Abstract
Through BP neural network, a nonlinear mapping model between feature information and diagnosis result is proposed. The method is applied in diagnosing fault of equipment functionally significant instrument, which can fuse all kinds of information in the running process of the functionally significant instrument. For the supervisory architecture, first-sitting-weight and first-sitting-threshold for BP neural network are used. Genetic algorithm is applied in neural network optimization. At last, the method is applied in fault diagnosis of a transmission system. Through comparison between diagnosis results and real results, the method is validated.
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Zan, X., Zhang, Sx., Gao, H., Zhang, Y., Han, Cs. (2018). Research on the Fault Diagnosis Method of Equipment Functionally Significant Instrument Based on BP Neural Network. In: Zuo, M., Ma, L., Mathew, J., Huang, HZ. (eds) Engineering Asset Management 2016. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-62274-3_31
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DOI: https://doi.org/10.1007/978-3-319-62274-3_31
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