Rolling bearing local damage weak fault feature extraction method

Abstract

The invention relates to a rolling bearing local damage weak fault feature extraction method. A vibration signal of a fault rolling bearing is acquired by using an acceleration sensor; the original signal is decomposed to different nodes by using MODWPT (Maximal Overlap Discrete Wavelet Packet Transform); next, SK (Spectrum Kurtosis) of mean square envelope autocorrelation of each node is calculated; then node signals of which SK is not smaller than half of the maximum SK in each layer are selected to be subjected to MED (Minimum Entropy Deconvolution) filtering ; then normalized spectrum information of the selected nodes on the same layers is superimposed according to a layer number and averaged; and finally, spectra of all the layers are merged, and local damage fault features of the rolling bearing are extracted. The invention gives out a method for extracting the local damage fault features of the rolling bearing under strong background noise interference, and provides an effectivediagnostic tool for implementing extraction of the weak fault features of the rolling bearing in the early stage of PHM (Prognostic and Health Management).

Description

technical field [0001] The invention relates to the technical field of fault diagnosis and signal processing and analysis, in particular to a method for extracting weak fault features of local damage of rolling bearings. Background technique [0002] As one of the core components of rotating machinery, rolling bearings are closely related to whether their operating status is normal or not. When the faulty rolling bearing with local damage is working, the impact signal will be generated, and the fault characteristic frequency will be different according to the location of the damage, so that the fault diagnosis of the rolling bearing can be completed. In the fault feature extraction, Fourier transform is an effective fault feature extraction method, but affected by strong interference such as environmental noise, the weak fault features in the original signal are easily submerged and difficult to extract, it is necessary to combine the relevant The signal processing method i...

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Problems solved by technology

[0004] In order to overcome the defects of the above-mentioned prior art, the purpose of the present invention is to provide a method for extracting weak fault features of rolling bearing local damage, by using MODWPT to decompose the signal, calculate the SK of the mean square envelope autocorrelation of each node, and then select the different The node signal smaller than the maximum SK is MED filtered, and finally the mean square envelope spectrum of the obtained signal is normalized and jointly analyzed to obtain the fault characteristic spectrum, which solves the problem that the early weak fault of the rolling bearing is difficult to extract due to the interference of environmental noise

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