Hybrid sampling feature enhancement: a few-shot learning method for substation equipment fault recognition

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Yongjie Zhai, Zhedong Hu, Tian Wang, Yaru Wang

Published: 01 Jan 2023, Last Modified: 07 Nov 2025Multim. Tools Appl. 2023EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Due to the small sample size and unbalanced distribution, fault recognition of substation equipment becomes difficult. A few-shot feature enhancement method is proposed and applied on hybrid sampling feature enhancement model. First, Gaussian sampling structure and Gumbel-SoftMax sampling structure are designed based on the constructed intra-class covariance matrix which represents the semantic direction. After that, Hybrid sampling feature enhancement (HSFE) model is proposed, and the loss upper bound of the model is deduced. Finally, those proposed design methods can be implemented. Experiments confirm the effectiveness and improvement of fault recognition rate for substation equipment compared to existing approaches, especially the value of mAP is 4% higher than the baseline model.