A novel U-Net and capsule network for few-shot high-voltage circuit breaker mechanical fault diagnosis

Deep learning methods have achieved noteworthy seeing results in the mechanical fault diagnosis of high-voltage circuit breakers with the recent advancements in artificial intelligence. However, the premise of the above method for obtaining excellent performance is to have sufficient samples, which is impractical due to the characteristics of the high-voltage circuit breakers. This study proposes a novel U-Net with CapsNet for high-voltage circuit breakers fault diagnosis to resolve these issues, achieving a high-precision and robust diagnosis of few-shot high-voltage circuit breakers. In a few-shot diagnosis, the U-Net with CapsNet takes advantage of the high accuracy of the U-Net. The capsule network is used in the contraction and expansion paths of the original U-Net to reduce the loss of features in the pooling process. The forward transfer from the bottom to the high-level capsule is completed by the dynamic routing algorithm. The feature information in the high-level capsule is unified with the bottom layer. The experimental results show that using the U-Net with CapsNet proposal, we can quickly and accurately realize the fault diagnosis of few-shot high-voltage circuit breakers, with an accuracy of 93.25%. The model has faster convergence speed and better stability, which provides a reliable solution for efficient and accurate fault diagnosis of high-voltage circuit breakers compared with traditional methods.