Bearing fault detection method and device under sample imbalance condition

Abstract

The invention discloses a bearing fault diagnosis method and device under a sample imbalance condition, and relates to the field of bearing fault diagnosis, and the method comprises the steps: collecting time domain vibration signals of a bearing fault through equipment, and classifying the collected signals; segmenting the fault signals into a plurality of samples, then performing fast Fourier transform to obtain frequency domain data, and making a training set and a test set according to a proportion; building a VAE-GAN fault sample generation model, respectively inputting minority classes of fault samples in the training set into the model, and balancing the training data set; building an FLCNN fault classification model, and inputting the balance training set obtained in the step S3 into the model for training; and analyzing an experiment result. According to the bearing fault diagnosis method and device, the VAE network and the GAN network are combined, meanwhile, the feature coding capacity of the VAE for training data and the adversarial learning mechanism of the GAN are used for reference, and compared with other methods, the fault diagnosis precision can be effectively improved under different unbalanced proportions.

Description

technical field [0001] The invention belongs to the field of bearing detection, in particular to a bearing fault diagnosis method and device under the condition of unbalanced samples. Background technique [0002] Rolling bearings are one of the core components of rotating machinery. Due to long-term operation and complex system environment, once they are damaged, they will affect the normal operation of the equipment and even cause accidents. Therefore, accurately diagnosing the fault state of bearings is of great significance for monitoring the health of equipment and eliminating potential safety hazards in a timely manner. Traditional bearing fault diagnosis methods often require a large amount of well-distributed fault data as a support to obtain better diagnostic results, but in actual production, there are few ready-made fault devices for data collection, and the equipment may have been used when collecting fault data. Unable to run, resulting in far fewer fault sampl...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide a sample unbalanced condition for the problems of poor generalization ability and low diagnostic a...

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