Rotating machine fault diagnosis method based on volume maximin concave penalty algorithm

Abstract

The invention discloses a rotating machine fault diagnosis method based on a volume maximin concave penalty algorithm, and the method comprises the steps: S1, collecting a rotating machine vibration acceleration response signal and a rotating speed signal, and recording possible fault feature information; S2, intercepting a section of vibration signal from the collected data, and obtaining a modefrom the section of vibration signal by using a shift invariant K-SVD method; S3, taking the obtained mode as input, solving a sparse coefficient through a convolution maximin concave penalty algorithm, and carrying out the convolution of the sparse coefficient and the mode, and obtaining a reconstruction fault feature; and S4, analyzing the extracted time domain characteristics and demodulation spectrum characteristics of the fault characteristic signal, determining a fault type, and completing fault diagnosis. According to the method, volume maximin concave penalty is taken as a penalty function, so that the problem of amplitude underestimation existing in an existing method is solved, an algorithm solving process is converted from a time domain to a frequency domain, and the operation efficiency is greatly improved.

Description

technical field [0001] The invention belongs to the field of fault diagnosis of rotating machinery, and more specifically relates to a fault diagnosis method for rotating machinery based on a rolling positive large minimum concave penalty algorithm. Background technique [0002] Rotating machinery serves in a complex and changeable environment, and failure will lead to serious consequences. Therefore, it is very important to monitor the condition of rotating machinery. The vibration response signal of rotating machinery contains a lot of noise. How to extract the fault characteristics from the noisy vibration signal accurately and quickly is a difficult problem in mechanical fault diagnosis. [0003] Signal processing methods such as Kurtogram, Empirical Mode Decomposition (EMD) and Convolutional Sparse Coding (CSC) have been applied to mechanical fault diagnosis, but there are still shortcomings in the method. For example, the spectral kurtosis method may cause the resonan...

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Problems solved by technology

[0004] The purpose of the present invention is to provide a local type fault diagnosis method for rotating machinery based on the volume positive large minimum concave penalty algorithm for the defect that the existing convolution sparse representation method will underestimate the impact characteristic amplitude

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