Robust Fault Diagnosis for Gas Turbine Rotor via Transfer Reinforcement Learning

As the central component of a significant power machine, the gas turbine's fault diagnosis accuracy is critical to the equipment's safety in service. However, the fault detection of the gas engine rotor system still faces challenges due to the difficulty of acquiring sensitive features and the lack of labeled data. To address these issues, we propose an improved DQN-based Transfer Reinforcement Learning method (Transfer-DQN) for robust gas turbine rotor fault diagnosis. The proposed method takes the collected one-dimensional raw vibration signal as input, with the fault sample set and fault category serving as the model environment and action. It uses a multi-scale one-dimensional wide convolutional neural network (M-WDCNN) with ∊-greedy strategy for Q-network fitting and decision making. Additionally, to consider the computational efficiency and differences between fault classes, Transfer-DQN uses multiple fault sample data as the source domain and a single fault class as the target domain, while performing the source-to-target domain transfer learning based on generative adversarial. Extensive experiments on the bearing dataset of Western Reserve University and our gas turbine test bench demonstrate the superiority of Transfer-DQN, achieving accuracies of 98.95% and 96.91 %, respectively. Compared with baseline approaches, our method breaks through the previous upper limit of 95 % to meet the need for robust and efficient fault diagnosis.