Abstract
A data-driven cloud test fault diagnosis method is proposed for the current testing system based on cloud computing, which has a low utilization rate of test data and fails to give full play to the operation and storage capacity of cloud computing. Firstly, the initial fuzzy reasoning fault diagnosis method is constructed based on expert knowledge and system parameters. Secondly, GSA is used to optimize the model based on historical data. Finally, the simulation platform is used for experimental verification. The results show that the system can effectively improve the utilization rate of cloud test data and achieve more accurate fault diagnosis.
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References
Xiao, M.Q., Zhao, Y, Huihui, X, Tang, X, Deng, J.: Exploration of cloud computing and its application in the field of testing. J. Air Force Eng. Univ. (Nat. Sci. Ed.) 16(01), 50–55 (2015)
Xiao, M.Q., Zhao, Y., Zhao, X.: Concept and application exploration of cloud testing. Comput. Meas. Control 24(01), 1–3 + 11 (2016)
Zhao, Y.: Research on cloud test resource virtualization and its optimal scheduling method. Air Force Engineering University (2018)
Guo, R., Zhao, X.: Development trend of automatic test system. Overseas Electron. Meas. Technol. 33(06), 1–4 (2014)
Zhou, Y., Jing, B., Zhang, J., Zhou, H.: Application model research of aircraft fault prediction and health management. Comput. Meas. Control 19(09), 2061–2063 + 2101 (2011)
Xin, L., Zhou, Y., Kong, Q., Zhao, Y.: Study on fault prediction of aerial equipment based on markov distance. Comput. Meas. Control 22(07), 2052–2054 + 2058 (2014)
Zhang, J., Feng, J., Li, Q., Lu, Q.: Study on fault diagnosis method of parallel test system based on fuzzy clustering. Comput. Meas. Control 17(01), 30–32 (2009)
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Kang, W., Xiao, J., Kong, X. (2020). Research on Data-Driven Fault Diagnosis Technology of Cloud Test. In: Jain, V., Patnaik, S., Popențiu Vlădicescu, F., Sethi, I. (eds) Recent Trends in Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 1006. Springer, Singapore. https://doi.org/10.1007/978-981-13-9406-5_28
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DOI: https://doi.org/10.1007/978-981-13-9406-5_28
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