Operation-mode recognition of surface microdischarge based on visible image and deep learning

Abstract

Abstract Discharging images contain useful information regarding the operation mode of surface microdischarge (SMD). To solve the shortcomings of low efficiency, high cost, and long operation time of existing SMD operation-mode recognition methods, a convolutional neural network (CNN) based on deep learning is introduced herein. The visible image library of SMD at different applied voltages, dielectric sheets with different dielectric constants, and dielectric sheets with different thicknesses and exposure times are constructed using a digital camera. The typical structure of a CNN is discussed, and the hyperparameters, including the number of network layers, convolution kernel size, number of neurons in the fully connected layer, and activation function type that affect the recognition accuracy of the CNN are investigated. The optimal structure of the CNN for the SMD operation-mode recognition is obtained via training. The recognition accuracy of the CNN is compared with those of three traditional machine learning methods: support vector machine (SVM), decision tree (DT), and random forest (RF). Test results show that the recognition accuracy based on the CNN is 99.745%, which is better than those of the SVM, DT, and RF. Finally, an SMD operation-mode online recognition method based on the CNN is proposed.