I am currently conducting research in the field of rotating machinery fault diagnosis, with a particular focus on the evolution mechanisms of bearing faults under non-stationary operating conditions and their intelligent diagnostic methods. My work integrates mechanical system dynamics, signal processing, and artificial intelligence techniques to reveal the fault development patterns of bearings in variable working conditions and to develop efficient and reliable intelligent diagnostic models, aiming to improve the operational safety and maintenance efficiency of industrial equipment. My academic interests include: Mechanism analysis and modeling of mechanical component faults, especially bearings, under non-stationary conditions; Intelligent fault diagnosis methods based on multi-sensor data, leveraging advanced algorithms such as machine learning and graph neural networks; Health condition assessment and predictive maintenance strategies for rotating machinery.