Rolling bearing fault detection using an adaptive lifting multiwavelet packet with a dimension spectrum

Abstract

Defect faults on the surface of rolling bearing elements are the most frequent cause of malfunctions and breakages of electrical machines. Due to increasing demands for quality and reliability, extracting fault features in vibration signals is an important topic for fault detection in rolling bearings. In this paper, a novel adaptive lifting multiwavelet packet with dimension spectrum to detect defects in rolling bearing elements is developed. The adaptive lifting multiwavelet packet is constructed to match vibration signal properties based on the minimum singular value decomposition (SVD) entropy using a genetic algorithm. A dimension spectrum is further employed to extract rolling bearing fault characteristic frequencies from background noise. The proposed method is applied to analyze the vibration signal collected from electric locomotive rolling bearings with outer raceway and inner raceway defects. The experimental investigation shows that the method is accurate and robust in rolling bearing fault detection.