TY - JOUR
T1 - A Novel Lifetime Estimation Method for Two-Phase Degrading Systems
AU - Zhang, Jian Xun
AU - Hu, Chang Hua
AU - He, Xiao
AU - Si, Xiao Sheng
AU - Liu, Yang
AU - Zhou, Dong Hua
N1 - Funding Information:
Manuscript received August 29, 2017; revised December 21, 2017 and April 11, 2018; accepted April 20, 2018. Date of publication June 13, 2018; date of current version May 28, 2019. This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grants 61573365, 61025014, 61490701, 61751307, 61473094, and 61703244; and in part by the Research Fund for the Taishan Scholar Project of Shandong Province of China under Grant LZB2015-162. The work of X. He was supported by National Key Research and Development Program of China under Grant 2017YFA0700300, NSFC under Grants 61733009, 61522309, 61473163, and Special Fund of Suzhou-Tsinghua Innovation Leading Action under Grant 2016SZ0202. The work of X. S. Si was supported in part by the NSFC under Grant 61773386, in part by the Young Elite Scientists Sponsorship Program (YESS) of China Association for Science and Technology (CAST) under Grant 2016QNRC001. Associate Editor: Q. Miao. (Corresponding author: Dong-Hua Zhou.) J. X. Zhang, C. H. Hu, and X. S. Si is with the Department of Automation, Xi’an Research Institute of High-TechXi’an 710048, China (e-mail: [email protected]; [email protected]; [email protected]).
Publisher Copyright:
© 1963-2012 IEEE.
PY - 2019/6/1
Y1 - 2019/6/1
N2 - Due to the inner deteriorating mechanism or the mutant environmental stress, the degradation systems with multi-phase features have frequently been encountered in engineering practice. The key issue for prognostics of such systems is to account for the impact of the changing-point variability and the associated degradation state at this point on the progression of the degradation process. However, current studies usually treat the degradation state at the change point as a fixed value rather a random variable. Thus, it is still challenging to predict the lifetime of such multi-phase degrading systems. To this end, we first formulate a general degradation modeling framework based on a two-phase Wiener process. In prognostics, we take into full account the uncertainty of the degradation state at the changing point and then derive the analytical expressions of the lifetime and remaining useful life under the concept of the first passage time. The derived results are distinguished from existing results limited to the fixed state at the changing point. Furthermore, we extend our approach and results to cases with unit-to-unit variability and multiple phases. To facilitate the model implementation, we propose both offline and online methods for parameter identification, which make full use of the historical data and the in-service data. Finally, a numerical simulation and a practical case study are provided for illustration.
AB - Due to the inner deteriorating mechanism or the mutant environmental stress, the degradation systems with multi-phase features have frequently been encountered in engineering practice. The key issue for prognostics of such systems is to account for the impact of the changing-point variability and the associated degradation state at this point on the progression of the degradation process. However, current studies usually treat the degradation state at the change point as a fixed value rather a random variable. Thus, it is still challenging to predict the lifetime of such multi-phase degrading systems. To this end, we first formulate a general degradation modeling framework based on a two-phase Wiener process. In prognostics, we take into full account the uncertainty of the degradation state at the changing point and then derive the analytical expressions of the lifetime and remaining useful life under the concept of the first passage time. The derived results are distinguished from existing results limited to the fixed state at the changing point. Furthermore, we extend our approach and results to cases with unit-to-unit variability and multiple phases. To facilitate the model implementation, we propose both offline and online methods for parameter identification, which make full use of the historical data and the in-service data. Finally, a numerical simulation and a practical case study are provided for illustration.
KW - Degradation
KW - life prognostics
KW - multi-phase Wiener process
KW - reliability
KW - remaining useful life (RUL) estimation
UR - http://www.scopus.com/inward/record.url?scp=85048571111&partnerID=8YFLogxK
U2 - 10.1109/TR.2018.2829844
DO - 10.1109/TR.2018.2829844
M3 - Article
AN - SCOPUS:85048571111
VL - 68
SP - 689
EP - 709
JO - IEEE Transactions on Reliability
JF - IEEE Transactions on Reliability
SN - 0018-9529
IS - 2
M1 - 8384302
ER -