Weak fault feature extraction of rolling bearings based on globally optimized sparse coding and approximate SVD

Abstract Fault feature extraction is crucial to condition monitoring and fault prognostics. However, when fault is in the initial stage, it is often very weak and submerged in the strong noise. This makes the fault feature very difficult to be extracted. In this paper, we propose a novel method based on sparse representation theory. It is inspired by the traditional K-SVD based de-noising method and can penetrate into the underlying structure of the signal. It learns sparse coefficients and dictionary from the noisy signal itself. The coefficients are globally optimized based on an l 1 -regularized least square problem solving method, which can locate the impulse coordinates more accurately compared with orthonormal matching pursuit (OMP) applied in the traditional K-SVD. The dictionary learning is based on an approximation of singular value decomposition (SVD). With the learned dictionary, we can capture the higher-level structure of the signal. Combining the sparse coefficients and the learned dictionary, we can de-noise the signal effectively and extract the incipient weak fault features of rolling bearings. The results of processing both simulated and experimental signals are illustrated and both validate the proposed method. All the experimental data are also processed by SpaEIAD, wavelet shrinkage, and fast kurtogram for comparison.