基于RSAN和EEMD的MKSVM多模态融合旋转机械故障分类

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振动与冲击 ›› 2025, Vol. 44 ›› Issue (9) : 290-302.

故障诊断分析

基于RSAN和EEMD的MKSVM多模态融合旋转机械故障分类

刘东*1, 2 谭鋆1, 2 龙小波1, 2 赵杰3, 赵志高4

作者信息 +

MKSVM multimode fusion rotating machinery fault classification method based on RSAN and EEMD

LIU Dong*1,2, TAN Yun1,2, LONG Xiaobo1,2, ZHAO Jie3, ZHAO Zhigao4

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摘要

针对真实场景下旋转机械的故障振动信号存在样本数量不足和具备较强复杂性的问题，思考单一模态下故障诊断的限制，提出一种基于残差空间注意力网络(residual spatial attention network，RSAN)和集合经验模式分解(ensemble empirical mode decomposition，EEMD)的多核支持向量机(multi-kernel support vector machine，MKSVM)多模态融合旋转机械故障分类方法。首先，对原始数据进行小波阈值去噪处理，将降噪的一维信号转化为二维递归图；然后，设计RSAN结构，输入训练集的递归图进行训练，捕获故障信号的递归特征；随后，计算EEMD的固有模态函数（intrinsic mode function，IMF）的样本熵，基于其变异系数来选取故障信号的时域特征。最后，利用MKSVM对多模态特征向量进行高维映射和融合，完成旋转机械的故障分类。结果表明，在少样本的转子和轴承故障数据上准确率分别达到100%和98.3%，证明该方法具备良好的可行性。

Abstract

In order to solve the problems of insufficient samples and high complexity of faulty vibration signals of rotating machines in real scenarios, a multimodal fusion rotating machine fault classification method with multi-kernel support vector machine (MKSVM) based on residual space attention network (RSAN) and ensemble empirical mode decomposition (EEMD) was proposed. First, wavelet thresholding denoising was performed on the raw data to transform the noise-reduced one-dimensional signal into a two-dimensional recurrence map; Then, the RSAN structure was designed to be trained by inputting the recurrence graph of the training set to capture the recurrence features of the fault signals; Subsequently, the sample entropy of the intrinsic modal function (IMF) of the EEMD was calculated and the time-domain characteristics of the fault signal were selected based on its coefficient of variation. Finally, a MKSVM was used to perform high-dimensional mapping and fusion of multimodal feature vectors to complete the fault classification of rotating machinery. It was shown that the accuracy reaches 100% and 98.3% prediction performance on rotor and bearing failure data with few samples, respectively, which proves that the method possesses good feasibility.

导出引用

刘东1, 2 谭鋆1, 2 龙小波1, 2 赵杰3, 赵志高4. 基于RSAN和EEMD的MKSVM多模态融合旋转机械故障分类[J]. 振动与冲击, 2025, 44(9): 290-302

LIU Dong1, 2, TAN Yun1, 2, LONG Xiaobo1, 2, ZHAO Jie3, ZHAO Zhigao4. MKSVM multimode fusion rotating machinery fault classification method based on RSAN and EEMD[J]. Journal of Vibration and Shock, 2025, 44(9): 290-302

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