Contamination Grades Recognition of Ceramic Insulators Using Fused Features of Infrared and Ultraviolet Images

Abstract

In order to realize the non-contact measurement of ceramic insulator contamination severity, a method based on feature level fusion of infrared (IR) and ultraviolet (UV) image information is proposed in this paper. IR and UV images of artificially polluted insulators were obtained from high voltage experiments at 80%, 85% and 90% RH. After the preprocessing of images, IR and UV features were calculated, respectively. Then, feature selection based on Fisher criterion was adopted to gain features, which have the ability to distinguish different contamination grades effectively. In feature level fusion section, kernel principal component analysis (KPCA) was applied to the dimensionality reduction fusion of IR and UV features and obtain three-dimensional fused features. A particle swarm optimized back propagation neural network (PSO-BPNN) classifier was constructed and trained to recognize the contamination grades. Experimental results indicate that the feature level fusion of IR and UV information based on KPCA has capability to characterize the contamination grades comprehensively. Compared with recognition using IR or UV features separately, recognition based on the feature level fusion is more accurate and effective. This study provides a new methodology for the measurement of insulator contamination severity at working condition.

Keywords

• contamination grades
• infrared images
• ultraviolet images
• feature level fusion
• Fisher criterion
• kernel principal component analysis
• particle swarm optimization