Bearing fault diagnosis method and system

Abstract

The invention provides a bearing fault diagnosis method and system. The bearing fault diagnosis method comprises the following steps: collecting experimental data of a faulty bearing under a working condition, sequentially constructing a training set, and establishing a classification model by using the training set; optimizing the smoothing factor of classification model by a verification set anda test set to obtain an optimized model; finally, performing fault diagnosis on bearing operation data by the optimized model. The classification model has higher classification accuracy and higher training and updating speed.

Description

technical field [0001] The invention belongs to the technical field of bearing diagnosis, and relates to a bearing fault diagnosis method and system. Background technique [0002] The health status of rolling bearings has a huge impact on the performance, stability and service life of electromechanical equipment. If it fails, it will affect the normal operation of the equipment, causing huge economic losses and even casualties. In recent years, as equipment has become increasingly intelligent and complex, it has become very important to accurately identify bearing faults and their severity. Traditional regular maintenance consumes a lot of manpower and material resources, and it is difficult to realize real-time monitoring of equipment. Therefore, real-time monitoring, identification of bearing fault types, and judgment of fault severity are of great significance to ensure long-term safe and reliable operation of equipment. [0003] With the rise of AI, intelligent fault d...

AI Technical Summary

Problems solved by technology

One is to first extract the features of the bearing signal to obtain the training set, and then train the backpropagation neural network, support vector machine and other classification models to realize fault diagnosis and classification. poor

The other is to use deep learning such as convolutional neural network, deep belief network, etc. to directly extract the characteristics of bearing signals and perform diagnostic classification. This type of method has a certain ability to resist noise interference, but requires a large amount of fault type data for feature learning. Model training and updating takes a long time, and it cannot guarantee that it still has good feature learning ability under small samples

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