TY - GEN
T1 - Fault Diagnosis of Less Oil Equipment Based on Domain Adversarial Transfer Learning Convolutional Neural Network
AU - Wang, Shuai
AU - Mu, Hai Bao
AU - Liu, Yan Qi
AU - Zhou, Jin Ming
AU - Lin, Hao Fan
AU - Zhang, Guan Jun
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Machine learning has made significant progress in main equipment fault diagnosis based on DGA data. However, due to characteristics such as less oil content, rapid fault progression, limited DGA data samples, and difficulty in obtaining fault samples, there is insufficient assessment methods for less oil equipment. This paper proposes a novel fault recognition method, domain adversarial transfer learning convolutional neural networks (DATLCNN). The proposed DATLCNN consists of a feature extractor based on residual convolutional neural networks (RCNN), a label classifier, and a domain discriminator. Through domain adversarial transfer learning, DATLCNN transfers the diagnostic knowledge learned by RCNN from main equipment DGA data to less oil equipment, achieving high-precision fault diagnosis for such equipment. Experimental results indicate that compared to other methods, DATLCNN achieves an accuracy of 86.6% with limited on-site less oil equipment DGA samples, demonstrating the effectiveness and accuracy of the proposed method.
AB - Machine learning has made significant progress in main equipment fault diagnosis based on DGA data. However, due to characteristics such as less oil content, rapid fault progression, limited DGA data samples, and difficulty in obtaining fault samples, there is insufficient assessment methods for less oil equipment. This paper proposes a novel fault recognition method, domain adversarial transfer learning convolutional neural networks (DATLCNN). The proposed DATLCNN consists of a feature extractor based on residual convolutional neural networks (RCNN), a label classifier, and a domain discriminator. Through domain adversarial transfer learning, DATLCNN transfers the diagnostic knowledge learned by RCNN from main equipment DGA data to less oil equipment, achieving high-precision fault diagnosis for such equipment. Experimental results indicate that compared to other methods, DATLCNN achieves an accuracy of 86.6% with limited on-site less oil equipment DGA samples, demonstrating the effectiveness and accuracy of the proposed method.
KW - convolutional neural networks
KW - domain adversarial transfer learning
KW - fault diagnosis
KW - less oil equipment
UR - https://www.scopus.com/pages/publications/85205728547
U2 - 10.1109/ICHVE61955.2024.10676075
DO - 10.1109/ICHVE61955.2024.10676075
M3 - 会议稿件
AN - SCOPUS:85205728547
T3 - 2024 IEEE International Conference on High Voltage Engineering and Applications, ICHVE 2024 - Proceedings
BT - 2024 IEEE International Conference on High Voltage Engineering and Applications, ICHVE 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Conference on High Voltage Engineering and Applications, ICHVE 2024
Y2 - 18 August 2024 through 22 August 2024
ER -