Intelligent Diagnosis Method for Typical Co-frequency Vibration Faults of Rotating Machinery Based on SAE and Ensembled ResNet-SVM  

作　　者：Xiancheng Zhang Xin Pan Hao Zeng Haofu Zhou 

机构地区：[1]Beijing Key Laboratoryof Health Monitoring and Self-recovery for High-end Mechanical Equipment,Beijing University of Chemical Technology,Beijing 100029,China [2]Engineering Research Center of Chemical Safety(Ministry of Education),Beijing University of Chemical Technology,Beijing 100029,China [3]Engineering Management Department of China Three Gorges New Energy(Group)Co.,Ltd.,Beijing 101199,China

出　　处：《Chinese Journal of Mechanical Engineering》2024年第4期215-230,共16页中国机械工程学报（英文版）

基　　金：Supported by National Natural Science Foundation of China (Grant No.51875031);Beijing Municipal Natural Science Foundation (Grant No.3212010)。

摘　　要：Intelligent fault diagnosis is an important method in rotating machinery fault diagnosis and equipment health management.To deal with co-frequency vibration faults,a type of typical fault in rotating machinery,this paper proposes a fault diagnosis method based on the stacked autoencoder(SAE)and ensembled ResNet-SVM.Furthermore,the time-and frequency-domain features of several co-frequency vibration faults are summarized based on the mechanism analysis and calculated using actual vibration data.To realize and validate the high-precision diagnosis method of rotating equipment with co-frequency faults proposed in this study,the following three criteria are required:First,to improve the effectiveness and robustness of the ensembled model and the sliding window using data augmentation,adding noise,autoencoder(AE)and SAE methods are analyzed in terms of principle and practical effects.Second,ResNet is used as the feature extractor for the ensembled ResNet-SVM model.Feature extraction is carried out twice,and the extracted co-frequency fault features are more comprehensive.Finally,the data augmentation method and ensemble ResNet-SVM are combined for fault diagnosis and compared with other methods.The experimental results show that the accuracy of the proposed method can exceed 99.9%.

关 键 词：Co-frequency vribation Data argumentation Ensembeled ResNet-SVM High precision fault diagnosis 

分 类 号：TH17[机械工程—机械制造及自动化] TP18[自动化与计算机技术—控制理论与控制工程]