信息与控制2024，Vol.53Issue(5)：615-630,16.DOI:10.13976/j.cnki.xk.2024.3223
自适应特征选择的多扩张卷积网络轴承故障诊断
Bearing Fault Diagnosis Based on Multi-dilated Convolutional Networks with Adaptive Feature Selection
摘要
Abstract
The existing multi-scale convolutional network models lack a fault discrimination mechanism and ignore the influence of different convolution scales on the extracted features of the model.Fur-thermore,most of the neural network models based on current signals for bearing fault diagnosis are poor in interpretability.Aiming at solving these problems,a bearing fault diagnosis method based on Multi-dilated Convolutional neural network with Adaptive Feature Selection(MCAFS)is pro-posed.Firstly,the demodulation techniques are used to suppress the interference of the fundamental frequency component of the original current signal,and then the fast Fourier transform(FFT)is employed to convert the demodulated signal from the time domain to the frequency domain.Sec-ondly,the shallow features are extracted from the frequency domain through a standard convolu-tional neural network.Next,multi-scale features are selected from shallow features using parallel dilated convolution(PDC)blocks with different kernel scales.Then,an improved multiscale di-lated convolutional attention(MCA)module is proposed to adaptively select the size of the convo-lution scale and extract the deep features.Finally,the spatial attention module(SAM)is intro-duced to visualize the attention distribution of the input frequency signal,which further improves the interpretability of the network.The proposed network model is verified by the current signals of the Paderborn rolling bearing dataset.The experimental results show that the proposed network model can adaptively select the convolution scale,effectively locate the key information of the input data,and provide certain interpretable significance for the feature extraction process of current sig-nal-based bearing fault diagnosis.关键词
轴承故障诊断/卷积神经网络/扩张卷积/注意力机制/多尺度卷积Key words
bearing fault diagnosis/convolutional neural network/dilated convolution/attentional mechanism/multiscale convolution分类
信息技术与安全科学引用本文复制引用
王照伟,钟晓勇,宋向金,赵文祥..自适应特征选择的多扩张卷积网络轴承故障诊断[J].信息与控制,2024,53(5):615-630,16.基金项目
国家自然科学基金杰出青年科学基金(52025073) （52025073）
国家自然科学基金青年科学基金(52007078,62002140) （52007078,62002140）
江苏省自然科学基金(BK20200887) （BK20200887）
江苏省双创博士计划 （）
