Bearing fault diagnosis method, device and equipment

Abstract

The invention discloses a bearing fault diagnosis method, device and equipment and a computer readable storage medium. the method comprises the steps: collecting bearing vibration signals, constructing a multi-source domain and target domain data set, constructing a bearing fault diagnosis model, processing the multi-source domain and target domain data set, inputting the processed data set into a feature learning device, carrying out the feature extraction, solving a moment distance according to the extracted sample features of the multi-source domain and the target domain, inputting each source domain sample feature into a corresponding classifier, outputting a prediction label, calculating classifier cross entropy loss with a real label, constructing a target function of the model by using the moment distance and the classifier cross entropy loss, and training the model by using an intra-class alignment training strategy. The target domain data set is input into the trained model, a comprehensive prediction result is output through a weighted classification mechanism, multi-source domain migration is adopted, more perfect fault information can be utilized, and fault diagnosis of multiple working conditions and multiple types is facilitated.

Description

technical field [0001] The present invention relates to the technical field of mechanical fault diagnosis and machine learning, in particular to a method, device, equipment and computer-readable storage medium for bearing fault diagnosis. Background technique [0002] With the widespread existence of rotating machinery in modern industrial production, rolling bearings, as key components in industrial production equipment, are widely used in various important fields such as machinery, electric power, chemical industry, and aviation. As the core component of rotating machinery, rolling bearings work under the condition of high-speed rotation, load and vibration for a long time, so they are prone to failure, causing huge economic losses and even a large number of casualties. The development of fault diagnosis technology is to solve this problem and avoid this loss. It can monitor the running condition of rolling bearings, and even predict its future failure trend, so as to avo...

AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is to provide a method, device, equipment and computer-readable storage medium for bearing fault diagnosis, so as to solve the problem that the fault diagnosis model of single-source domain transfer learning in the prior art cannot diagnose actual bearing faults

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