Semi-supervised small-sample gearbox fault diagnosis with privacy protection
Gearboxes,as a key component of mechanical drive systems,encounter problems of data leakage and low model accuracy when employing traditional fault diagnosis methods in terms of limited labeled data and data privacy protection requirements.Therefore,this study proposes a privacy-preserving framework based on federated learning using a small-sample gearbox fault diagnosis method that combines semi-supervised prototype networks with comparative learning.Initially,the DeceFL federated learning framework is constructed to produce positive and negative sample pairs using a limited number of labeled samples for each client.Meanwhile,the pretraining method of contrast learning is used to provide initialization parameters for the autoencoder.Subsequently,the autoencoder is used as a feature mapping function for the prototype network to compute the category prototype using limited labeled samples.Finally,category prototype is fine-tuned using the prototype refinement method to reduce the interference of abnormal data and obtain a more stable and accurate prototype.After validation with real gearbox data,the results show that the proposed semi-supervised small-sample fault diagnosis method can achieve better fault recognition accuracy with very few samples.This research provides an innovative approach to address data challenges in real industrial applications and promotes further research in the gearbox fault diagnosis field.
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