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

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.

Generation of meter-scale nanosecond pulsed DBD and the intelligent evaluation based on multi-dimensional feature parameter extraction

“…Considering the complicated discharge characteristics of large-scale DBD under different experimental conditions, it is necessary to extract the discharge characteristic parameters and establish the correction between multi-dimensional discharge parameters and plasma characteristics [20,21]. Machine learning is an effective method that can independently learn the structure and internal patterns of input sample data, which has been applied in deep-going analysis of plasma properties [22], such as surface micro-discharge identification [23], trace gas detection [24], carbon dioxide methane reforming [25], etc. Therefore, extracting multidimensional characteristic parameters and establishing suitable diagnostic methods based on machine learning would provide a novel efficient approach for large-scale discharge diagnosis and evaluation.…”