Rolling bearings,as a type of precision mechanical component,are widely used in modern industrial machinery and equipment.It is of great significance to diagnose bearing faults using reasonable methods during bea-ring operation.However,in the actual complex and ever-changing environment,the collection of vibration signals often faces challenges such as limited sample sizes,noise interference,and operating condition variations,resulting in low fault diagnosis accuracy.To address the problem of small-sample rolling bearing fault diagnosis under noise interference and variable operating conditions,this paper proposed a meta-learning denoising model based on proto-type domain enhancement(Meta-DAE).Firstly,a small-sample fault dataset based on time-frequency diagrams was constructed,and a deep convolutional generative adversarial network was introduced for data preprocessing to gene-rate a pseudo-sample set with a similar distribution.Then,the fault sample set was input into Meta-DAE for adap-tive feature extraction.Meta-DAE adopts a prototype domain enhancement strategy to make prototype points of the same category more closely clustered in the embedding space.At the same time,an encoder with noise reduction performance was constructed,and a target function based on prototype domain enhancement and denoising was designed.By fine-tuning the model under small-sample conditions,the noise robustness and classification accuracy of the model were improved.Experimental results of small-sample fault diagnosis under noise interference and vari-able operating conditions show that,compared to other models,the proposed model demonstrates strong noise robustness.Under-8 dB strong noise interference,the model achieves a classification accuracy improvement of 35.78%to 57.25%using only 10 samples for fine-tuning.
维普
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