Rolling bearing fault diagnosis method based on multi-information fusion characteristics under complex working conditions

Abstract

To address the problem of low accuracy of traditional neural network models for rolling bearing fault diagnosis under complex working conditions, a novel method based on the multi-information fusion axis attention mechanism (MFA) and improved multi-scale convolutional neural network (MFA-IMSCNN) is proposed. Firstly, a multi-information fusion axis attention mechanism is presented to accurately extract the feature channel information and the number axis location information at various scales. Secondly, a multi-scale dense cyclic feature extraction module (MSDC) is built to improve the feature extraction ability of the MFA-IMSCNN under small sample conditions. The module better captures the potential feature information of time series and ensures the integrity of the information. Finally, two different datasets are imported into the MFA-IMSCNN model to evaluate its effectiveness, and the experimental results verify that the MFA-IMSCNN obtains better classification performance and stronger robustness under various experimental conditions.