Research on Discharge Sound Recognition Based on Machine Learning and Convolutional Neural Network Training Algorithm
Support Vector Machine (SVM) is a machine learning method based on statistical learning theory and structural risk minimization principle. The selection of many parameters directly affects the performance of SVM. SVM can be used as a classifier for estimating sound source position, and the anti-noise ability of the algorithm can be improved by selecting appropriate parameters. Convolutional neural network (CNN) can directly obtain effective information from the original image, omitting the processes of preprocessing, feature extraction and data reconstruction of the original image, and is highly invariant to displacement, scaling and other forms of distortion. By setting different solver parameters, network structure and the number of training samples, the results of defect recognition are compared and analyzed, and it is found that the improved Alexnet network has strong adaptive learning ability, which provides a new idea for pattern recognition in DC cable fault diagnosis.
2021 IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2021
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Shen, Yifan; Liu, Yuanzhe; Ye, Wenhuan; Zhang, Yiming; Qian, Juncheng; and Wang, Yuqiao, "Research on Discharge Sound Recognition Based on Machine Learning and Convolutional Neural Network Training Algorithm" (2021). Kean Publications. 918.