Abstract
Reducing noise pollution in signals is of great significance in the field of signal detection. In order to reduce the noise in the signal and improve the signal-to-noise ratio (SNR), this paper takes the singular value decomposition theory as the starting point, and constructs various singular value decomposition denoising models with multiple multi-division structures based on the two-division recursion singular value decomposition, and conducts a noise reduction analysis on two experimental signals containing noise of different power. Finally, the SNR and mean square error (MSE) are used as indicators to evaluate the noise reduction effect, it is verified that the two-division recursion singular value decomposition is the optimal noise reduction model. This noise reduction model is then applied to the diagnosis of faulty bearings. By this method, the fault signal is decomposed to reduce noise and the detail signal with maximum kurtosis is extracted for envelope spectrum analysis. Comparison of several traditional signal processing methods such as empirical modal decomposition (EMD), ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), wavelet decomposition, etc. The results show that multi-resolution singular value decomposition (MRSVD) has better noise reduction effect and can effectively diagnose faulty bearings. This method is promising and has a good application prospect.
Highlights
Signals are inevitably subject to natural and man-made interference during acquisition and transmission
Based on the theory of multi-resolution singular value decomposition (MRSVD), this paper studies the noise reduction model of multi-division several noise reduction models, and the is obtained under different conditions structure MRSVD and summarizes it
The signal-to-noise ratio (SNR) and mean square error (MSE) are used as evaluation indexesand to verify comparing with traditional noise the results show that the and the several MRSVD
Summary
Signals are inevitably subject to natural and man-made interference during acquisition and transmission. Drawing on the idea of wavelet multi-resolution analysis, Zhao [25] proposed a new research idea, using the recursive idea to construct the signal into a second-order matrix, and perform singular value decomposition on the matrix layer by layer to obtain the decomposition results of signals in different levels of space and different resolutions, so as to achieve multi-resolution decomposition similar to wavelet analysis that can decompose signals into a series of different hierarchical subspaces This is essentially different from previous SVD-based signal processing methods, called MRSVD. The results show that the MRSVD method avoids EMD to a certain extent Defects such as value the application of noise reduction. EEMDinhave the advantages of better adaptability and robustness, and havestructure certain research value is in as Section briefly introduces the methods, including MRSVD andofmulti-division structure thefollows: application of noise reduction.
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