A Novel Hybrid Transfer Learning Approach for Small-Sample High-Voltage Circuit Breaker Fault Diagnosis On-site

Abstract

Although the data-driven fault diagnosis method has achieved perfect diagnosis of high-voltage circuit breakers (HVCBs) mechanical fault under the massive data built in the laboratory, it is still a challenge to train a high-precision and robust diagnosis model under the condition of small samples on-site at this stage. To solve the above issues, this paper proposes a novel hybrid transfer learning to realize small-sample HVCB fault diagnosis on-site. To fully learn domain discriminative features and domain matching, this paper simultaneously introduces domain adaptation transfer learning and domain adversarial training into small-sample HVCB diagnosis on-site. At the same time, the two kinds of feature transfer learning are combined through ensemble learning to get the final diagnosis result. To extract discriminative features that characterize HVCB faults, this paper constructs a one-dimensional attention residual convolutional neural network, which can ensure that the network pays attention to key features while fully extracting temporal fine-grained information. The experimental results show that the hybrid transfer learning proposed in this paper achieves 94.69% accuracy of small-sample HVCB fault diagnosis on-site, which is significantly higher than other methods. It has laid a solid foundation for small-sample HVCB fault diagnosis on-site.