Drone-based condition monitoring technology enables rapid and efficient structural assessment and decision-making, offering significant operational benefits across diverse industrial sectors. However, drone-based condition monitors (DCMs) are substantially affected by inherent drone vibrations and acoustic noise, which degrade the signal-to-noise ratio (SNR) and may lead to signal loss in monitoring data. Enhancing DCM performance through advanced vibration and acoustic control strategies is therefore essential for reliable deployment in practical industrial environments. Although vibration and noise control in DCMs, particularly through emerging metamaterial technologies, has attracted growing interdisciplinary interest, limited research has addressed the integration of metamaterials to enhance DCM performance. This doctoral research aims to fill this gap by developing novel metamaterial structures for drone-based vibration and acoustic control, with the goal of improving monitoring efficiency and diagnostic accuracy. The research begins by characterizing the acoustic and vibration signatures of drone operations and their impacts on DCM performance. It was found that operational vibrations are primarily induced by the rotational frequencies and harmonic components of propulsion motors, with low-frequency components posing particular challenges. Acoustic noise, mainly comprising blade passing frequencies, harmonics, and broadband components exceeding 95 dB, was shown to interfere with signal acquisition, establishing a foundation for targeted metamaterial design. To address passive low-frequency vibration attenuation, a locally resonant metamaterial (LRM) interface structure was proposed between the drone fuselage and the image-based DCM. Modeling and simulation confirmed the formation of low-frequency bandgaps and effective vibration suppression, which were experimentally validated. The LRM structure significantly reduced low-frequency vibration while maintaining a lightweight profile, improving the SNR of captured images. To overcome limitations of image-based DCMs in scenarios involving visual occlusion or internal structural defect monitoring, a contact-based DCM for vibration measurement was developed. This DCM integrated a negative-stiffness (NS) metastructure for enhanced energy absorption and stable physical contact, along with high-performance sensing nodes assisted by frequency-tunable (FT) metastructures. Numerical and experimental studies demonstrated superior fault detection performance at reduced sampling rates, validating its applicability in industrial environments. Further advancing remote diagnostic capabilities, an acoustic-based DCM was developed using highly anisotropic gradient acoustic metamaterials to manipulate drone-emitted acoustic fields. Numerical simulations and experimental evaluations showed that the proposed DCM provides frequency-selective and directional enhancement, effectively suppressing incoherent noise from drone propulsion and enabling high-SNR remote detection of target acoustic signatures using single-channel input and simple signal processing algorithm. Its potential for remote internal fault diagnosis was confirmed through simulation of representative fault scenarios. Finally, to address potential performance limitations of large-scale metamaterials on drone flight dynamics, a novel coupled metamaterial structure was introduced by embedding spatial folding channels within gradient metamaterials. This deep-subwavelength design yielded a compact acoustic-based DCM while preserving low-frequency enhancement and noise suppression capabilities. Modeling, numerical simulations, and experimental validations confirmed that the structure offers flexible frequency-selective amplification across low-frequency bands. The final prototype demonstrated significant noise suppression and signal enhancement, indicating strong potential for industrial deployment. Collectively, these research outcomes establish a transformative framework that enables high-efficiency and high-performance drone-based condition monitoring through novel metamaterial-enhanced strategies, thereby advancing drone capabilities toward reliable deployment in real-world industrial applications.
| Date of Award | 6 Jan 2026 |
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| Original language | English |
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| Sponsors | Beijing Institute of Technology |
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| Supervisor | Fengshou Gu (Main Supervisor) & Helen Miao (Co-Supervisor) |
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