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Degradation Data-Driven Remaining Useful Life Estimation in the Absence of Prior Degradation Knowledge
Joint Authors
Yu, Yong
Hu, Changhua
Zhang, Jianxun
Si, Xiaosheng
Source
Journal of Control Science and Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-12-03
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Electronic engineering
Information Technology and Computer Science
Abstract EN
Recent developments in prognostic and health management have been targeted at utilizing the observed degradation signals to estimate residual life distributions.
Current degradation models mainly focus on a population of “identical” devices or an individual device with population information, not a single component in the absence of prior degradation knowledge.
However, the fast development of science and technology provides us with many kinds of new systems, and we just have the real-time monitoring information to analyze the reliability for them.
The fusion algorithm presented herein addresses this challenge by combining the excellent modeling ability of Bayesian updating method for the multilevel data and the prominent estimation ability of ECM algorithm for incomplete data.
Residual life distributions and posterior distributions are first calculated through the Bayesian updating method based on random initial a priori distributions.
Then the a priori distributions are revised and improved for future predictions by the ECM algorithm.
Once a new signal is observed, we can reuse the fusion algorithm to improve the accuracy of residual life distributions.
The applicability of this fusion algorithm is validated by a set of simulation experiments.
American Psychological Association (APA)
Yu, Yong& Hu, Changhua& Si, Xiaosheng& Zhang, Jianxun. 2017. Degradation Data-Driven Remaining Useful Life Estimation in the Absence of Prior Degradation Knowledge. Journal of Control Science and Engineering،Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1173459
Modern Language Association (MLA)
Yu, Yong…[et al.]. Degradation Data-Driven Remaining Useful Life Estimation in the Absence of Prior Degradation Knowledge. Journal of Control Science and Engineering No. 2017 (2017), pp.1-11.
https://search.emarefa.net/detail/BIM-1173459
American Medical Association (AMA)
Yu, Yong& Hu, Changhua& Si, Xiaosheng& Zhang, Jianxun. Degradation Data-Driven Remaining Useful Life Estimation in the Absence of Prior Degradation Knowledge. Journal of Control Science and Engineering. 2017. Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1173459
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1173459