Bearing vibration signal sparse reconstruction method

Abstract

The invention relates to a bearing vibration signal sparse reconstruction method. The method includes steps: S1, on the basis of discrete cosine transform, subjecting an original bearing vibration signal x to sparsification to obtain sparse representation theta of x in a transform domain psi, wherein x=<Theta, and psi is an orthogonal matrix; S2, on the basis of restricted isometry conditions, using an observation matrix phi for compressing the signal x to obtain an observation signal y, wherein y=Phix=Phi<Theta; S3, adopting an ADMM (alternate direction multiplier method) and an LSQR (least square QR-factorization) algorithm to obtain a reconstructed signal according to the observation signal y. Compared with the prior art, the method has advantages that sparse reconstruction of bearing vibration signals with nonlinearity and non-stationarity characteristics is realized quickly and effectively while high reconstruction precision is guaranteed.

Description

technical field [0001] The invention relates to the field of signal processing, in particular to a method for sparse reconstruction of bearing vibration signals. Background technique [0002] Today, machinery and equipment in modern industry are developing towards more automation, precision and efficiency. In order to realize real-time status monitoring and timely fault handling of mechanical equipment, the collection of a large number of vibration signals has made the fault diagnosis technology of mechanical equipment enter the era of big data. In order to restore the original signal without distortion as much as possible, traditional signal acquisition techniques must follow the Nyquist sampling theorem, that is, the sampling frequency should not be less than twice the highest frequency in the signal spectrum. In the reality that failures do not occur instantaneously, data sampling, compression, and transmission must continue. As time goes by, the amount of data increases...

AI Technical Summary

Problems solved by technology

On the one hand, the algorithm is relatively simple, so that the reconstructed signal is quite different from the original signal, and more information is lost

Especially when carrying fault signals, the loss of information will affect the extraction of fault features and hinder the process of condition monitoring and fault handling

On the other hand, the optimization of the sparse reconstruction algorithm is not enough, and it cannot meet the requirements of small number of iterations and small reconstruction error at the same time, which drags down the processing speed of the signal

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