Xueting Fu
Research Subjects
Research Interests
Xueting Fu has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Bingzhou Ma
Jiangsu Province Key Laboratory of Aerospace Power System, College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
1 shared publication - Daming Deng
Jiangsu Province Key Laboratory of Aerospace Power System, College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
1 shared publication - Feng Lu
College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
1 shared publication - Haoran An
Jiangsu Province Key Laboratory of Aerospace Power System, College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
1 shared publication - Qiuhong Li
Jiangsu Province Key Laboratory of Aerospace Power System, College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
1 shared publication
Latest Publications
- Open Access
Health Assessment for Gas Turbines Based on Domain-Adversarial Neural Network
To address the challenges of limited access to full-life-cycle data and insufficient labeled samples in gas turbine health management, a Bidirectional Long Short-Term Memory-Domain Adversarial Neural Network (BiLSTM-DANN) is adopted to achieve cross-domain health assessment for gas turbines. The model extracts temporal health features with a two-layer BiLSTM network and integrates DANN to achieve cross-domain feature alignment, thereby learning domain-invariant health representations. The simulation results demonstrate that the BiLSTM-DANN model outperforms the traditional BiLSTM and DCNN models on both the FD001 and FD003 datasets of C-MAPSS. Health assessment tests conducted on real gas turbine operation datasets indicate that the BiLSTM-DANN model can effectively depict the long-term operational health evolution trend of the entire unit and accurately reflect the health changes of the gas turbine before and after water washing. Therefore, the method studied in this paper provides a transferable solution for assessing the health of the entire gas turbine under conditions of scarce labels.
Open AccessHealth Assessment for Gas Turbines Based on Domain-Adversarial Neural Network
To address the challenges of limited access to full-life-cycle data and insufficient labeled samples in gas turbine health management, a Bidirectional Long Short-Term Memory-Domain Adversarial Neural Network (BiLSTM-DANN) is adopted to achieve cross-domain health assessment for gas turbines. The model extracts temporal health features with a two-layer BiLSTM network and integrates DANN to achieve cross-domain feature alignment, thereby learning domain-invariant health representations. The simulation results demonstrate that the BiLSTM-DANN model outperforms the traditional BiLSTM and DCNN models on both the FD001 and FD003 datasets of C-MAPSS. Health assessment tests conducted on real gas turbine operation datasets indicate that the BiLSTM-DANN model can effectively depict the long-term operational health evolution trend of the entire unit and accurately reflect the health changes of the gas turbine before and after water washing. Therefore, the method studied in this paper provides a transferable solution for assessing the health of the entire gas turbine under conditions of scarce labels.