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Reliability analysis method of aerospace equipment system based on big data
Author(s): Yufei Dou; Haihong Fang; Xiaoyan Ju; Hongjie Zhang; Tian Zhang; Debiao Li
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Paper Abstract

Aerospace equipment is developing in the direction of high speed, accuracy, stability and long life. However, the reliability and life expectancy of some equipment systems are still far from the world advanced level. The main reason is that the data volume is not complete enough, and the data format is single. In order to ensure the healthy operation of these equipment systems, it is necessary to use a large amount of data to diagnose and predict faults. Therefore, the research from simple data collection to the whole process analysis of big data is of great significance for equipment life assessment and reliability analysis. Based on the analysis of big data, this paper proposes a processing method for equipment fault diagnosis and prediction, which is carried out from the aspects of aerospace big data characteristics, data acquisition, data storage, algorithm model and prediction, from complex equipment operation. The fault information is discovered and analyzed to ensure the stable and safe operation of the entire system. Finally, we use the cloud service platform method to simulate the life of the equipment. Compared with the traditional method, the long-term memory network model has been added to predict the full life cycle of the equipment by more than 10%.

Paper Details

Date Published: 12 March 2019
PDF: 5 pages
Proc. SPIE 11023, Fifth Symposium on Novel Optoelectronic Detection Technology and Application, 110234U (12 March 2019); doi: 10.1117/12.2519534
Show Author Affiliations
Yufei Dou, Beijing Institute of Space Long March Vehicle (China)
Haihong Fang, Beijing Institute of Space Long March Vehicle (China)
Xiaoyan Ju, Beijing Institute of Space Long March Vehicle (China)
Hongjie Zhang, Beijing Institute of Space Long March Vehicle (China)
Tian Zhang, Beijing Institute of Space Long March Vehicle (China)
Debiao Li, Beijing Institute of Space Long March Vehicle (China)


Published in SPIE Proceedings Vol. 11023:
Fifth Symposium on Novel Optoelectronic Detection Technology and Application
Qifeng Yu; Wei Huang; You He, Editor(s)

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