An effective feature selection method based on maximum class separability for fault diagnosis of ball bearing

The paper deals with the development of a novel feature selection approach for bearing fault diagnosis to overcome drawbacks of the distance evaluation technique (DET); one of the well-established feature selection approaches. Its drawbacks are the influence of its effectiveness by the noise and the selection of salient features regardless of the classification system. To overcome these shortcomings, an optimal discrete wavelet transform (DWT) is firstly used to decompose the bearing vibration signal at different decomposition depths to enhance the signal to noise ratio. Then, a combination of DET with binary particle swarm optimisation (BPSO) algorithm and a criterion based on scatter matrices employed as an objective function are suggested to improve the classification performances and to reduce the computational time. Finally, support vector machine is utilised to automate the identification of different bearing conditions. From the obtained results, the effectiveness of the suggested method is proven.