Sparse Feature Extraction for Variable Speed Machinery Based on Sparse Decomposition Combined GST

In order to extract fault impulse feature of variable speed machinery from strong background noise, a sparse feature extraction method based on sparse decomposition combined generalized S transform (GST) was proposed in this paper. Firstly, multi-resolution generalized S transform (MGST) was used to pursuit the optimal atom in each iteration, to get normalized time-frequency spectrums with different scales, and to find the maximum energy and corresponding time-frequency factors to build an optimal atom. Then, an orthogonal matching pursuit (OMP) was used to decompose the signal into several optimal atoms, and the efficiency of atoms pursuit was improved with MGST. Finally, the theoretical locations of impulses were calculated according to the location of first impulse in the sparse representation signal, and the fault was diagnosed through the comparison of theoretical and measured locations. The results of simulation and experiment validate the performances of the proposed method, being better than traditional GST method and OMP method in precision and decomposition speed.