Rolling bearing fault classification method and system based on spectral kurtosis and neural network

Abstract

The invention provides a rolling bearing fault classification method and system based on spectral kurtosis and neural network. The method comprises the following steps: filtering the bearing fault signals based on the spectral kurtosis; extracting Mel cepstrum coefficient characteristics and differential characteristics of the filtered bearing fault signals to obtain a Mel cepstrum coefficient characteristic set and a differential characteristic set; randomly extracting a plurality of characteristics from the Mel cepstrum coefficient characteristic set and the differential characteristic set respectively, and sequentially arranging the characteristics according to the extraction sequence to form a Mel cepstrum coefficient characteristic diagram and a differential characteristic diagram which are represented by a two-dimensional matrix with preset size, so as to form a training set; inputting the Mel cepstrum coefficient characteristic diagram and the differential characteristic diagramin the training set into corresponding channels of a double-channel convolutional neural network for training to obtain a rolling bearing fault classification model; and carrying out fault classification on the bearing fault signals received in real time by using the rolling bearing fault classification model.

Description

technical field [0001] The disclosure belongs to the field of rolling bearing fault classification, and in particular relates to a rolling bearing fault classification method and system based on spectrum kurtosis and neural network. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] The vibration-acoustic signal generated during the operation of the bearing contains a wealth of bearing state information. The vibration-acoustic signal is processed by signal processing technology, and finally the detection of the equipment state can be completed by analyzing the vibration-acoustic signal through the fault diagnosis method. Most of the current bearing fault diagnosis and classification methods focus on the decomposition and envelope analysis of bearing vibro-acoustic signals, such as W.A.Smith et al. published a review paper "Rolling element be...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the above problems, the first aspect of the present disclosure provides a rolling bearing fault classification method based on spectral kurtosis and neural network, which classifies rolling bearing faults based on spectral kurtosis and convolutional neural network, effectively solving the strong noise influence The problem of low precision and unrecognizable fault degree in lower bearing diagnosis

Images