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

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Chinese Journal of Mechanical Engineering 2024年第4期37卷 215-230页

作者： Xiancheng Zhang Xin Pan Hao Zeng Haofu Zhou Beijing Key Laboratoryof Health Monitoring and Self-recovery for High-end Mechanical Equipment Beijing University of Chemical TechnologyBeijing 100029China Engineering Research Center of Chemical Safety(Ministry of Education) Beijing University of Chemical TechnologyBeijing 100029China Engineering Management Department of China Three Gorges New Energy(Group)co. Ltd.Beijing 101199China

Intelligent fault diagnosis is an important method in rotating machinery fault diagnosis and equipment health *** 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 ***,the time-and frequency-domain features of several co-frequency vibration faults are summarized based on the mechanism analysis and calculated using actual vibration *** 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 ***,ResNet is used as the feature extractor for the ensembled ResNet-SVM *** extraction is carried out twice,and the extracted co-frequency fault features are more ***,the data augmentation method and ensemble ResNet-SVM are combined for fault diagnosis and compared with other *** 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

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