###### Abstract  
Ultraviolet (UV) imaging technology has been used in corona discharge detection by characterizing the discharge degree through the detected number of photons in the UV imager or facular area of the UV image. This paper is to expand the UV imaging method in the electrical equipment's fault detection and improve the accuracy of its detection. First, an image segmentation algorithm based on Gaussian function color mapping is proposed for the segmentation of colored facular areas in the UV image, and its effectiveness is proved. Second, the testing results for a high-voltage insulator's discharge are used to fit the distance attenuation function of the facular area. The relationship between the voltage and the attenuation function coefficient is obtained by analyzing the experimental results. Finally, by analyzing actual inspection data of insulators in a substation, it is found that different defect types of insulators have different characteristics of changes in the facular area during the discharge. Based on the characteristics, accurate direct detection by using UV imaging can be realized for the defect types of discharging insulators.  
ultraviolet imaging; insulator discharge; fault detection; image segmentation; color mapping 2020 IEEEexample: date 10 <ccs2010> <concept  
The UV imager is not an electronic measurement device and cannot directly measure the degree of corona discharge, as this requires measuring the photon numbers within the facular area [10]. Experimental findings in [1] demonstrate that the photon number initially increases and then decreases with the gain of the UV imager. Conversely, the facular area consistently enlarges with the increase in the UV imager's gain. Therefore, the facular area is more appropriate for characterizing the discharge volume than the photon volume, facilitating the automatic identification of insulation equipment defects.  
Many researchers have conducted extensive experiments to characterize the discharge capacity of insulation devices through the light spot area. Typically, the discharge area in the UV imager is set to white during experimental detection of insulator discharge. However, in actual device status detection, the background environment is complex. To more accurately differentiate the facular area from the light spot area, the UV imager's color parameter is often set to other colors. A challenging problem that arises from the calculation of the facular area is how to segment the facular area from a UV image [11; 12; 13; 14]. This complex issue also limits the development of UV imaging technology in the field of intelligent monitoring of power equipment.  
To address the challenge of segmenting special color spots in UV imaging technology, this paper proposes an image segmentation algorithm by using the Gaussian function to perform the color processing in the image segmentation, and the algorithm can smoothly adjust the grayscale of the faculae area through the Gaussian function. Also, the UV image is segmented by a threshold segmentation algorithm. Experiments show that the proposed segmentation algorithm is effective and robust. By analyzing the experimental results, we can find the changing trends of the facular area under varying detection distances and voltages. What is more, by analyzing the actual inspection data of insulators from a real substation, it is observed that different defect types of insulators can lead to different characteristics of the facular area during the discharge. The analysis results show that it is feasible to directly detect the defect types of discharge insulators by using UV images. The proposed algorithm can be used to improve the detection accuracy of the UV imaging method and also expand the application of the UV imaging method in the fault detection of electrical equipment.