96 / 2021-10-23 09:25:12
Few-Shot Transfer Learning with Attention Mechanism for High-Voltage Circuit Breaker fault diagnosis
domain adaptive transfer learning, fault diagnosis, few-shot, high-voltage circuit breakers, one-dimensional convolutional neural network
Final Paper
Yanxin Wang / State Key Laboratory of Electrical Insulation and Power Equipment; Department of Electrical Engineering; Xi’an Jiaotong University
Jing YAN / State Key Laboratory of Electrical Insulation and Power Equipment, Department of Electrical Engineering, Xi’an Jiaotong University
Xinyu Ye / State Key Laboratory of Electrical Insulation and Power Equipment, Department of Electrical Engineering, Xi’an Jiaotong University
Qianzhen Jing / State Key Laboratory of Electrical Insulation and Power Equipment, Department of Electrical Engineering, Xi’an Jiaotong University
Jianhua Wang / State Key Laboratory of Electrical Insulation and Power Equipment, Department of Electrical Engineering, Xi’an Jiaotong University
Yingsan Geng / State Key laboratory of Electric Power Equipment; Xi’an Jiaotong University
Data-driven artificial intelligence methods, especially convolutional neural networks (CNNs), have achieved excellent performance in high-voltage circuit breaker mechanical fault diagnosis due to their powerful feature extraction and classification capabilities. However, CNN relies heavily on massive data. When the amount of data decreases, the fault diagnosis performance drops severely. To solve the above problems, this paper proposes a few-shot transfer learning (FSTL) with attention mechanism to realize the mechanical fault diagnosis of high-voltage circuit breakers. First, a one-dimensional CNN (1DCNN) with attention mechanism (AM) is used to extract the mechanical fault features of high-voltage circuit breakers. The introduction of the AM makes CNN pay more attention to the interesting part of the fault signal to extract effective key features. Then, domain adaptive transfer learning (DATL) is used to realize the deployment and application of 1DCNN constructed under a large amount of low-voltage level data to small samples of ultra-high voltage (UHV), so as to realize reliable diagnosis of UHV circuit breakers in small samples. The proposed DATL can consider the marginal distribution and conditional distribution of the two data at the same time to achieve better feature matching. Experimental results show that the FSTL proposed can achieve highly accurate and robust fault diagnosis of high-voltage circuit breakers with few-shot on site. Compared with the traditional method, the method proposed in this paper is obvious and provides a reliable reference for the diagnosis of high-voltage circuit breakers.
Important Date
  • Conference Date

    Jul 11

    2023

    to

    Aug 18

    2023

  • Nov 10 2021

    Draft paper submission deadline

  • Dec 10 2021

    Registration deadline

  • Dec 11 2021

    Contribution Submission Deadline

Sponsored By
IEEE IAS
Organized By
IEEE IAS Student Chapter of Southwest Jiaotong University (SWJTU)
IEEE IAS Student Chapter of Huazhong University of Science and Technology (HUST)
IEEE PELS (Power Electronics Society) Student Chapter of HUST