Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/94621
Title: | Health condition estimation of spacecraft key components using belief rule base | Authors: | Tang, X Wang, X Xiao, M Yung, KL Hu, B |
Issue Date: | 2021 | Source: | Enterprise information systems, 2021, v. 15, no. 8, p. 1107-1127 | Abstract: | This paper proposes a method for estimating the health status of spacecraft key components based on the belief rule base (BRB), a semi-quantitative method which uses both human judgmental information and numerical data. It not only allows experts to establish rules to provide useful conclusions, but also allows historical data to train its parameters to obtain more accurate outputs. To balance the parameter training and experts’ knowledge, the Markov Chain Monte Carlo (MCMC) technique instead of traditional optimization method is used to adjust the BRB parameter. A practical case of estimating the health condition of space application batteries is studied. | Keywords: | Belief rule base Health condition estimation Markov Chain Monte Carlo Spacecraft |
Publisher: | Taylor & Francis | Journal: | Enterprise information systems | ISSN: | 1751-7575 | EISSN: | 1751-7583 | DOI: | 10.1080/17517575.2019.1670361 | Rights: | © 2019 Informa UK Limited, trading as Taylor & Francis Group This is an Accepted Manuscript of an article published by Taylor & Francis in Enterprise Information Systems on 02 Oct 2019 (published online), available at: http://www.tandfonline.com/10.1080/17517575.2019.1670361. |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
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Yung_Health_Condition_Estimation.pdf | Pre-Published version | 1.14 MB | Adobe PDF | View/Open |
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