桂林电子科技大学学报2025，Vol.45Issue(1)：1-10,10.DOI:10.16725/j.1673-808X.2023180
融合二维频域与改进型ResNet的故障诊断模型
Fault diagnosis model integrating 2D frequency domain and improved residual networks
摘要
Abstract
Due to the constraints of data format,using deep learning models directly after fast Fourier transform on fault data often fails to obtain sufficient features,making it difficult to further improve the accuracy and generalization of fault diagnosis under vari-able operating conditions.To this end,a lightweight fault diagnosis model is proposed that integrates two-dimensional frequency do-main and improved residual networks.Before acquiring and learning more features,the first half of the symmetric spectrum convert-ed by the FFT method is intercepted and unfolded in a row first manner on a two-dimensional surface to obtain frequency domain data feature information represented by a matrix;Then ReLU6 is used as the Activation function of the improved residual networks to learn the matrix features;Finally,the final state feature information is mapped into a one hot encoding vector.The test on the data set of Case Western Reserve University shows that the diagnostic accuracy of this method is improved by 7.07%compared with the one-dimensional diagnostic model;Compared with the standard residual network,the fluctuation in accuracy has been reduced by 0.14%,improving the accuracy and stability of fault prediction.关键词
故障诊断/二维频域/残差网络/独热码/全局平均池化Key words
fault diagnosis/2D frequency domain/residual networks/one-hot encoding/GAP分类
计算机与自动化引用本文复制引用
王琳,贾飞,胡晓丽..融合二维频域与改进型ResNet的故障诊断模型[J].桂林电子科技大学学报,2025,45(1):1-10,10.基金项目
国家自然科学基金(62267003) （62267003）
桂林市科学研究与技术开发计划(2020011123) （2020011123）
