# Evaluation of Pressure Effect on Accelerated Aging Tests of Polymer-Insulated Aircraft Wires
## 3 The Proposed Experimental Method to Measure the Surface Discharges Activity
### Three Proposed Methods to Quantify the Degradation Process of the Twin Wire Samples  
The first method of quantifying the degradation of the wire samples is based on the SDIV measured by the CMOS sensor. It is expected that as the samples degrade over time, the SDIV value will decrease.  
Figure 2: Flowchart of the method for measuring surface release activity for each sample at each degradation cycle and pressure level.  
The second method is based on determining the intensity of the discharge images. Image processing software was programmed in the Matlab(r) R2022b environment to detect the SDIV and quantify the surface discharge intensity from the RGB images captured by the CMOS sensor. This process is summarized in Figure 3.  
First, a 25 s long exposure image is captured with the CMOS imaging sensor. The image is then imported into the Matlab(r) environment and converted into an RGB array. Each pixel of the array is represented by an RGB triplet whose coordinates are in the range of 0-255, 0-255, 0-255. Next, the area of surface discharge activity is identified and located by applying the color threshold filtering method, which consists of selecting certain pixels from the image that correspond to a specific color or range of colors corresponding to the characteristic wavelengths of the light emitted by the surface discharges (see Table 1).  
After applying the color threshold filter, the resulting array is a single-channel 2D array with 0-1 binary values. Pixels within the selected color range are represented by a value of 1, while pixels outside the range are represented by a value of 0. It is worth noting that image processing, such as color threshold filtering, operates at the single pixel level. Consequently, the resulting image may contain some degree of noise or random pixels that fall within the corona color range but are not actually indicative of surface discharge activity. To address this issue, pixel region analysis was applied to the filter pixel aggregates of a size smaller than 100 pixels. To quantify the intensity of the discharges, bounding boxes were applied to the image to detect and locate the specific regions of surface discharge activity. These bounding boxes allowed for the isolation of the areas identified by the color threshold filtering process, providing a clear visual representation of the corona affected areas within the image. Next, the pixels within the bounding boxes are converted to grayscale using the following transformation [33],  
\[I_{geyscale}(i,j)=0.299I_{R}(i,j)+0.587I_{G}(i,j)+0.114I_{B}(i,j) \tag{1}\]  
where \(I\) (\(i,j\)) \(\in\) (0, 255) is the intensity of each pixel in the \(i\)-th row and \(j\)-th column of the image matrix (\(i,j\)), while 0.299, 0.587, and 0.114 are the weights of the red, green, and blue channels, respectively, taking into account the luminous efficiency function of the average human eye.  
Figure 3: Flowchart of the surface discharge intensity detection and quantification method using the CMOS sensor. The red rectangles identify the area of surface discharge activity.  
After each region is transformed into a separate grayscale image, the intensity of the surface discharges can be calculated as follows [34],  
\[Intensity=\sum_{i=1}^{m}\sum_{j=1}^{n}I_{grayscale}(i,j) \tag{2}\]  
where \(m\) and \(n\) are the number of rows and columns in the image matrix.  
Finally, the third method of quantifying the insulation degradation of the wire samples is to directly measure the output signal provided by the solar blind UV sensor. It provides a binary on/off output signal with a frequency that rises with the intensity of the light falling in the solar blind region emitted by the discharges.