[1]JI Youchang,YUAN Weiwei,MAO Shanbin,et al.Partial domain adversarial adaptation networks for imbalanced small samples in aeroengine vibration prediction[J].CAAI Transactions on Intelligent Systems,2023,18(5):1005-1016.[doi:10.11992/tis.202210030]
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Partial domain adversarial adaptation networks for imbalanced small samples in aeroengine vibration prediction

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 5 Page number: 1005-1016 Column: 学术论文—机器学习 Public date: 2023-09-05
Title:
Partial domain adversarial adaptation networks for imbalanced small samples in aeroengine vibration prediction
Author(s):
JI Youchang1;  YUAN Weiwei1;  MAO Shanbin2;  REN Chunhong2;  GUAN Donghai1
1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
2. Beijing Power Machinery Institute, Beijing 100074, China
Keywords:
imbalanced data; domain adaptation; adversarial learning; vibration prediction; heterogeneous transfer learning; target domain; few-shot learning; feature space
CLC:
TP391.41；TP18
DOI:
10.11992/tis.202210030
Abstract:
In aeroengine vibration prediction, due to the imbalanced small samples of the actual assembly data, directly establishing an effective prediction model is difficult. Transfer learning can improve model performance in the target domain by transferring the knowledge of the source domain. Therefore, this study proposes an aeroengine vibration prediction model based on a partial domain adversarial adaptation network. Each domain is divided into multiple local domains according to labels. Through multiple local domain adversarial adaptation networks, the samples in the target domain can be mapped onto the source domain so that the minority class samples can be transferred correctly. The pseudo label is used to solve the domain transformation of the samples in the target domain, and the classifier of the source domain is used to provide a reliable prediction result. In this study, the validity and generalization of the proposed method are verified on several real datasets. Compared with other methods, the area under the curve and F1 of the vibration prediction model can be improved by approximately 15% on average.
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