现代信息科技2025，Vol.9Issue(7)：179-185,7.DOI:10.19850/j.cnki.2096-4706.2025.07.033
基于改进多尺度卷积网络的轴承故障诊断研究
Research on Bearing Fault Diagnosis Based on Improved Multi-Scale Convolutional Networks
摘要
Abstract
In this paper,Improved Multi-Scale Convolutional Networks bearing fault diagnosis method is proposed to solve the problems of Convolutional Neural Network in complex environments,such as easy to be disturbed,difficult to extract rich fault features from fixed receptive field and low diagnosis accuracy.Firstly,the original vibration signal is preprocessed.Secondly,the convolution kernels of different receptive fields are used to extract multi-scale features to effectively capture diversified fault information.Thirdly,the Self-Attention Mechanism is introduced to enable the model to dynamically calculate and adjust the weight of each position in the feature map,and adaptively enhance the key fault features.Finally,the fully connected layer is used to classify the extracted features to achieve accurate diagnosis.The experimental results show that the diagnosis accuracy of the method on the public dataset reaches about 98%,and it shows good anti-noise and generalization ability under different signal-to-noise ratio conditions.关键词
多尺度卷积网络/特征提取/自注意力机制/轴承故障诊断Key words
Multi-Scale Convolutional Networks/feature extraction/Self-Attention Mechanism/bearing fault diagnosis分类
机械制造引用本文复制引用
贡莹莹,朱晓娟..基于改进多尺度卷积网络的轴承故障诊断研究[J].现代信息科技,2025,9(7):179-185,7.基金项目
安徽高校省级自然科学研究重点项目(KJ2020A0300) （KJ2020A0300）
