Sparse reconstruction optimization algorithm-based signal envelope line extraction method

Abstract

The invention discloses a sparse reconstruction optimization algorithm-based signal envelope line extraction method. A particle swarm-based sparse reconstruction optimization algorithm is adopted; change factors of changing frequency bandwidths of DCT bases are taken as attributes of particles in the particle swarm-based sparse reconstruction optimization algorithm; global optimal positions of upper and lower envelope lines are obtained through multi-time iteration and correspondingly serve as the change factor used for changing the frequency bandwidth of the DCT base required for an upper envelope line obtaining process and determined by a particle and the change factor used for changing the frequency bandwidth of the DCT base required for a lower envelope line obtaining process and determined by a particle, thereby obtaining an optimal DCT base most suitable for a change trend of the upper envelope line and an optimal DCT base most suitable for a change trend of the lower envelope line; and finally an optimal upper envelope line and an optimal lower envelope line are adaptively extracted. Therefore, the extraction precision of the upper envelope line and the lower envelope line is improved; and the anti-noise performance of upper and lower envelope line extraction is improved.

Description

technical field [0001] The invention relates to a signal processing technology, in particular to a signal envelope extraction method based on a sparse reconstruction optimization algorithm. Background technique [0002] In the field of mechanical fault diagnosis and bridge vibration detection, many vibration signals contain modulation signal components, and different faults produce different modulation forms. An important part of the analysis and processing of the modulated signal is demodulation. Demodulation analysis can obtain the envelope curve and envelope demodulation spectrum of the modulated signal, and the envelope of the modulated signal often carries useful information concentratedly. Therefore, in In many fields such as signal processing and fault diagnosis, the research of envelope demodulation method has always been the focus of many scholars. [0003] Currently commonly used envelope analysis methods are: generalized detection filter, Hilbert transform, cubic...

AI Technical Summary

Problems solved by technology

Among them, the generalized detection filter may produce frequency mixing effect in the process of envelope extraction, so it is required to select an appropriate sampling frequency to sample the signal; the method of extracting the signal envelope based on Hilbert transform has weak anti-noise, when the signal-noise When the ratio becomes smaller, the envelope error obtained by the Hilbert transform will increase accordingly; the extraction of the envelope based on the cubic spline interpolation method will cause the problem of endpoint effect, which will affect the accuracy of the empirical mode decomposition

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