Bearing fault diagnosis method based on improved EMD decomposition and sensitive characteristic selection

Abstract

The invention discloses a bearing fault diagnosis method based on improved EMD decomposition and sensitive characteristic selection. The method comprises steps of: performing wavelet noise reduction and EMD decomposition on the original vibration signals of a bearing in different fault states to obtain a plurality of IMF components; selecting, by quantitatively computing the correlation of each IMF component and the corresponding original vibration signal, the first h IMF components including the main fault information of the bearing as an object from which fault characteristic information is extracted, and extracting the characteristic parameters from the IMF components to form a original characteristic set; determining the sensitivity factor of each characteristic in the original characteristic set according to a distance evaluation method and constructing a sensitive characteristic set; inputting the sensitive characteristic vector of a training sample in the fault sample of the bearing into a SVM to be trained, optimizing the kernel function parameter g and the penalty factor c of the SVM according to a genetic algorithm, and identifying the fault of a tested sample. The method may reduce the dimensionality of the fault characteristic vector and the computational scale of a classifier, and increasing fault diagnosis accuracy of the antifriction bearing.

Description

technical field [0001] The invention belongs to the technical field of bearing fault diagnosis, and in particular relates to a bearing fault diagnosis method based on improved EMD decomposition and sensitive feature selection. Background technique [0002] As one of the important parts of rotating machinery, rolling bearings are extremely important to ensure the reliable operation of the entire mechanical system. However, due to the errors in the manufacturing process and the influence of the complex and harsh environment when the bearings work, only a small part of the bearings can meet the design requirements. Life, bearing failure will cause serious accidents of key equipment such as rotating machinery, resulting in huge economic losses and casualties. Therefore, monitoring the operating status of bearings and timely fault diagnosis can ensure the normal operation of rotating machinery and avoid accidents. happened. [0003] Because bearings often work in complex, harsh,...

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Problems solved by technology

[0002] As one of the important parts of rotating machinery, rolling bearings are extremely important to ensure the reliable operation of the entire mechanical system. However, due to the errors in the manufacturing process and the influence of the complex and harsh environment when the bearings work, only a small part of the bearings can meet the design requirements. Life, bearing failure will cause serious accidents of key equipment such as rotating machinery, resulting in huge economic losses and casualties. Therefore, monitoring the operating status of bearings and timely fault diagnosis can ensure the normal operation of rotating machinery and avoid accidents. happened

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