TY - JOUR
T1 - A metamaterials-augmented drone monitor for acoustics-based remote fault detection and diagnosis
AU - Lin, Yubin
AU - Huang, Shiqing
AU - Deng, Rongfeng
AU - Wang, Minglei
AU - Zou, Zhexiang
AU - Gu, Fengshou
AU - Ball, Andrew D
N1 - Publisher Copyright:
© 2025 Elsevier Ltd
PY - 2025/1/18
Y1 - 2025/1/18
N2 - Drone Acoustic Remote Sensing Technology (DARST) holds significant potential in the approach to promote condition monitoring performance and efficiency. However, due to high aerodynamic noises from the drone itself, DARST has to be equipped with large scale microphone arrays and embedded with complex algorithms in order to improve the signal-to-noise ratio (SNR). This complicated construction along with high costs, makes DARST systems hard to be deployed in industrial applications. To address this challenge, this paper proposes a compact acoustic metamaterial structure-based system to augment the drone acoustic sensing, which is shortened to Metamaterials-augmented Drone Monitor (MADM) approach for the sake of simplicity. The design and performance of the proposed MADM method were explored through numerical simulations and experiments, demonstrating superior acoustic signal enhancement, frequency-selective behaviour and directional sensitivity characteristics. Subsequently, a series of experiments with machinery fault signals under different SNRs were conducted, comprehensively assessing the fault detection capabilities of the proposed MADM in monitoring and diagnosing a machinery transmission system. The results show that the MADM method can efficiently suppress drone-induced noise and robustly extract diagnostic features from bearing and gear faults, even under strong noise influence at SNR as low as −20 dB, and the average SNR improvement surpasses 140 %. The simple structured system needs only a single-channel acoustic signal with commonly used FFT-based algorithms, which occupies merely 22 % of the drone’s total payload capacity, offering a new cost-efficient approach for DARST in online inspection and fault diagnosis of industrial machinery.
AB - Drone Acoustic Remote Sensing Technology (DARST) holds significant potential in the approach to promote condition monitoring performance and efficiency. However, due to high aerodynamic noises from the drone itself, DARST has to be equipped with large scale microphone arrays and embedded with complex algorithms in order to improve the signal-to-noise ratio (SNR). This complicated construction along with high costs, makes DARST systems hard to be deployed in industrial applications. To address this challenge, this paper proposes a compact acoustic metamaterial structure-based system to augment the drone acoustic sensing, which is shortened to Metamaterials-augmented Drone Monitor (MADM) approach for the sake of simplicity. The design and performance of the proposed MADM method were explored through numerical simulations and experiments, demonstrating superior acoustic signal enhancement, frequency-selective behaviour and directional sensitivity characteristics. Subsequently, a series of experiments with machinery fault signals under different SNRs were conducted, comprehensively assessing the fault detection capabilities of the proposed MADM in monitoring and diagnosing a machinery transmission system. The results show that the MADM method can efficiently suppress drone-induced noise and robustly extract diagnostic features from bearing and gear faults, even under strong noise influence at SNR as low as −20 dB, and the average SNR improvement surpasses 140 %. The simple structured system needs only a single-channel acoustic signal with commonly used FFT-based algorithms, which occupies merely 22 % of the drone’s total payload capacity, offering a new cost-efficient approach for DARST in online inspection and fault diagnosis of industrial machinery.
KW - Acoustic Sensing and Monitoring
KW - Drone Surveillance
KW - Metamaterial
KW - Signal Enhancement
KW - Bearing and Gear Fault Diagnosis
UR - http://www.scopus.com/inward/record.url?scp=85215240314&partnerID=8YFLogxK
U2 - 10.1016/j.ymssp.2025.112346
DO - 10.1016/j.ymssp.2025.112346
M3 - Article
VL - 226
JO - Mechanical Systems and Signal Processing
JF - Mechanical Systems and Signal Processing
SN - 0888-3270
M1 - 112346
ER -