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

Authors: Tawfik Thelaidjia; Abdelkrim Moussaoui; Salah Chenikher

Addresses: Laboratory of Electrical Engineering of Guelma (LGEG), University 8 May 1945 Guelma, Guelma, Algeria ' Laboratory of Electrical Engineering of Guelma (LGEG), University 8 May 1945 Guelma, Guelma, Algeria ' LABGET Laboratory, Larbi Tebessi University, Tebessa, Algeria

Abstract: 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.

Keywords: ball bearing; binary particle swarm optimisation; BPSO; discrete wavelet transform; DWT; data analysis; distance evaluation technique; DET; fault diagnosis; feature selection; scatter matrices.

DOI: 10.1504/IJDATS.2019.098817

International Journal of Data Analysis Techniques and Strategies, 2019 Vol.11 No.2, pp.115 - 132

Received: 17 Aug 2016
Accepted: 10 May 2017
Published online: 03 Apr 2019 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article