Rolling bearing fault diagnosis method based on dynamic index antagonism self-adaption

Abstract

The invention discloses a rolling bearing fault diagnosis method based on dynamic index antagonism self-adaption. The rolling bearing fault diagnosis method comprises the following steps: collecting vibration data when a bearing operates under different working conditions; taking the source domain features and the mixed domain sample features as input, performing adversarial training on a classifier and a domain discriminator, optimizing a feature extractor, and calculating loss; constructing an objective function of a bearing fault diagnosis model by using the loss, searching an optimal parameter until the bearing fault diagnosis model is completed, and reducing marginal distribution and conditional distribution differences of a source domain sample and a target domain sample by using a dynamic index regulation factor in a training process; and inputting the target domain sample into the bearing fault diagnosis model, and outputting a bearing fault diagnosis result. According to the method, the proportion of the marginal distribution and the conditional distribution in the overall data distribution can be accurately and quantitatively measured, so that the model can migrate data sets under different working conditions in a more targeted manner, and accurate fault diagnosis is realized.

Description

technical field [0001] The invention relates to the technical field of mechanical fault diagnosis, in particular to a rolling bearing fault diagnosis method based on dynamic index antagonism self-adaptation. Background technique [0002] With the development of industry, more and more rotating mechanical machines are used in production and life. Rolling bearings are one of the most important key components in rotating machinery, and their status is directly related to whether the rotating machinery can operate normally. Fault diagnosis is a comprehensive technology and an important measure to ensure the safe and reliable operation of mechanical equipment. Therefore, the diagnosis of rolling bearing faults, especially the analysis of early faults, and the realization of fast and accurate bearing fault monitoring are of great significance for the normal operation of mechanical equipment and safe production. Traditional fault diagnosis methods, such as time-domain statistical...

AI Technical Summary

Problems solved by technology

The few methods that consider conditional distributions, such as the joint distribution alignment method, assume that the marginal distribution alignment and the conditional distribution alignment have the same weight in the overall data distribution alignment, which obviously cannot be applied to all data distributions.

Images