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Integrated System Health Management‐based Fuzzy On‐board Condition Prediction for Manned Spacecraft Avionics
Author(s) -
Xu Jiuping,
Meng Zhiyi,
Xu Lei
Publication year - 2016
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.1736
Subject(s) - avionics , spacecraft , fuzzy logic , reliability engineering , gray (unit) , reliability (semiconductor) , health management system , computer science , engineering , aerospace , process (computing) , systems engineering , artificial intelligence , aerospace engineering , medicine , power (physics) , physics , alternative medicine , pathology , quantum mechanics , radiology , operating system
The purpose of this study is to develop a prediction methodology for a condition assessment of on‐board integrated health management systems in manned spacecraft avionics. The framework of this study is based on two main premises. The first is the need to examine the problems in the on‐board prediction tools in space avionics integrated system health management, an area that has been rarely focused on. The second is the need to consider the failure correlation coefficients to enable accurate health predictions. To deal with the uncertainty in the prediction process, a fuzzy theory–gray model–support vector machine approach, which uses fuzzy theory combined with a gray model and a support vector machine, is used to make the prediction. An example is given to demonstrate the accuracy and reliability of the model. Copyright © 2014 John Wiley & Sons, Ltd.