Gear fault classification method based on improved SAGGAN model
It was difficult to acquire gear fault samples,and it compromised the reliability and accuracy of deep learning-driven fault classification models,therefore a semi-supervised gear failure classification model called improved self-attention and gate unit generated adversarial network(SAGGAN)built upon the improvements to the self-attention mechanism was proposed.Firstly,in order to enhance the feature representation capabilities of the proposed SAGGAN model and consequently improve the semi-supervised classification performance for gear failures,the enhancements were made to the existing self-attention generative adversarial network(SAGAN)framework by incorporating gated channel transformation(GCT),refining self-attention gate modules(SAG),and integrating pre-trained Inception V3 branches.Then,the vibration signals were collected from a gear failure experimental apparatus,capturing data across four states:gear breakage,wear,pitch error,and normal operation.The collected data was then partitioned into training,validation,and test sets for further analysis.Finally,the performance of the proposed SAGGAN model was compared against existing semi-supervised classification methods such as TripleGAN,Bad-GAN,Reg-GAN,and SF-GAN.Additionally,a study on the effectiveness of the enhancement modules was conducted through ablation experiments.The research results indicate that the improved SAGGAN model achieves significantly higher overall classification accuracy,particularly demonstrating superiority when the number of labeled samples is limited.Specifically,at label sample sizes of 40,60,80,and 100,the overall classification accuracies of the improved SAGGAN model are respectively89％,90％,92％,and 94.25％,which surpasses the performance of the other four methods.This suggests that the improved SAGGAN model can effectively enhance classification performance,especially in scenarios with a limited number of labeled samples.The above results reveal the practicality and superiority of the improved SAGGAN model in the field of gear fault classification.
gear faultpattern classificationself-attention and gate unit generated adversarial network(SAGGAN)semi-supervised learningself-attention generative adversarial network(SAGAN)gated channel transformation(GCT)self-attention gate modules(SAG)
万方数据
维普
