Jul 2026· PHM Society European Conference· Vol 9, pp. 1-7· 1 citation· 11 references
Abstract
Propellers are critical to the safe operation of multicopter unmanned aerial vehicles (UAVs), as faults can decrease the efficiency of the propulsion system and affect flight performance. Depending on the type and extent of the fault, the effects can range from a slight reduction in performance to a significant loss of thrust that could compromise safety. Because of the limited amount of sensor data available on board a UAV, propeller damage cannot be measured directly. Therefore, a data-based prediction using available sensors is required. This paper focuses on establishing and predicting a health index for damaged propellers.A test bench is used to investigate the effects of two different types of damage: broken propeller tips and notches at the leading edge. Each type of damage is examined at three levels of severity. Based on sensor data collected from the test bench, a health index is defined to characterize the remaining performance of the damaged propellers. A two-stage approach for the data-based health prediction is implemented by first classifying the type of the propeller faults, and then employing a random forest regressor to estimate the remaining health.
This paper aims to identify the characteristics of two AXI 4130 brushless direct current (BLDC) motors and the propeller-motor performance of five Aeronaut CAM folding propellers. The resulting data sets can be used to select the optimal propeller for small Unmanned Aerial Vehicles.
The characteristics of AXI 4130–16 and AXI 4130–20 motors were identified under various supply voltages, as these motors support several battery-cell configurations. Following the motor characterization, five different propellers were tested on a LY-Micro-Max UAV Power System test bench using the selected AXI 4130–20 motor to estimate its thrust, torque, input power and revolutions per minute under static conditions.
The obtained data sets provide a reliable basis for optimizing propeller-motor combinations, predicting Unmanned Aerial Vehicle performance under static conditions and selecting the most efficient propeller. The presented analysis confirmed that the 18x11-inch propeller delivers the highest propulsion force and the best thrust-to-torque ratio. If this is used as the primary criterion, the selection of the optimal propeller for a small Unmanned Aerial Vehicle can be easily determined.
The data presented in this paper is novel and specifically tailored for small Unmanned Aerial Vehicle design.
This paper provides specific characteristics of BLDC motors that are often difficult to find in existing literature. The identified propeller characteristics can be used to select the optimal propeller for small Unmanned Aerial Vehicles.
Miodrag Milenković-Babić, Mario Silvagni, N. Ristić et al.· Aircraft Engineering and Aer...· 0 citations
In recent years, drone technology has seen widespread application in both civilian and military fields. By 2025, China will introduce supportive policies from multiple dimensions, including industrial development, technological innovation, and application promotion, to significantly increase the number of UAVs in use and their frequency. However, drones are prone to malfunctions due to factors such as bad weather and electromagnetic interference, which may result in serious consequences, including property damage and casualties. Therefore, improving the accuracy of fault detection and the response time of drones is of great significance. Although current research has made progress, there are still deficiencies: First, most of them rely on a single or limited data source, resulting in incomplete information and vulnerability to interference, which leads to low detection accuracy and reliability; Second, traditional methods are mostly based on fixed thresholds or simple rules, lacking real-time dynamic monitoring and adaptive analysis capabilities, making it difficult to issue timely warnings of potential faults. To this end, this study proposes a multi-scale time series prediction model based on multimodal and multi-branch, integrating multimodal data, constructing a dual-branch architecture, and combining deep learning and attention mechanisms to enhance the anomaly detection effect of unmanned aerial vehicles. A dual-branch anomaly detection model based on 1DCNN-BiLSTM and continuous wavelet transform is proposed, including a trajectory prediction difference branch and a full time series data branch. In the dual-branch output stage, the attention gating mechanism is utilized to fuse features and improve the detection performance. The experimental results show that this model performs excellently in both normal trajectory prediction and anomaly detection, providing an effective solution for drone anomaly detection.
Zhenglin Pu, Lin Zhang· SAE technical paper series· 0 citations
Accurate performance prediction of unmanned aerial vehicle (UAV) propellers under dynamic operating conditions is crucial for propulsion system matching and flight efficiency optimization. Traditional static models often struggle to capture complex nonlinear aerodynamic behaviors. This study aims to significantly improve the prediction accuracy of propeller performance under various working conditions using a data-driven approach. To this end, a physical experimental test bench was established to conduct comprehensive step-throttle tests on two different models of propellers, systematically collecting real-world dynamic response data. Based on the experimental dataset, a machine learning-based prediction model was constructed. The model directly utilizes battery voltage and throttle command as input features to simultaneously and accurately predict key output metrics, including mechanical power, rotational speed, and overall efficiency. Experimental validation demonstrates that the proposed machine learning model exhibits excellent prediction accuracy and highly robust curve-fitting capabilities across different throttle steps. The results indicate that this data-driven framework effectively overcomes the limitations of conventional analytical methods, providing a highly reliable and efficient tool for evaluating and optimizing UAV propulsion systems.
Xiaoyang Du, Zhikun Xu, Zhendong Sun· 2026 IEEE 27th China Confere...· 0 citations
Abstract. This work deals with the development of a reduced-order dynamic model of a Wankel rotary engine for the propulsion of a lightweight helicopter UAV, to be used for in-flight health-monitoring. The model, which is real-time executable by the on-board electronics of the UAV, includes the simulation of mechanical power generation, intake manifold pressure dynamics, fuel flow consumption, and thermal transfer in the combustion chamber. Aiming to develop a simulator based on physical principles, a correlation analysis among the experimental measurements obtained by endurance tests has been carried out, up to select the states and the inputs of the model sections and to identify their parameters via ordinary least square method. Simulation demonstrates to satisfactorily reproduce the experimental behaviour (maxima errors are lower than 5%), when the engine works near its nominal operating conditions, i.e. at constant rotor speed, while the model errors increase (up to 30%) during the transients from/to warm-up phases.
G. Di Rito· Materials Research Proceedin...· 0 citations
Comparative analysis reveals that no single technical route can fully address the coupled challenges of uncertainty, accuracy, and real-time performance, underscoring that hybrid frameworks are essential for balancing these competing requirements.
In order to improve the crashworthiness of UAVs, this paper improves and designs a wheeled UAV structure from a traditional quadrotor platform, focusing on its drop impact response characteristics. Aiming at the drop impacts that wheeled UAVs may face during flight and landing, this paper systematically investigates the structural response of UAVs under different drop conditions based on the display dynamics theory. By establishing a refined finite element model containing a tyre cushioning system and using ANSYS/LS-DYNA finite element simulation, the maximum equivalent force distribution law with or without wheels, at different drop heights and multi-angle attitudes, is analysed. The simulation results show that the presence of wheels significantly changes the drop impact stress transfer path and reduces the risk of damage to critical parts of the fuselage. This study provides a theoretical basis and engineering guidance for the impact resistance design of wheeled UAVs.
Huanye Huang, Hui Shi, Ning Xu et al.· SAE technical paper series· 0 citations
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