Fault diagnosis method based on semi-supervised learning deep adversarial network

Abstract

The invention discloses a fault diagnosis method based on a semi-supervised learning deep adversarial network. The fault diagnosis method comprises the steps of: acquiring vibration signals of a bearing under different operation faults, and subjecting a vibration time-domain signal of the faulty bearing to wavelet transformation to form a two-dimensional image; and conducting supervised learning on a small amount of labeled data by a generative adversarial network, training on a large amount of unlabeled data in an unsupervised manner, extracting high-dimensional features by means of a convolutional neural network to achieve data classification, and therefore identifying a fault category of the bearing. According to the fault diagnosis method, a high-precision fault diagnosis model is obtained by training under the condition of limited labeled data, and a more precise discriminator is obtained, so that precise fault diagnosis can be carried out based on the vibration signals of the rolling bearing.

Description

technical field [0001] The invention relates to the technical field of rolling bearing vibration signal processing, in particular to a fault diagnosis method based on a semi-supervised learning deep confrontation network. Background technique [0002] The study of advanced mechanical fault diagnosis methods is an important part of ensuring the safety of equipment and personnel. Among them, bearings are one of the most important mechanical parts in rotating machinery. They are widely used in various important fields such as electric power, chemical industry, metallurgy, and aviation. At the same time, bearings are also One of the most easily damaged components, the quality of bearing performance and working conditions will directly affect the performance of the entire machine equipment. Defects in bearing performance and working conditions will cause abnormal vibration and noise of the equipment, and even cause equipment damage. Therefore, it is particularly important to carr...

AI Technical Summary

Problems solved by technology

However, using deep learning to train fault classification models requires a large amount of labeled sample data. However, in actual working conditions, although a large amount of vibration data can be collected, most of the vibration data do not have labels. For each fault It is unrealistic to collect a large amount of labeled vibration data

Therefore, the fault diagnosis method based on deep learning is not suitable for fault diagnosis of rolling bearings.

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