Rotor rubbing diagnosis method based on fusion of time-frequency features of blade tip clearance

High end turbomachinery represented by aero-engine and ship gas turbine tends to have a more compact and complex internal structure under high efficiency and large maneuverability flight conditions, and cause an increasing probability of internal rotor-stator rubbing. Due to the lack of inner measured points and difficulty in monitoring, it is often difficult to detect rubbing faults in a timely and accurate manner. Here, a rotor rubbing fault diagnosis method was proposed based on time-frequency features fusion of blade tip clearance. Firstly, blade tip clearance (BTC) sequence was obtained using blade tip vibration-clearance composite sensor, and then the abnormality of BTC sequence was evaluated with machine learning algorithm. If abnormal alarm appearing, Fourier decomposition was performed for abnormal signal and time-frequency energy spectra of various components were summarized. Finally, the occurrence of faults was determined with rubbing bands in time-frequency energy spectra. The experimental results showed that after exerting short-term and continuous rubbing conditions to rotor, the machine learning model can conduct fault diagnosis in real time, and the proposed method can fully distinguish the occurrence of the two rubbing conditions after alarm and the process of continuous rubbing slowly exerting and fast ending. Compared with the rotor Vibration signal, it was shown that the proposed method can obtain BTC through engine case measured point, and sense rotor rubbing faults to realize one sensor multiple messages" and provide a new method for condition monitoring and health management of turbomachinery. © 2024 Chinese Vibration Engineering Society. All rights reserved."