Journal of Shanghai Jiao Tong University ›› 2026, Vol. 60 ›› Issue (5): 848-859.doi: 10.16183/j.cnki.jsjtu.2024.200

• Mechanical Engineering • Previous Articles     Next Articles

Fault Warning for Gas Turbine Combustion Chamber Based on Deep Transfer Learning

WU Yajun, KANG Yingwei()   

  1. College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China
  • Received:2024-05-29 Revised:2024-07-27 Accepted:2024-10-18 Online:2026-05-28 Published:2026-06-03
  • Contact: KANG Yingwei E-mail:controlkyw@126.com

Abstract:

Aiming at the inefficiency of gas turbine combustion chamber fault warning due to the scarcity of sample data, a method based on transfer learning and convolutional neural network-bidirectional gated recurrent unit (CNN-BiGRU) network is proposed. First, the CNN-BiGRU network is pre-trained in the source domain using K-fold cross validation and the optimal model is filtered out. Then, the target domain fault warning model is obtained using transfer learning. The target domain data is imported into the model and a sliding window is used to reduce the false alarms caused by the abnormal data points. Finally, the fault occurrence is determined by the warning thresholds calculated using the triple standard deviation (3-sigma) method. The experimental results show that compared with the non-transfer learning model, the proposed method reduces the root mean square error by 87.5% and the mean absolute error by 89.05%, while improving the R-squared by 6.39% under conditions of insufficient sample data. In addition, compared with the system alarm moment, it can detect the signs of faults earlier, providing a fault warning for the gas turbine combustion chamber 82 min in advance.

Key words: gas turbine, combustion chamber, fault warning, transfer learning, convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU) network

CLC Number: