Neighbors Class Solidarity Feature Selection for Fault Diagnosis of Brushless Generator Using Thermal Imaging

This article aims to propose a thermography-based fault detection approach for a brushless synchronous generator. In this approach, the energy of 2-D wavelet coefficients is utilized to extract features from thermal images. In order to select the appropriate wavelet basis function and decomposition level (DL), a novel filter feature selection called neighbors class solidarity (NCS) is propounded. To this purpose, the energy of the approximation coefficients is obtained for 65 wavelet basis functions and 12 DLs. Subsequently, the best feature is selected by using NCS. The selected feature is then applied to $k$ -nearest neighbors and support vector machine in order to classify the images into three operational states. These states include healthy, one diode open-circuit fault of the diode rectifier, and one open-phase fault of the exciter armature, while the tests are conducted under three different load conditions for each state. Results imply the efficiency of the proposed approach for the fault diagnosis in the brushless synchronous generator with an accuracy of 95.1%. To evaluate the performance of the NCS, it is compared with other conventional filter feature selection methods, and also for different test analysis.