A novel hybrid meta-learning for few-shot gas-insulated switchgear insulation defect diagnosis

Abstract

Though deep learning-based diagnosis methods have been extensively researched in gas-insulated switchgear (GIS) insulation defects diagnosis. Training a high-precision and robust GIS insulation defect diagnosis model under complex and few-shot conditions remains difficult. To address these issues, a novel hybrid meta-learning for a few-shot GIS insulation defect diagnosis is proposed. The meta Siamese network (MSN) is designed by incorporating the model-agnostic meta-learning (MAML) into the Siamese network (SN). Firstly, a SN is designed to acquire knowledge of GIS insulation defect diagnosis. To capture the discriminative characteristics, the attention mechanism is introduced into SN to reduce the attention to irrelevant information. Then, the model parameters are optimized through MAML. To ensure the model’s optimal performance, the meta-stochastic gradient descent is introduced to realize optimizer learning in an end-to-end form. The experimental results show that when the support sets is 5, MSN can achieve 93.15% accuracy, which outperforms other methods. Furthermore, the issue of unbalanced samples is effectively avoided, providing a feasible solution for the few-shot GIS insulation defects.