Weak Signal Detection Based on Combination of Sparse Representation and Singular Value Decomposition

Due to the inevitable acquisition system noise and strong background noise, it is often difficult detect features of weak signals. To solve this problem, sparse representation can effectively extract useful information according characteristics However, less effective against non-Gaussian white noise. Therefore, a novel SRSVD method combining singular value decomposition proposed further improve denoising performance algorithm. All signal components highly matched with dictionary are extracted by representation, then each component weighted evaluation index, PMI, which indicate in signal, so that algorithm greatly improved. The verified processing signals circuit early fault bearings. results show successfully suppress interference. Compared other existing methods, has better performance.