Fast 1D K-SVD for transient feature extraction
To detect the incipient faults of rotating parts used in electromechanical systems widely, a novel transient feature extraction method based on the improved orthogonal matching pursuit (OMP) and one-dimensional K-SVD algorithm is explored. Firstly, the stopping criterion of adaptive spark is developed, and then the corresponding OMP algorithm is used to remove the modulated and harmonic signals adaptively. Secondly, the residual signal is reformulated as a signal matrix by period segmentation and circulating shift, and the initial transient dictionary is constructed via the time-domain average technique. Subsequently, a novel K-SVD algorithm is proposed to get the optimized transient dictionary for the one-dimensional signal. Finally, the repetitive transient signal is recovered by the optimized dictionary. The simulated and experimental results show that the proposed method can not only much faster extract the fault characteristics than the traditional K-SVD method, but also more accurately detect the repetitive transients than the infogram method and the traditional K-SVD method.
For the detail, please see: Yi Qin*, Jingqiang Zou, Baoping Tang, Yi Wang, Haizhou Chen. Transient feature extraction by the improved orthogonal matching pursuit and K-SVD algorithm with adaptive transient dictionary, IEEE Transactions on Industrial Informatics, 2019, DOI 10.1109/TII.2019.2909305