Gas path fault diagnosis for gas turbine group based on deep transfer learning

Gas turbines are widely used in power generation. To ensure reliability, data-driven diagnosis has become increasingly popular. However, sufficient historical data are unavailable, especially for newly-run gas turbines. An intuition arises regarding whether data from gas turbine group with long-term operation and abundant historical data, is helpful for newly-run gas turbines. Inspired by transfer learning, this paper proposes a novel method to identify the health states of newly-run gas turbines by transferring shared knowledge from data-rich gas turbines. Convolutional neural network (CNN) is employed to extract fault knowledge from data-rich gas turbines. Then, the trained CNN is finetuned with a few data from newly-run gas turbines. Experiment is presented on six datasets from simulation platform with identical-type and different-type gas turbines. Experiment shows that it improves diagnostic accuracy of newly-run gas turbines by 4.22–7.39% compared with conventional methods. Transferability and visualization analysis reveal the shared knowledge is transferred effectively.