高技术通讯2025，Vol.35Issue(12)：1364-1374,11.DOI:10.3772/j.issn.1002-0470.2025.12.009
时频图像多模态的电机故障特征提取融合算法
Motor fault feature extraction fusion algorithm based on multimodal time-frequency images
摘要
Abstract
Traditional unimodal detection methods are susceptible to noise interference,whereas multimodal approaches can effectively integrate information from different modalities.This paper proposes a multimodal motor fault feature extraction and fusion algorithm based on time-frequency images.The proposed method collects data from three mo-dalities-vibration,sound,and current-of a three-phase asynchronous motor and extracts features from the corre-sponding time-frequency signals.Feature extraction involves two approaches:first,time-frequency analysis is used to generate image samples,and a deep residual network(ResNet)is employed for image feature extraction;sec-ond,wavelet transform is applied to extract localized information from the time-frequency signals,removing high-frequency noise components caused by environmental factors.The two sets of features are then fed into an autoen-coder for dimensionality reduction and redundancy removal,ultimately achieving feature fusion.Finally,a support vector machine classifier is used to classify the fused features,and assess classification accuracy.Experimental re-sults demonstrate that,compared to unimodal classification networks,the proposed method achieves higher classifi-cation accuracy,supporting the effectiveness and feasibility of the approach.关键词
故障诊断/多模态/特征提取/卷积神经网络/小波变换Key words
fault diagnosis/multimodal/feature extraction/convolutional neural network/wavelet transform引用本文复制引用
王付安,占天行,赵媛媛,冯坤,吴斯琪,杨雨琦..时频图像多模态的电机故障特征提取融合算法[J].高技术通讯,2025,35(12):1364-1374,11.基金项目
国家级重点课题(SQ2024YFC3000042)资助项目. （SQ2024YFC3000042）
