Physics-Informed Convolutional Neural Network for Localizing and Identifying Rotor Unbalance in the Long-Endurance UAV Turbine Engine

Zhou, Liang; Zhang, Dayi; Zhang, Qicheng; Zhang, Jingxuan; Wang, Cun

doi:10.3390/drones10030208

Open AccessArticle

Physics-Informed Convolutional Neural Network for Localizing and Identifying Rotor Unbalance in the Long-Endurance UAV Turbine Engine

by

Liang Zhou

¹

,

Dayi Zhang

¹

,

Qicheng Zhang

^1,*

,

Jingxuan Zhang

^2,* and

Cun Wang

³

¹

School of Energy and Power Engineering, Beihang University, Beijing 100191, China

²

Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China

³

Beijing Power Machinery Institute, Beijing 100074, China

^*

Authors to whom correspondence should be addressed.

Drones 2026, 10(3), 208; https://doi.org/10.3390/drones10030208

Submission received: 8 February 2026 / Revised: 10 March 2026 / Accepted: 12 March 2026 / Published: 16 March 2026

(This article belongs to the Section Drone Design and Development)

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Abstract

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.

Keywords: physics-informed neural network; rotor unbalance/imbalance; rotor dynamics; fault diagnosis; unmanned aerial vehicles

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MDPI and ACS Style

Zhou, L.; Zhang, D.; Zhang, Q.; Zhang, J.; Wang, C. Physics-Informed Convolutional Neural Network for Localizing and Identifying Rotor Unbalance in the Long-Endurance UAV Turbine Engine. Drones 2026, 10, 208. https://doi.org/10.3390/drones10030208

AMA Style

Zhou L, Zhang D, Zhang Q, Zhang J, Wang C. Physics-Informed Convolutional Neural Network for Localizing and Identifying Rotor Unbalance in the Long-Endurance UAV Turbine Engine. Drones. 2026; 10(3):208. https://doi.org/10.3390/drones10030208

Chicago/Turabian Style

Zhou, Liang, Dayi Zhang, Qicheng Zhang, Jingxuan Zhang, and Cun Wang. 2026. "Physics-Informed Convolutional Neural Network for Localizing and Identifying Rotor Unbalance in the Long-Endurance UAV Turbine Engine" Drones 10, no. 3: 208. https://doi.org/10.3390/drones10030208

APA Style

Zhou, L., Zhang, D., Zhang, Q., Zhang, J., & Wang, C. (2026). Physics-Informed Convolutional Neural Network for Localizing and Identifying Rotor Unbalance in the Long-Endurance UAV Turbine Engine. Drones, 10(3), 208. https://doi.org/10.3390/drones10030208

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Physics-Informed Convolutional Neural Network for Localizing and Identifying Rotor Unbalance in the Long-Endurance UAV Turbine Engine

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