TY - JOUR
T1 - Residual life prediction for complex systems with multi-phase degradation by ARMA-filtered hidden Markov model
AU - Sheng, Zhidong
AU - Hu, Qingpei
AU - Liu, Jian
AU - Yu, Dan
N1 - Funding Information:
This work was partly supported by the National Center for Mathematics and Interdisciplinary Sciences (CAS) and Key Laboratory of Systems and Control (CAS).
Publisher Copyright:
© 2017, © 2017 International Chinese Association of Quantitative Management.
PY - 2019/1/2
Y1 - 2019/1/2
N2 - The performance of certain critical complex systems, such as the power output of ground photovoltaic (PV) modules or spacecraft solar arrays, exhibits a multi-phase degradation pattern due to the redundant structure. This pattern shows a degradation trend with multiple jump points, which are mixed effects of two failure modes: a soft mode of continuous smooth degradation and a hard mode of abrupt failure. Both modes need to be modeled jointly to predict the system residual life. In this paper, an autoregressive moving average model-filtered hidden Markov model is proposed to fit the multi-phase degradation data with unknown number of jump points, together with an iterative algorithm for parameter estimation. The comprehensive algorithm is composed of non-linear least-square method, recursive extended least-square method, and expectation–maximization algorithm to handle different parts of the model. The proposed methodology is applied to a specific PV module system with simulated performance measurements for its reliability evaluation and residual life prediction. Comprehensive studies have been conducted, and analysis results show better performance over competing models and more importantly all the jump points in the simulated data have been identified. Also, this algorithm converges fast with satisfactory parameter estimates accuracy, regardless of the jump point number.
AB - The performance of certain critical complex systems, such as the power output of ground photovoltaic (PV) modules or spacecraft solar arrays, exhibits a multi-phase degradation pattern due to the redundant structure. This pattern shows a degradation trend with multiple jump points, which are mixed effects of two failure modes: a soft mode of continuous smooth degradation and a hard mode of abrupt failure. Both modes need to be modeled jointly to predict the system residual life. In this paper, an autoregressive moving average model-filtered hidden Markov model is proposed to fit the multi-phase degradation data with unknown number of jump points, together with an iterative algorithm for parameter estimation. The comprehensive algorithm is composed of non-linear least-square method, recursive extended least-square method, and expectation–maximization algorithm to handle different parts of the model. The proposed methodology is applied to a specific PV module system with simulated performance measurements for its reliability evaluation and residual life prediction. Comprehensive studies have been conducted, and analysis results show better performance over competing models and more importantly all the jump points in the simulated data have been identified. Also, this algorithm converges fast with satisfactory parameter estimates accuracy, regardless of the jump point number.
KW - System reliability
KW - hidden Markov model
KW - multi-phase degradation
KW - residual life prediction
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U2 - 10.1080/16843703.2017.1335496
DO - 10.1080/16843703.2017.1335496
M3 - Article
AN - SCOPUS:85020659296
VL - 16
SP - 19
EP - 35
JO - Quality Technology and Quantitative Management
JF - Quality Technology and Quantitative Management
SN - 1684-3703
IS - 1
ER -