Gear vibration signal source underdetermined blind source separation method based on density and compressed sensing

Abstract

The invention discloses a gear vibration signal source underdetermined blind source separation method based on density and compressed sensing, and the method comprises the steps: firstly carrying outthe noise reduction preprocessing of a collected signal through employing a wavelet noise reduction method; secondly, carrying out short-time Fourier transform on the mixed signals, and converting a convolution mixed model in a time domain into a linear mixed model in each frequency band; then, achieving effective estimation of a hybrid matrix through a single-source-point extraction method basedon sparse coding and a vibration source number recognition method based on a density peak clustering method; recovering the source signal through a compressed sensing method to obtain a separation signal; and finally, performing sequence and amplitude correction on the separation signals of each frequency band, and converting the separation signals from a frequency domain to a time domain. According to the method, the robustness to noise is enhanced by estimating the number of the vibration sources through the density peak clustering method, the probability of generating false peaks is reduced, then the estimation error of the number of the vibration sources is reduced, and the separation work of mixed signals can be effectively completed.

Description

technical field [0001] The invention belongs to the field of signal processing and blind source separation, in particular to a method for underdetermined blind source separation of gear vibration sources based on density and compressed sensing. Background technique [0002] In engineering applications, it is usually necessary to use the vibration signals generated by various components during the working process to judge their working status and health status. For example, some specific periodic vibration signals generated by rotating parts such as gears during the working process can reflect the working status and health status of the system, but such effective signals will be mixed with other parts and environmental noise when acquired, making the characteristic If the signal is submerged, it is difficult to draw an effective judgment by directly using multi-source mixed signals for analysis and processing. Therefore, the research on the method of blind source separation ...

AI Technical Summary

Problems solved by technology

Some scholars use the potential function method to estimate the number of sources, but the potential function method is sensitive to the division interval and is not robust to noise. It is easy to generate false peaks, resulting in errors in the estimation of the number of source signals, which in turn leads to the separation of source signals. error

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