An information-based K-singular-value decomposition method for rolling element bearing diagnosis

Abstract

K-singular value decomposition (K-SVD), as an extension of sparse coding, has attracted great attention for fault feature extraction of rolling element bearings (REBs) in recent years. However, the performance of original K-SVD algorithm is flawed since its atoms in the dictionary are invariably updated according to the principle component, which reduces its pertinence to periodic impulses under complex interferences. To cope with this deficiency, this paper proposes an information-based K-SVD (IK-SVD) method considering the intrinsic properties of fault response. In this framework, an information-based atom selection strategy (IASS) is designed to seek a group of fault-related atoms for dictionary updating, in which a new index named harmonic ratio (HR) is employed to provide the evaluation criterion. On this basis, the fault signatures can be specifically extracted from degraded vibration signals with proposed method. Moreover, a residual estimation method is presented to compute the threshold value used in the sparse coding stage. The superiority of IK-SVD is verified on simulated signal and practical signals from a locomotive bearing test rig. The analysis results demonstrate the good performance of proposed method for the fault detection of REBs.