Multivariable degradation modeling and life prediction using multivariate fractional Brownian motion

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Authors
Asgari, Ali
Si, Wujun
Yuan, Liang
Krishnan, Krishna K.
Wei, Wei
Advisors
Issue Date
2024
Type
Article
Keywords
Life prediction , Likelihood-ratio test , Long memory , Multivariable degradation , Multivariate fractional Brownian motion
Research Projects
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Citation
Asgari, A., Si, W., Yuan, L., Krishnan, K., Wei, W. Multivariable degradation modeling and life prediction using multivariate fractional Brownian motion. (2024). Reliability Engineering and System Safety, 248, art. no. 110146. DOI: 10.1016/j.ress.2024.110146
Abstract

In system prognostics and health management, multivariable degradation models have been widely developed to predict the life of complex systems using degradation data of multiple Performance Characteristics (PCs). Recent studies have detected a Long-Term Memory (LTM) effect among the degradation process of various PCs, implying a strong coupling phenomenon between the future degradation behavior and historical degradation trajectory. Although the LTM has been widely integrated into single-PC-based degradation modeling, it has not been considered in multi-PC-based scenarios. To capture LTM among multiple PCs, this article proposes a novel LTM-integrated Multivariate Degradation Model (MDM) for system life prediction based on multivariate fractional Brownian motion, which simultaneously incorporates the cross-correlation among different PCs. To estimate parameters of the LTM-integrated MDM, a maximum likelihood method is developed. Two likelihood-ratio hypothesis tests are developed to test the existence of the overall and individual LTM effect among multiple PCs. Both simulation studies and physical experiments on the performance degradation of solar energy conversion and storage devices are conducted to validate the proposed model. Results reveal that the proposed LTM-integrated MDM significantly outperforms existing MDMs in life prediction, while the lifetime uncertainty is heavily underestimated by those traditional approaches that neglect the LTM. © 2024 Elsevier Ltd

Table of Contents
Description
Publisher
Elsevier Ltd
Journal
Reliability Engineering and System Safety
Book Title
Series
PubMed ID
ISSN
0951-8320
EISSN