谐波诊断技术和DCGAN-AIexNet电机劣化等级分类

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中图分类号：TM307 文献标志码：A 文章编号：1672-1098（2025）01-0049-08

Abstract：Objective To addressthe problems of unevensamples of motor deterioration class and low accuracy of deterioration clasification.Methods A degradation classification method combining harmonic diagnosis technique and improved DCGAN-AlexNet was proposed.Firstly，inorder to solve the imbalance of motordeteriorationsamples，a Wasserstein distance-based deep convolutional generative adversarial network（ W- DCGAN）was established for sample data augmentation so as to expand the dataset.Secondly，a modification was made on the basis of the traditional AlexNet network byapplying batch normalisation to change theconvolutional kernel size，simplifying the fullyconnected layer and adding a random deactivation layer（DropOut）．The modified model performed beter feature extraction to enhance the feature learning capability by adding the Attntion Mechanism Module（CBAM）after the normalisation.Finally，the efectiveness of the proposed model was experimentally verified.Results The amount of parameters of the modified CBAM-AlexNet network model was reduced to 56% of the originalone，improving efectivelytherecognition accuracyof the motor deterioration classification under thesmall sampleconditions.Conclusion The combination of harmonic diagnosis technology and the improved DCGAN-Alex

Net，with smallmodel and high recognition accuracy，provides anew idea and eficient solution for the develop ment of motor deterioration class diagnosis technology.

Key Words： Harmonic faults；Deep learning；Image classification；AlexNet network

随着工业技术的不断进步，生产设备的更新迭代已成为现代制造业的主要趋势。（剩余9402字）