5	0.005 5	0.003 6	-0.003 3	0.002 4	0.004 1
SD	0.026 5	0.042 3	0.058 8	0.046 7	0.032 2	0.045 5	0.032 2
RMSE	0.029 9	0.044 2	0.062 6	0.045 2	0.028 7	0.042 1	0.035 1

$\alpha $	$\beta $	$\gamma $	$\lambda $	$a$	$b$	$\sigma $
0.003 43	0.000 06	0.512	0.048	2.67$\times $10$^{ - 5}$	1.98	0.2$\times $10$^{ - 4}$

Reliability assessment considering stress drift and shock damage caused by stress transition shocks in a dynamic environment

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 18

参考文献 35

相关文章 0

编辑推荐

Metrics

本文评价