E3S Web Conf.
Volume 243, 2021The 5th International Conference on Power, Energy and Mechanical Engineering (ICPEME 2021)
|Number of page(s)||4|
|Section||Mechanical Engineering and Industrial Automation|
|Published online||11 March 2021|
Research on Defect Diagnosis Method of Reactor Acoustic Vibration Method Based on Deep Learning
1 China Electric Power Research Institute, High Voltage Research Institute, 15 Xiaoying East Road, Qinghe, Haidian District, Beijing, China
2 Electric Power Research Institute, Hebei Electric Power Co., Ltd, 238 South TIYU street, Shijiazhuang, China
3 Equipment Management Department, State Grid Corporation Limited, 86 West Chang'an Street, Xicheng District, Beijing, China
* Corresponding author:firstname.lastname@example.org
Although the state evaluation method based on characteristic parameters and weight factors can extract the characteristic quantities in time domain and frequency domain according to the collected acoustic and vibration signals of reactors, it is necessary to analyze a large number of test data to establish the functional relationship between the characteristic quantities and the defect states, and to establish the function relationship between the characteristic quantities and the defect states, and to establish the function relationship between the characteristic quantities and the defect states The method can directly learn the data samples, and self-study the correlation rules of characteristic parameters and defects through the training of neural network. In this paper, the deep learning neural network model is constructed, and the data obtained from reactor defect simulation experiment and field measurement are used as samples to train the deep learning network. Through the training of neural network, the characteristics of acoustic vibration signal are automatically learned, and the characteristics are stored in the parameters of neural network. Finally, the state of reactor is realized by the classifier at the end of the network assessment
© The Authors, published by EDP Sciences, 2021
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