A method for decomposing decorrelated multi-frequency empirical modes

Abstract

The invention provides a decorrelation multi-frequency empirical mode decomposition method, which can be used for solving the mode aliasing phenomenon existing in the EMD method and causing the problem of low feature extraction accuracy. Firstly, the masking signals of multiple frequencies are added to the initial signal, and the signal components with different frequency ratios are preliminarilydecomposed to obtain multiple IMF components. Secondly, the correlation coefficients between adjacent IMFs are calculated and decoupled to further separate the aliased IMF and obtain the optimal IMF.Finally, the optimal IMF is subtracted from the original signal and the above steps are repeated until the residual component is constant or monotonic. Because the IMFs are independent of each other and do not interfere with each other, the phenomenon of modal aliasing is significantly reduced, and the feature extraction accuracy is effectively improved. The innovation of this method is that it combines the masking signal processing method with the correlation processing method, which makes it possible to decompose mixed signals with different frequency ratios adaptively, suppress modal aliasing phenomena and improve feature extraction accuracy.

Description

technical field [0001] The present invention relates to the field of signal decomposition and signal processing, and more specifically, relates to a decorrelation multi-frequency empirical mode decomposition method. Background technique [0002] The Empirical Mode Decomposition (EMD) method is a method proposed by Dr. Huang Alligator to analyze and process non-stationary nonlinear signals, which can overcome the global and non-adaptive nature of traditional methods, and has been used in equipment It has achieved good results in different fields such as fault identification, power quality analysis, and EEG signal processing. At the same time, EMD has the ability to decompose complex multi-frequency mixed signals into multiple single-component signals that are easy to process and contain rich feature information. However, since the vibration signal contains a large number of similar time-frequency domain components and noise, the single signal component decomposed by EMD cont...

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Problems solved by technology

This directly causes the aliased IMF components to lack sufficient physical meaning, which leads to confusion in the subsequent time-frequency distribution, and ultimately affects the accuracy of feature extraction.

[0003] At present, domestic and foreign experts have done a lot of research on the problem of modal aliasing, among which the noise processing method and decorrelation processing method are particularly effective. However, these two methods still have certain limitations, such as the selection of noise is random, The signal cannot be adaptively processed; the decorrelation processing method has a good suppression effect on the modal aliasing generated by the mixed signal with small frequency ratio, but the decomposition effect on the mixed signal with different frequency ratio is reduced

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