Abstract
With the increase of aerospace launch density, the stability of the firing range measurement and control system and the network communication system in the range is particularly important. The potential failure of the range information system needs more attention, including the aging of the line, the destruction of animals, human damage, and the influence of viruses. Electromagnetic interference, etc., may cause serious problems such as delay in launching missions, errors in receiving and monitoring signals, and inability to issue satellite in-orbit control commands, even causing major accidents involving star destruction. In order to adapt to the load capacity during the high-density task period, to enhance the cognitive ability of the new load launch, and to improve the ability of the range to perform difficult tasks, it is necessary to accurately diagnose and maintain the launch system of the space range.
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Bowler S. SpaceX success. Astron Geophys. 2014;55(1):1.9. https://doi.org/10.1093/astrogeo/atu026.
Kou H, Chen P. An ensemble signature-based approach for performance diagnosis in big data platform. In: 2018 IEEE symposium on service-oriented system engineering (SOSE), Bamberg. 2018, p. 106–15.
Chen M, Yang J, Zhou J, Hao Y, Zhang J, Youn CH. 5G-smart diabetes: Toward personalized diabetes diagnosis with healthcare big data clouds. IEEE Commun Mag. 2018;56(4):16–23.
Xu Y, Sun Y, Wan J, Liu X, Song Z. Industrial big data for fault diagnosis: Taxonomy, review, and applications. IEEE Access. 2017;5:17368–80.
Benayas F, Carrera A, Iglesias CA. Towards an autonomic Bayesian fault diagnosis service for SDN environments based on a big data infrastructure. In: 2018 fifth international conference on software defined systems (SDS), Barcelona, 2018, p. 7–13.
Wang B, Xiao X, Xu Y, Li Y. Research on fault diagnosis of power equipment based on big data. In: 2017 IEEE international conference on energy internet (ICEI), Beijing, 2017, p. 193–7.
Gao Y, et al. Review of wireless big data in 5G: From physical layer to application layer. In: 2016 2nd IEEE international conference on computer and communications (ICCC), Chengdu, 2016, p. 23–7.
Babar MI, Jehanzeb M, Ghazali M, Jawawi DNA, Sher F, Ghayyur SAK. Big data survey in healthcare and a proposal for intelligent data diagnosis framework. In: 2016 2nd IEEE international conference on computer and communications (ICCC), Chengdu, 2016, p. 7–12.
Hu H, Tang B, Gong X, Wei W, Wang H. Intelligent fault diagnosis of the high-speed train with big data based on deep neural networks. IEEE Trans Industr Inf. 2017;13(4):2106–16.
Ren R, Jia Z, Wang L, Zhan J, Yi T. BDTUne: Hierarchical correlation-based performance analysis and rule-based diagnosis for big data systems. In: 2016 IEEE international conference on big data (Big Data), Washington, DC, 2016, p. 555–62.
Pasupathi C, Kalavakonda V. Evidence based health care system using big data for disease diagnosis. In: 2016 2nd international conference on advances in electrical, electronics, information, communication and bio-informatics (AEEICB), Chennai, 2016, p. 743–7.
Zhang J-H, Zou Q. Group learning analysis and individual learning diagnosis from the perspective of big data. In: 2016 IEEE international conference on cloud computing and big data analysis (ICCCBDA), Chengdu, 2016, p. 15–1.
Gao Y, Ao H, Wang K, Zhou W, Li Y. The diagnosis of wired network malfunctions based on big data and traffic prediction: An overview. In: 2015 4th international conference on computer science and network technology (ICCSNT), Harbin, 2015, p. 1204–8.
Acknowledgements
This work is funded by National Natural Science Foundation of China (61701503), the work of Su Hu was jointly supported by the MOST Program of International S&T Cooperation (Grant No. 2016YFE0123200), National Natural Science Foundation of China (Grant No. 61471100/61101090/61571082), Science and Technology on Electronic Information Control Laboratory (Grant No. 6142105040103) and Fundamental Research Funds for the Central Universities (Grant No. ZYGX2015J012/ZYGX2014Z005). We would like to thank all the reviewers for their kind suggestions to this work.
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Gao, Y. et al. (2020). Big Data-Based Precise Diagnosis in Space Range Communication Systems. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_70
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DOI: https://doi.org/10.1007/978-981-13-6504-1_70
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