A new approach to the analysis of water treeing using feature extraction of vented type water tree images

Abstract

In this study, vented type water trees were initiated and grown in laboratory environment. A smart test platform was used to accelerate the initiation and growth of vented type water trees. 6 kV/4 kHz voltage was applied to the specimens to initiate and grow water trees. Mel-frequency cepstral coefficients of the vented type water tree images are obtained after 2 h and 10 h of aging respectively. The insignificant regions in the vented type water tree images were removed by using morphological filtering method before MFCC feature extraction. Finally, the statistical values of these features were analyzed. Scatter plots of the standard deviations and mean values of the cepstral coefficients were plotted. As expected, it has been observed that the points in the scatter plot are clustered in a certain area. MFCC is a popular and frequently used feature extraction method in speech recognition, however there are some studies which employs MFCC as a successful feature extraction method in image processing applications. This study provides a new approach to the analysis of vented water treeing using image processing techniques. The other new approach is using MFCC as a feature extraction method in microscopic water tree images.