A novel intelligent method for bearing fault diagnosis based on affinity propagation clustering and adaptive feature selection

Abstract

Bearings faults are one of the main causes of breakdown of rotating machines. Thus detection and diagnosis of mechanical faults in bearings is very crucial for the reliable operation. A novel intelligent fault diagnosis method for roller bearings based on affinity propagation (AP) clustering algorithm and adaptive feature selection technique is proposed to better equip with a non-expert to carry out diagnosis operations. Ensemble empirical mode decomposition (EEMD) and wavelet packet transform (WPT) are utilized to accurately extract the fault characteristic information buried in the vibration signals. Moreover, in order to improve the efficiency of clustering algorithm and avoid the curse of dimensionality, a new adaptive features selection technique is developed in this work, whose effectiveness is verified in comparison with other methods. The proposed intelligent method is then applied to the bearing fault diagnosis. Results demonstrate that the proposed method is able to reliably and accurately identify different fault categories and severities of bearings.