Bearing vibration signal sparse reconstruction method

A bearing vibration and signal sparse technology, applied in the field of signal processing, can solve the problems of insufficient optimization of sparse reconstruction algorithm, affecting fault feature extraction, multi-information, etc. Effect

Active Publication Date: 2017-05-31
SHANGHAI UNIV OF ENG SCI
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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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Embodiment Construction

[0055] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is carried out on the premise of the technical solution of the present invention, and detailed implementation and specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.

[0056] like figure 1 As shown, a method for sparse reconstruction of bearing vibration signals with high reconstruction accuracy and few iterations in the calculation process includes the following steps:

[0057] S1: Use the discrete cosine transform basis to sparse the original bearing vibration signal x to obtain the sparse representation θ of x on the transform domain Ψ, x=Ψθ, Ψ is an orthogonal matrix.

[0058] The premise of compressive sensing theory is that the signal is sparse, or sparse in a transform domain. However, the vibration signal of the research object bearing in ...

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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...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/04
CPCG01M13/045
Inventor 史宗莉宋万清
Owner SHANGHAI UNIV OF ENG SCI
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