Weak fault feature extraction of rolling bearings based on globally optimized sparse coding and approximate SVD. (October 2018)

• Record Type: Journal Article
• Authors: Hou, Fatao; Chen, Jin; Dong, Guangming
• Is Part Of: Mechanical systems and signal processing. Volume 111(2018)
• Page Start: 234
• Page End: 250
• Publication Date: 2018-10
• Subjects: Globally optimized sparse coding -- Approximate SVD -- K-SVD -- Dictionary learning -- Rolling bearing -- De-noising -- Weak fault feature extraction; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2018.04.003
• Languages: English
• ISSNs: 0888-3270

Abstract

Graphical abstract: Highlights: A modified K-SVD method for bearing incipient weak fault detection is proposed. The noise can be suppressed more effectively. The impulse feature related to incipient weak fault can be enhanced and extracted. Both simulated and experimental signals validate the capability and application potential. 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.