An Improved ABC Algorithm and Its Application in Bearing Fault Diagnosis with EEMD

• Published in: Algorithms Vol. 12; no. 4; p. 72
• Main Authors: Chen, Weijia, Xiao, Yancai
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2019
• Subjects: bearing fault diagnosis; Blindness; chaos initialization; Decomposition; EEMD; Fault diagnosis; Honey; improved artificial bee colony; Levy flight; Neighborhoods; Noise; Optimization algorithms; Search algorithms; Search process; Signal processing; Standard deviation; Swarm intelligence; Wavelet transforms; White noise
• ISSN: 1999-4893; 1999-4893
• DOI: 10.3390/a12040072

The Ensemble Empirical Mode Decomposition (EEMD) algorithm has been used in bearing fault diagnosis. In order to overcome the blindness in the selection of white noise amplitude coefficient e in EEMD, an improved artificial bee colony algorithm (IABC) is proposed to obtain it adaptively, which providing a new idea for the selection of EEMD parameters. In the improved algorithm, chaos initialization is introduced in the artificial bee colony (ABC) algorithm to insure the diversity of the population and the ergodicity of the population search process. On the other hand, the collecting bees are divided into two parts in the improved algorithm, one part collects the optimal information of the region according to the original algorithm, the other does Levy flight around the current global best solution to improve its global search capabilities. Four standard test functions are used to show the superiority of the proposed method. The application of the IABC and EEMD algorithm in bearing fault diagnosis proves its effectiveness.