Rolling bearing fault diagnosis method based on short-time Hilbert transform

Abstract

The invention relates to the technical field of bearing fault detection, and discloses a rolling bearing fault diagnosis method based on short-time Hilbert transform, and the method comprises the steps: A) collecting a bearing vibration signal, and designing an optimal filter through employing minimum entropy deconvolution; B) carrying out filtering processing on the bearing vibration signal by using the optimal filter; C) performing short-time Hilbert transform on the filtered bearing vibration signal to obtain a feature image; D) constructing a convolutional neural network model; and E) utilizing the trained convolutional neural network model to realize bearing fault category classification. The minimum entropy deconvolution is utilized to carry out filtering processing on the collectedsignals, and then the short-time Hilbert transform method is utilized to acquire the feature images so that the vibration signal features can be retained to the maximum extent, the classification of the bearing fault types and the fault severity is realized through the convolutional neural network and the accuracy of fault classification is improved.

Description

technical field [0001] The invention relates to the technical field of bearing fault detection, in particular to a rolling bearing fault diagnosis method based on short-time Hilbert transformation. Background technique [0002] Rolling bearings are one of the key parts of rotating machines, which support the rotating body and ensure the accuracy of rotation, and are widely used in various fields. In the faults of rotating machines, bearing damage accounts for about 30%, and bearing damage seriously affects the working performance of rotating machines. Therefore, it is of practical significance to carry out fault monitoring on the state of rolling bearings, diagnose bearing faults in time, and provide guidance and suggestions for equipment maintenance. [0003] The on-site process of rotating machines is complex, and due to the interference of signal transmission attenuation and background noise, the fault information of rolling bearings is submerged in the noise signals. Th...

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Problems solved by technology

[0005] The present invention aims to solve the problems of inaccurate fault frequency band positioning and limited extraction of characteristic parameters in the prior art, and provides a rolling bearing fault diagnosis method based on short-time Hilbert transform. The present invention utilizes minimum entropy deconvolution The collected signal is filtered, and then the characteristic image is obtained by the short-time Hilbert transform method, which can preserve the characteristics of the vibration signal to the greatest extent, and then the classification of the bearing fault type and fault severity is realized through the convolutional neural network

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