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12 March 2019 Reliability analysis method of aerospace equipment system based on big data
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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%.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yufei Dou, Haihong Fang, Xiaoyan Ju, Hongjie Zhang, Tian Zhang, and Debiao Li "Reliability analysis method of aerospace equipment system based on big data", Proc. SPIE 11023, Fifth Symposium on Novel Optoelectronic Detection Technology and Application, 110234U (12 March 2019); https://doi.org/10.1117/12.2519534
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