Intelligent Fault Diagnosis Method Based on Meta-Transfer Learning
In practical engineering,it may be difficult and expensive to acquire numerous fault samples,that is,the fault samples used for training are relatively few,which makes it difficult for the fault diagnosis mod-els trained by the traditional deep learning methods to quickly adapt to the new working conditions.There-fore,an intelligent fault diagnosis method based on meta-transfer learning is proposed.Firstly,a well-de-signed self-attention network(SAN)that effectively extracts fault features is pre-trained.Secondly,a base-leaner and a meta-learner are trained on each"N-way M-shot"meta-task to adaptively adjust the parame-ters of the pre-trained SAN model.Finally,the effectiveness of the proposed method is verified through dif-ferent few-shot fault diagnosis(FSFD)tasks.The results prove that the method achieves high diagnosis ac-curacies under cross-working condition FSFD tasks.
万方数据
维普
