XMDCA-TL: An Explainable Multi-Domain Channel Attention Transfer Learning Framework for Fault Diagnosis in Industrial Gas Turbines

Abstract

In this study, a multi-task, interpretable transfer learning framework, XMDCA-TL, is proposed for fault diagnosis in industrial gas turbines. In the proposed method, the vibration time waveform is first converted into a multi-domain RGB representation comprising time, frequency, and time-frequency domains. A ConvNeXtV2-based encoder then processes these images, and the Multi-Domain Channel Attention (MDCA) mechanism is applied to its deep layers to model interactions among different domains and complementary dependencies in the signals. To improve the quality of the learned representations and enhance the model's robustness to noise, a self-supervised strategy based on hybrid masking, along with a UNet-based decoder to reconstruct the masked regions, has been designed. To overcome the limitation of labeled data in industrial environments, transfer learning was employed to transfer knowledge from laboratory data to real-world data from a 42.2 MW MGT-40 gas turbine at the Zahedan power plant. Additionally, a comprehensive Explainable Artificial Intelligence (XAI) framework was developed to analyze decision-making regions, evaluate domain importance, examine the flow of attention between domains, and assess reconstruction uncertainty. The results showed that XMDCA-TL, while achieving satisfactory fault diagnosis performance, possesses domain adaptability and robustness to noise and provides a physical interpretation of the model's decision-making process.