机电工程技术2024，Vol.53Issue(7)：29-33,180,6.DOI:10.3969/j.issn.1009-9492.2024.07.006
噪声背景下的MSECAE轴承故障诊断方法研究
Research on MSECAE Bearing Fault Diagnosis Method Under Noise Background
摘要
Abstract
In response to the problems of single scale feature extraction,poor noise resistance in traditional fault diagnosis based deep learning,a multi-scale convolutional autoencoder fusion efficient channel attention mechanism(MSECAE)method for fault diagnosis of bearings is proposed.Firstly,the Fourier transform is used to normalize the raw data,the original one-dimensional vibration signal is converted to the frequency domain for representation,which is beneficial for the model to extract features.Secondly,the MSECAE structure is constructed,and multi-scale convolution(MSCNN)is used to extract multi-scale features from the original information.An efficient channel attention mechanism(ECA)is used to dynamically select the size of the convolution kernel,and different weights are assigned based on the importance of features in each channel.To verify the performance of the proposed model,multiple experiments are conducted using datasets with two different sampling frequencies under four different noise conditions,and compared with other models.The experimental results show that compared with other models,the proposed model has a classification accuracy of over 99%,better generalization ability,and stronger robustness.关键词
故障诊断/多尺度卷积模块/高效通道注意力机制(ECA)/轴承Key words
fault diagnosis/multi scale convolution module/effective channel attention(ECA)/bearings分类
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徐坤,任万凯,王晓夫,魏志民,潘作舟,刘征,蔡木霞..噪声背景下的MSECAE轴承故障诊断方法研究[J].机电工程技术,2024,53(7):29-33,180,6.基金项目
天津市教委科研计划项目(2021KJ102) （2021KJ102）
