IDEAS home Printed from https://ideas.repec.org/r/eee/energy/v200y2020ics0360544220305740.html

Fault diagnosis of gas turbine based on partly interpretable convolutional neural networks

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Soleimani, Mohammadjavad & Irani, Fatemeh Negar & Yadegar, Meysam & Meskin, Nader, 2025. "Comprehensive review of gas turbine fault diagnostic strategies," Applied Energy, Elsevier, vol. 401(PC).
  2. Paweł Ziółkowski & Marta Drosińska-Komor & Jerzy Głuch & Łukasz Breńkacz, 2023. "Review of Methods for Diagnosing the Degradation Process in Power Units Cooperating with Renewable Energy Sources Using Artificial Intelligence," Energies, MDPI, vol. 16(17), pages 1-28, August.
  3. Cheng, Xianda & Zheng, Haoran & Yang, Qian & Zheng, Peiying & Dong, Wei, 2023. "Surrogate model-based real-time gas path fault diagnosis for gas turbines under transient conditions," Energy, Elsevier, vol. 278(PA).
  4. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
  5. Lin, Meng & Li, Jiangkuan & Li, Yankai & Wang, Xu & Jin, Chengyi & Chen, Junjie, 2023. "Generalization analysis and improvement of CNN-based nuclear power plant fault diagnosis model under varying power levels," Energy, Elsevier, vol. 282(C).
  6. Chen Zhang & Tao Yang, 2023. "Anomaly Detection for Wind Turbines Using Long Short-Term Memory-Based Variational Autoencoder Wasserstein Generation Adversarial Network under Semi-Supervised Training," Energies, MDPI, vol. 16(19), pages 1-18, October.
  7. Huang, Yufeng & Sun, Gang & Rabczuk, Timon & Zhuang, Xiaoying, 2026. "Propagation and evolution graph method embedded with physical constraints for multi-factor coupled deep fault diagnosis in aero-engines," Energy, Elsevier, vol. 342(C).
  8. Muhammad Baqir Hashmi & Mohammad Mansouri & Amare Desalegn Fentaye & Shazaib Ahsan & Konstantinos Kyprianidis, 2024. "An Artificial Neural Network-Based Fault Diagnostics Approach for Hydrogen-Fueled Micro Gas Turbines," Energies, MDPI, vol. 17(3), pages 1-23, February.
  9. Huang, Yufeng & Tao, Jun & Zhao, Junyi & Sun, Gang & Yin, Kai & Zhai, Junyi, 2023. "Graph structure embedded with physical constraints-based information fusion network for interpretable fault diagnosis of aero-engine," Energy, Elsevier, vol. 283(C).
  10. Aygun, Hakan & Dursun, Omer Osman & Toraman, Suat, 2023. "Machine learning based approach for forecasting emission parameters of mixed flow turbofan engine at high power modes," Energy, Elsevier, vol. 271(C).
  11. Liu, Jiaquan & Hou, Lei & Zhang, Rui & Sun, Xingshen & Yu, Qiaoyan & Yang, Kai & Zhang, Xinru, 2023. "Explainable fault diagnosis of oil-gas treatment station based on transfer learning," Energy, Elsevier, vol. 262(PA).
  12. Chen, Jinwei & Hu, Zhenchao & Chen, Yifan & Zhang, Huisheng, 2026. "A novel dual-knowledge embedded graph convolutional network method combining knowledge quantification for gas turbine gas path analysis," Energy, Elsevier, vol. 342(C).
  13. Huang, Yufeng & Tao, Jun & Sun, Gang & Wu, Tengyun & Yu, Liling & Zhao, Xinbin, 2023. "A novel digital twin approach based on deep multimodal information fusion for aero-engine fault diagnosis," Energy, Elsevier, vol. 270(C).
  14. Thanh Dam Mai & Jaiyoung Ryu, 2021. "Effects of Damaged Rotor Blades on the Aerodynamic Behavior and Heat-Transfer Characteristics of High-Pressure Gas Turbines," Mathematics, MDPI, vol. 9(6), pages 1-21, March.
  15. Żymełka, Piotr & Szega, Marcin, 2020. "Issues of an improving the accuracy of energy carriers production forecasting in a computer-aided system for monitoring the operation of a gas-fired cogeneration plant," Energy, Elsevier, vol. 209(C).
  16. Wang, Pengfei & Zhang, Jiaxuan & Wan, Jiashuang & Wu, Shifa, 2022. "A fault diagnosis method for small pressurized water reactors based on long short-term memory networks," Energy, Elsevier, vol. 239(PC).
  17. Cheng, Xianda & Jian, Menghua & Dong, Wei & Yan, Hongming, 2025. "Gas turbine gas path fault diagnosis based on open-set recognition and physics-data fusion," Energy, Elsevier, vol. 340(C).
  18. Irani, Fatemeh Negar & Soleimani, Mohammadjavad & Yadegar, Meysam & Meskin, Nader, 2024. "Deep transfer learning strategy in intelligent fault diagnosis of gas turbines based on the Koopman operator," Applied Energy, Elsevier, vol. 365(C).
  19. Zhou, Dengji & Jia, Xingyun & Ma, Shixi & Shao, Tiemin & Huang, Dawen & Hao, Jiarui & Li, Taotao, 2022. "Dynamic simulation of natural gas pipeline network based on interpretable machine learning model," Energy, Elsevier, vol. 253(C).
  20. Chen, Yu-Zhi & Zhang, Wei-Gang & Tsoutsanis, Elias & Zhao, Junjie & Tam, Ivan C.K. & Gou, Lin-Feng, 2025. "An advanced performance-based method for soft and abrupt fault diagnosis of industrial gas turbines," Energy, Elsevier, vol. 321(C).
  21. Attallah, Omneya & Ibrahim, Rania A. & Zakzouk, Nahla E., 2023. "CAD system for inter-turn fault diagnosis of offshore wind turbines via multi-CNNs & feature selection," Renewable Energy, Elsevier, vol. 203(C), pages 870-880.
  22. Long, Zhenhua & Bai, Mingliang & Ren, Minghao & Liu, Jinfu & Yu, Daren, 2023. "Fault detection and isolation of aeroengine combustion chamber based on unscented Kalman filter method fusing artificial neural network," Energy, Elsevier, vol. 272(C).
  23. Chen, Yu-Zhi & Tsoutsanis, Elias & Xiang, Heng-Chao & Li, Yi-Guang & Zhao, Jun-Jie, 2022. "A dynamic performance diagnostic method applied to hydrogen powered aero engines operating under transient conditions," Applied Energy, Elsevier, vol. 317(C).
  24. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2023. "How to improve the application potential of deep learning model in HVAC fault diagnosis: Based on pruning and interpretable deep learning method," Applied Energy, Elsevier, vol. 348(C).
  25. Chen, Yu-Zhi & Zhao, Xu-Dong & Xiang, Heng-Chao & Tsoutsanis, Elias, 2021. "A sequential model-based approach for gas turbine performance diagnostics," Energy, Elsevier, vol. 220(C).
  26. Zhao, Junjie & Li, Yi-Guang & Sampath, Suresh, 2023. "A hierarchical structure built on physical and data-based information for intelligent aero-engine gas path diagnostics," Applied Energy, Elsevier, vol. 332(C).
  27. Zhang, Jingjing & Li, Jian & Li, Xuemin, 2025. "An agent composed of data model and thermodynamic model for multi-component degradation identification of gas turbine online," Energy, Elsevier, vol. 335(C).
  28. Gong, Junhua & Shi, Guoyun & Wang, Shaobo & Wang, Peng & Chen, Bin & Chen, Yujie & Wang, Bohong & Yu, Bo & Jiang, Weixin & Li, Zongze, 2024. "Efficient super-resolution of pipeline transient process modeling using the Fourier Neural Operator," Energy, Elsevier, vol. 302(C).
  29. Patnaik, Bhaskar & Mishra, Manohar & Bansal, Ramesh C. & Jena, Ranjan K., 2021. "MODWT-XGBoost based smart energy solution for fault detection and classification in a smart microgrid," Applied Energy, Elsevier, vol. 285(C).
  30. Li, Jiangkuan & Lin, Meng & Wang, Bo & Tian, Ruifeng & Tan, Sichao & Li, Yankai & Chen, Junjie, 2024. "Open set recognition fault diagnosis framework based on convolutional prototype learning network for nuclear power plants," Energy, Elsevier, vol. 290(C).
  31. Yu, Bosheng & Cao, Li'ang & Xie, Daxing & Chen, Jinwei & Zhang, Huisheng, 2025. "Fault diagnosis of gas turbine based on feature fusion cascade neural network," Energy, Elsevier, vol. 321(C).
  32. Wei, Zhiyuan & Zhang, Shuguang & Jafari, Soheil & Nikolaidis, Theoklis, 2022. "Self-enhancing model-based control for active transient protection and thrust response improvement of gas turbine aero-engines," Energy, Elsevier, vol. 242(C).
  33. Aliyon, Kasra & Rajaee, Fatemeh & Ritvanen, Jouni, 2023. "Use of artificial intelligence in reducing energy costs of a post-combustion carbon capture plant," Energy, Elsevier, vol. 278(PA).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.