Bearing fault diagnosis method based on semi-supervised generative adversarial network

Abstract

The invention relates to a bearing fault diagnosis method based on a semi-supervised generative adversarial network, and the method comprises the following steps of obtaining vibration signals of thebearing in different states, and dividing the vibration signals into multiple samples; randomly dividing the samples into a training set and a test set; constructing a small number of label samples ofdifferent faults in the training set; constructing a one-dimensional semi-supervised generative adversarial network model; and inputting the training set into the adversarial network for training, wherein the trained adversarial network is used to test the diagnosis of centralized bearing faults. The method provided by the invention directly inputs the originally collected vibration signal and directly outputs the category of the bearing fault in the test set through training to achieve an end-to-end optimal diagnosis model, and uses a one-dimensional convolutional layer and a one-dimensionaldeconvolutional layer to enhance the ability of the one-dimensional semi-supervised generative adversarial network for extracting features. The invention is a semi-supervised training method, which does not require a large number of manual label samples, greatly saves time and labor costs, and has strong bearing fault diagnosis effect and anti-noise capability, and good stability.

Description

technical field [0001] The invention relates to the technical field of bearing fault diagnosis, in particular to a bearing fault diagnosis method based on a semi-supervised generation confrontation network. Background technique [0002] In mechanical systems and electrical systems, rolling bearings are one of the important basic components. Under various complex working conditions, various defects such as rolling element deformation, wear, corrosion, and cracks are often prone to occur. Damaged rolling bearings may cause huge economic losses in the production process in engineering practice, and may even cause personnel safety accidents. Therefore, the research on fault diagnosis of rolling bearings is of great significance. [0003] The fault types of rolling bearings are divided according to their fault forms, mainly including peeling, wear, corrosion, etc. The causes of these faults are very complicated, structural design problems, improper assembly, use and maintenance ...

AI Technical Summary

Problems solved by technology

However, in practical problems, most of the original data obtained are unlabeled data, and manual labeling of fault samples requires extensive expert experience, and requires a lot of time and labor costs

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