Few-Shot Transfer Learning With Attention Mechanism for High-Voltage Circuit Breaker Fault Diagnosis

Abstract

Data-driven artificial intelligence methods, especially convolutional neural networks (CNNs), have achieved excellent performance in high-voltage circuit breaker (HVCB) fault diagnosis. However, CNN relies heavily on massive data. When the amount of data decreases, the fault diagnosis performance drops severely. To settle these problems, a few-shot transfer learning (FSTL) with attention mechanism (AM) to realize the mechanical fault diagnosis of HVCBs is proposed. First, a one-dimensional CNN with AM is used to extract the fault features of HVCBs. The introduction of the AM makes CNN pay more attention to the interesting part of the fault signal to extract discriminative features. Then, domain adaptive transfer learning is used to realize a reliable diagnosis of HVCBs in small samples.The subdomain adaptation is adopted to adjust the distribution of related subdomains under the same category. The proposed subdomain adaptation can not only align the global distribution well but also effectively align the distribution of the same category of subdomains. Experimental results show that the FSTL proposed can achieve highly accurate and robust fault diagnosis of HVCBs with few-shot on-site. Compared with the traditional methods, the FSTL is obvious and provides a reliable reference for the diagnosis of HVCBs.