A rolling bearing fault diagnosis method under variable working conditions based on deep features and transfer learning

Abstract

The invention discloses a deep feature and transfer learning-based rolling bearing fault diagnosis method under variable working conditions, relates to the technical field of fault diagnosis, and aimsto solve the problem of low state identification accuracy of different fault positions and different performance degradation degrees of a rolling bearing under the variable working conditions. The method comprises the following steps: firstly, carrying out feature extraction on the vibration signal frequency domain amplitude of the rolling bearing by adopting SDAE to obtain vibration signal deepfeatures, and forming a source domain feature sample set and a target domain feature sample set; then, adopting the JGSA to carry out domain adaptation processing on the source domain feature sample and the target domain feature sample, the purpose of reducing distribution offset and subspace transformation difference of feature samples between domains is achieved, and domain offset between different types of feature samples is reduced. And finally, completing rolling bearing multi-state classification under variable working conditions through a K nearest neighbor algorithm. Compared with other methods, the method disclosed by the invention shows better feature extraction capability under the variable working condition of the rolling bearing, the sample feature visualization effect of therolling bearing is optimal, and the fault diagnosis accuracy of the rolling bearing under the variable working condition is high.

Description

technical field [0001] The invention relates to a rolling bearing fault diagnosis method under variable working conditions, and relates to the technical field of rolling bearing fault diagnosis. Background technique [0002] Rolling bearings are key components of rotating machinery, and their normal operation is an important guarantee for the work of production equipment [1] . In practice, the working conditions of rolling bearings are constantly changing, which directly affects the change of vibration characteristics of rolling bearings [2] . The traditional fault diagnosis method based on constant working conditions is very prone to misdiagnosis or missed diagnosis of rolling bearings in response to the complex and variable working conditions of rolling bearings. [3] . Therefore, it is of great significance to accurately identify the running state of rolling bearings under variable working conditions for the healthy operation of mechanical equipment. [0003] In recen...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is: Aiming at the problem that it is difficult or impossible to obtain rolling bearing vibration data and its labels under variable working conditions, resulting in low accuracy of multi-state identification of different fault locations and different performance degradation degrees of rolling bearings under variable working conditions, the proposed A Fault Diagnosis Method for Rolling Bearings under Variable Conditions Based on Sparse Denoising Autoencoder (SDAE) and Joint Adjustment Algorithm of Geometric Space and Statistical Distribution (JGSA)

Images