Various types of turbine engines have been chosen as the primary power source of the long-endurance unmanned aerial vehicles (UAVs) because of their high propulsive efficiency and low specific fuel consumption. To ensure the healthy operation of UAV turbine engines, rotor unbalance should be monitored and constrained to a preset limit. This paper proposes an efficient and physically interpretable method to achieve rotor unbalance monitoring. This method enables the frequency response function (FRF) to inform the neural network design, bringing the physics-informed convolutional neural network (PICNN). Firstly, the FRF gives a qualitative judgment of the axial positions of dominant faulty parts. Then, the following subnet proceeds to achieve quantitative identification. This method is demonstrated on a series of numerical cases and on a twin-disk rotor-bearing-casing experimental setup with anisotropic supporting stiffness. This setup is representative of engine installation status on the UAV platform. The results show that the PICNN can achieve higher precision compared to pure data-driven or model-based benchmarks. The PI layer does not require a high-fidelity model that generates responses identical to the actual ones. The robustness against modeling errors in stiffness and damping ratios is demonstrated. The achieved relative errors are less than 1.5% under various experimental datasets.
Zhou et al. (Mon,) studied this question.