Acoustic Monitor and Fault Detection Method for Unmanned Aerial Vehicles Based on Metamaterial Reinforcement

Abstract

This invention discloses a metamaterial-reinforced acoustic monitor for unmanned aerial vehicles (UAVs) and a fault detection method thereon. The UAV acoustic monitor comprises an acoustic sensing system and a horn-shaped acoustic metamaterial structure mounted on the load area beneath the UAV. The acoustic metamaterial structure includes several circular plates with linearly increasing diameters from bottom to top and a lever passing through the center of all the circular plates. The acoustic sensing system includes a data acquisition card and several acoustic sensors. One acoustic sensor is installed in the gap between adjacent circular plates, and all acoustic sensors are electrically connected to the data acquisition card. This invention applies to the technical field of UAV remote sensing.

Description

Technical Field

[0001] This invention is applied to the field of UAV remote sensing technology, and particularly relates to a UAV acoustic monitor and fault detection method based on metamaterial reinforcement. Background Technology

[0002] Unmanned aerial vehicle (UAV) remote sensing technology has become a transformative approach, enhancing traditional condition monitoring and inspection methods with unprecedented speed, efficiency, and versatility across diverse terrains. UAV remote sensing is increasingly being applied in areas such as infrastructure health monitoring, railway and traffic surveillance, forest and fire management, photovoltaic energy system inspection, and wind turbine evaluation. Current applications primarily rely on optical sensing modalities, such as those using high-resolution cameras, thermal imaging, multispectral sensors, and laser scanning. However, these methods face limitations when dealing with visual obstacles or internal defects in the monitored object. In such cases, acoustic-based UAV remote sensing offers a promising alternative, for example, in detecting internal mechanical faults in wind turbine drive systems.

[0003] While UAV acoustic remote sensing technology holds great potential, its widespread adoption in industrial environments is significantly limited. The main problem lies in the strong background noise generated by the UAV's propulsion system during flight, characterized primarily by the tonal and broadband components of the propeller blade passing frequencies. This complex noise environment is even more pronounced in industrial UAVs, which typically have four or more motors and propeller units, making it difficult for onboard acoustic sensors to isolate the target sound source from the noise generated by the UAV itself. This often results in a signal-to-noise ratio (SNR) of less than -15 dB, rendering traditional sound analysis algorithms completely ineffective and severely hindering the practical application of UAV acoustic remote sensing technology.

[0004] To overcome these limitations, numerous technical solutions currently focus on optimizing signal processing for multi-microphone arrays using advanced beamforming techniques. Specifically, this method leverages the spatial distribution and phase relationships between microphone array elements to enhance source estimation and reduces irrelevant noise through phase difference correction, thereby improving the signal-to-noise ratio. While substantial progress has been made in developing complex airborne microphone array systems and refining related signal processing algorithms over the past decade, some shortcomings remain.

[0005] According to beamforming theory, increasing the number of microphones and array aperture can significantly improve sensing performance. Therefore, expanding the number of microphones or using an enlarged array frame to maximize phase difference is beneficial for improving the signal-to-noise ratio (SNR). However, these techniques introduce new limitations, particularly the increased computational demands of real-time multi-channel audio processing, reduced UAV payload capacity, and aerodynamic constraints. Corresponding alternative strategies include expanding heterogeneous microphone array configurations or using special connection mechanisms to keep the microphone system relatively far from the UAV noise source, thereby directly improving the SNR of the target source signal. While these techniques offer some enhancement, they often alter the UAV's center of gravity, requiring complex flight control compensation and potentially increasing flight operation risks due to center of gravity instability. Furthermore, machine learning-assisted microphone array signal calculation methods also promise to enhance the SNR, but these methods also face limitations such as model generalization and further constraints on onboard computing resources. Moreover, most current UAV acoustic remote sensing technologies are primarily limited to sound source localization applications, unable to acquire high SNR target source signals, and thus unable to provide effective solutions for practical industrial monitoring, fault inspection, and other application scenarios. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a metamaterial-reinforced UAV acoustic monitor and fault detection method. The metamaterial-reinforced UAV acoustic monitor uses an artificially designed acoustic metamaterial structure to adjust the sound field around the UAV. By adopting a highly anisotropic acoustic metamaterial structure, frequency selectivity enhancement of the target source signal is obtained. At the same time, the strong directional sensitivity of the acoustic metamaterial structure is used to suppress incoherent noise, especially severe noise interference from the UAV. High signal-to-noise ratio sound signal acquisition can be achieved in UAV acoustic sensing, and efficient fault detection can be achieved using only a single-channel signal and a simple processing algorithm.

[0007] The technical solution adopted in this invention is as follows: This invention includes a drone acoustic monitor and fault detection method based on metamaterial reinforcement. The drone acoustic monitor based on metamaterial reinforcement includes an acoustic sensing system and a horn-shaped acoustic metamaterial structure installed in the load area below the drone. The acoustic metamaterial structure includes several circular plates with a linearly increasing diameter from bottom to top and a lever passing through the center of all the circular plates. The acoustic sensing system includes a data acquisition card and several acoustic sensors. One acoustic sensor is installed in the gap between adjacent circular plates, and all the acoustic sensors are electrically connected to the data acquisition card.

[0008] Furthermore, the circular plate is made of polylactic acid material through 3D printing.

[0009] Furthermore, the spacing between adjacent circular plates is equal.

[0010] Furthermore, the data acquisition card is electrically connected to the integrated control board of the UAV, and the integrated control board is electrically connected to the interface of the UAV's onboard computer system.

[0011] Furthermore, the number of circular plates is 18, the thickness of the circular plates is 3mm, the spacing between adjacent circular plates is 10mm, the radius of the largest circular plate is 110mm, and the radius of the smallest circular plate is 10mm.

[0012] A fault detection method for the aforementioned metamaterial-reinforced UAV acoustic monitor includes the following steps:

[0013] S1. Set the inspection path of the UAV and set a number of detection points on the inspection path to cover the objects to be detected. When the UAV flies to the set detection point, it hovers and collects the audio at the current detection point through the UAV acoustic monitor based on metamaterial reinforcement to judge the state of the object to be detected.

[0014] S2. At the detection point of the current detection object, the drone is set to hover within a 30-degree cone range directly above the detection object to collect audio data, so as to make full use of the strengthening performance of metamaterials, and the acoustic signals on the detection object are collected simultaneously by several acoustic sensors of the drone acoustic monitor based on metamaterial strengthening to form the audio to be analyzed.

[0015] S3. Extract audio point data from the audio to form audio data sets corresponding to different enhancement frequency bands of the metamaterial;

[0016] S4. Select the audio data of the required frequency band based on the physical characteristics of the object being detected for time-frequency pre-analysis;

[0017] S5. Further filter and envelope analysis are performed on the current audio data set. After fixed-band filtering using a bandpass filter, Hilbert transform is performed to obtain the envelope spectrum. The fault characteristic frequencies are identified through the envelope spectrum to determine the fault state of the detected object.

[0018] The beneficial effects of this invention are: 1. Unlike traditional microphone (i.e. acoustic sensor) array technology, the metamaterial-reinforced UAV acoustic monitor achieves both compact design and acoustic enhancement. This innovative design structure effectively reduces the severe interference of noise caused by the UAV, eliminates the need to increase the number and spacing of microphones to improve the signal-to-noise ratio, and optimizes the center of gravity of the UAV, thereby enhancing the overall flight stability and payload.

[0019] 2. It has special frequency-selective enhancement characteristics, which can effectively extract subtle mechanical fault features even under high noise conditions with a signal-to-noise ratio as low as -20dB; in addition, its strong directional enhancement characteristics reduce the interference of unrelated signals, providing a solid foundation for fault identification and localization in the UAV inspection process.

[0020] 3. This method can effectively detect mechanical faults using single-channel acoustic data without relying on complex signal processing algorithms for noise reduction or optimal frequency band search. This greatly reduces the hardware requirements and computational load of the airborne computer, providing a feasible solution for online monitoring and fault diagnosis in UAV industrial inspection. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the structure of an acoustic monitor for unmanned aerial vehicles (UAVs).

[0022] Figure 2 (a) is a schematic diagram of a two-dimensional effective medium for metamaterials. Figure 2 (b) is a schematic diagram of the sound pressure distribution at different incident frequencies in a metamaterial medium;

[0023] Figure 3 (a) is a schematic diagram of the pressure field distribution obtained by numerical simulation with incident frequencies of 1350 Hz, 1490 Hz, and 1710 Hz. Figure 3 (b) shows the normalized pressure-frequency response in the 2nd, 4th and 6th gaps, illustrating the broadband acoustic enhancement schematic.

[0024] Figure 4 (a) is a schematic diagram of the measurement setup for the directional enhancement properties through numerical simulation and experiments. Figure 4 (b) Figure 4 (c) and Figure 4 (d) is a schematic diagram showing the normalized directional response at the 2nd, 4th and 6th gaps, respectively;

[0025] Figure 5 (a) is a diagram of the experimental setup for evaluating the invention using a synthetic signal. Figure 5 (b) is a schematic diagram of the acoustic measurement location of the UAV;

[0026] Figure 6 (a) is a schematic diagram showing the comparison of time-domain waveforms obtained in free space and in the sixth gap of the metamaterial of this invention. Figure 6 (b) is the spectrum of the signal obtained inside the metamaterial. Figure 6 (c) A comparison plot of the envelope spectra corresponding to the two sets of signals is provided;

[0027] Figure 7This is the envelope spectrum of gear fault signals acquired by a metamaterial-reinforced UAV acoustic monitor at different distances and incident angles; among which... Figure 7 (a) At a vertical distance of 4 meters directly above the sound source. Figure 7 (b) A perpendicular distance of 4 meters from the sound source with an incident angle of 26.6°. Figure 7 (c) At a vertical distance of 9 meters directly above the sound source. Figure 7 (d) A perpendicular distance of 4 meters from the sound source with an incident angle of 45°. Figure 7 (e) At a vertical distance of 15 meters directly above the sound source. Figure 7 (f) 4 meters vertical distance from the sound source with an incident angle of 56.3°. Detailed Implementation

[0028] In this embodiment, the present invention includes a metamaterial-reinforced acoustic monitor for unmanned aerial vehicles and a fault detection method. For example... Figure 1 As shown, the metamaterial-reinforced UAV acoustic monitor includes an acoustic sensing system and a horn-shaped acoustic metamaterial structure installed in the load area below the UAV 1. The acoustic metamaterial structure includes 18 circular plates 2 with linearly increasing diameters from bottom to top and coaxially arranged, and a lever 3 passing through the center of all the circular plates 2. The lever 3 is a solid lever. The acoustic sensing system includes a data acquisition card 4 and several acoustic sensors 5. The gaps between adjacent circular plates 2 are all integrated with the same microelectromechanical system acoustic sensor 5. All the acoustic sensors 5 are electrically connected to the data acquisition card 4. The acquisition card 4 is electrically connected to the integrated control board of the UAV 1, and the integrated control board is electrically connected to the onboard computer system interface of the UAV 1. The combined weight of the acoustic metamaterial structure and the acoustic sensing system is approximately 0.77 kg, accounting for only 22% of the effective payload capacity of the UAV. Furthermore, since the acoustic performance of the acoustic metamaterial structure is mainly affected by its geometric structure and has limited influence from the base material, using a lower density rigid material can further reduce the weight of the system and ensure that the system performance remains unchanged. Preferably, the circular plate 2 is made of polylactic acid material through 3D printing.

[0029] In this embodiment, the spacing between adjacent circular plates 2 is 10mm, the thickness of the circular plate 2 is 3mm, the radius of the largest circular plate 2 is 110mm, and the radius of the smallest circular plate 2 is 10mm.

[0030] In this embodiment, to briefly illustrate the function of the acoustic metamaterial, such as Figure 2 (a) illustrates the conceptualization of the proposed acoustic metamaterial structure into a two-dimensional representation, where sound waves propagate along the positive x-axis. Using an effective medium model, the array of circular plates dispersing air can be characterized as a continuous, lossless medium. Therefore, the effective mass densities along the x-axis and y-axis are ρx and ρy, respectively.x and ρ y The bulk modulus K of the acoustic metamaterial structure can be expressed as:

[0031] ρ x =F r ρ plate +(1-F r )ρ air

[0032]

[0033] Where F r =t / (g+t) is the filling ratio of circular plate 2, and the material property of circular plate 2 is defined as density ρ plate =1250kg / m 3 The bulk modulus K of circular plate 2 plate = 3.8 GPa. The density of the surrounding air medium is ρ. air =1.2kg / m 3 The bulk modulus K of the surrounding air air =1.4*10 -5 GPa. This metamaterial model assumes that the entire structure can be decomposed into a series of infinitesimally small cross-sections composed of uniform metamaterial plates, and the effective refractive index change of the metamaterial can be further derived as:

[0034]

[0035] In the above formula, ω=2πf represents the angular frequency of the incident sound wave, where n air The refractive index of incident air is usually approximated by n. air =1. y(x) represents the width of the metamaterial plate, which is assumed to be a continuous function along the propagation axis. The gradual increase in refractive index will lead to wavelength compression, further resulting in spatial concentration of acoustic energy. Using the change in effective refractive index, the relationship between the acoustic pressure amplitude inside the metamaterial and the incident frequency along the x-axis can be expressed as:

[0036]

[0037] Based on the above formula for sound pressure amplitude and incident frequency, Figure 2 (b) shows the sound pressure amplitude distribution at different locations within the metamaterial medium across the incident wave frequency range, illustrating the ability of this acoustic metamaterial structure to selectively enhance sound pressure amplitude at different frequencies in its spatial domain. This behavior allows for precise frequency-based acoustic manipulation and sensing, which is crucial for the metamaterial-reinforced UAV acoustic monitoring method proposed in this invention.

[0038] Based on the above principle, the propagation of sound waves within the designed structure results in compression and amplification of different frequencies within the different circular plate intervals of the metamaterial. To further elucidate the amplification characteristics of the proposed acoustic sensing system, a three-dimensional finite element model was established using the commercial finite element software COMSOL Multiphysics v6.0, and numerical simulation calculations were performed using the pressure acoustics module. Figure 3 (a) As shown on the left, the model uses plane wave radiation boundary conditions to simulate incident sound waves, while applying perfectly matched layers on all external boundaries to mitigate reflection interference. Figure 3 (a) shows the pressure field distribution within the metamaterial structure at frequencies of 1350 Hz, 1490 Hz, and 1710 Hz on the right. This result indicates that the pressure amplitude within the metamaterial interstices is significantly increased compared to a free field, and the spatial concentration of acoustic energy is related to the incident frequency. To quantitatively characterize the frequency-selective enhancement properties, Figure 3 (b) The normalized pressure-frequency response within the 2nd, 4th, and 6th gaps (where the gap refers to the distance between adjacent circular plates 2) is further presented, showing enhanced peaks at frequencies of 1357 Hz, 1492 Hz, and 1708 Hz, respectively. These three representative gap locations demonstrate that the pressure enhancement caused by acoustic compression manifests as broadband acoustic enhancement, which is particularly advantageous in practical engineering applications.

[0039] Furthermore, due to the high anisotropy of the acoustic metamaterial structure, it is sensitive to the incident angle of sound waves. To further elucidate the directional sensitivity and directional enhancement characteristics of the UAV acoustic monitor, the following were conducted: Figure 4 (a) shows the directional response measurement. Based on the above numerical simulation settings, the incident wave angle is kept constant, while the acoustic metamaterial structure is rotated in 1-degree increments, and the change in sound pressure amplitude within the gap is extracted. Figure 4 (b), (c), and (d) show the normalized directional response results for the 2nd, 4th, and 6th gaps, respectively, with the incident wave frequency corresponding to the peak frequency of each gap. Figure 4 As shown in (b), (c), and (d), the sound pressure amplitude is maximum when the sound wave is incident along the positive direction of the acoustic metamaterial tip, and decreases rapidly with increasing incident angle. When the incident angle reaches 45° or 315°, the pressure amplitude drops to about 1 / 5 of its maximum value. Further increases in the incident angle lead to a continuous decrease in pressure amplitude, reaching a minimum when the sound wave is incident perpendicularly to the metamaterial disc 2 (i.e., 90° or 270°). This is because for perpendicular sound wave incident, the equivalent medium of the metamaterial can be approximated as air. Furthermore, due to the severe impedance mismatch at the metamaterial-air interface, the pressure amplitude of sound waves incident in the reverse direction (>90° and <270°) is significantly lower than that of sound waves incident in the positive direction.

[0040] Figure 4(a) Measurement of directional enhancement properties by numerical simulation and experiment: Schematic diagram of directional response measurement setup. In the simulation, the metamaterial disc 2 rotates in increments of 1 degree; in the experiment, the UAV acoustic monitor rotates in increments of 5 degrees from a fixed sound source.

[0041] This pronounced directional sensitivity and enhanced properties provide favorable conditions for the application of UAV acoustic remote sensing technology. First, according to the metamaterial-reinforced UAV acoustic monitor proposed in this invention, when the UAV collects signals above the monitored target, the target sound source signal radiates from the positive direction into the metamaterial system and is selectively enhanced. Simultaneously, noise generated by the UAV itself or other irrelevant noise radiates into the metamaterial system mainly from the sides or rear, with significantly reduced gain or even suppression compared to the target source signal. This differential enhancement plays a crucial role in improving the signal-to-noise ratio of the target source signal. Second, the strong directional sensitivity of the metamaterial also ensures that the target source signal only achieves maximum gain when perpendicular to the UAV system. This directional selectivity effectively prevents the enhancement of other irrelevant noise during UAV monitoring, reduces the risk of false detection, and provides important evidence for accurately locating fault sources.

[0042] Leveraging the unique wave modulation properties of this acoustic metamaterial to enhance frequency selectivity in signal acquisition at different locations within the structure, and further suppressing the interference of UAV noise on signal acquisition, this innovative fault detection method for UAV acoustic monitors based on metamaterial reinforcement improves the signal-to-noise ratio of the target source signal. It can effectively extract useful information from complex background noise without requiring complex signal processing algorithms, such as for detecting internal mechanical fault signals and extracting fault features. In contrast, traditional signal measurement methods are severely affected by noise generated by UAVs and environmental interference, making it very difficult to extract relevant fault signal features. The following two examples briefly illustrate its beneficial effects.

[0043] To evaluate the effectiveness and fault detection capability of a metamaterial-reinforced UAV acoustic monitor, this invention employs a mechanical fault synthesis signal with a precisely quantifiable signal-to-noise ratio. For example... Figure 5 As shown in (a), a synthesized signal is emitted using a high-fidelity loudspeaker to simulate a fault source. A prototype of the metamaterial-reinforced UAV acoustic monitor was deployed at an altitude of approximately 4 meters, flying and hovering, collecting sound signals directly above the sound source at a sampling frequency of 48 kHz. An integrated control board interfaces directly with the UAV's onboard computer system, while a ground station accesses the onboard computer and controls the acquisition of acoustic signals via a wireless network. Furthermore, this invention tests the changes in fault detection performance of the metamaterial-reinforced UAV acoustic monitor at different distances and angles to demonstrate its potential for fault localization through directional enhancement characteristics. Figure 5 As shown in (b), six strategically selected detection positions were established. Positions 1-3 represent the UAV hovering directly above the sound source, with vertical distances of 4 meters, 9 meters, and 15 meters, respectively. Positions 4, 5, and 6 maintain the same vertical distance of 4 meters from the sound source, while the horizontal distances are 2 meters, 4 meters, and 6 meters, respectively, corresponding to incident angles of 26.6 degrees, 45 degrees, and 56.3 degrees.

[0044] Figure 5 (a) Experimental setup for evaluating the invention using synthetic signals. This device demonstrates a practical application of fault detection in an outdoor environment. (b) Schematic diagram of UAV acoustic measurement locations. Six UAV locations (numbered 1-6) are shown at different altitudes and horizontal distances from the sound source.

[0045] In the first embodiment, the formula for the bearing fault signal can be expressed as:

[0046]

[0047] The above formula can be used to generate a simulated rolling bearing fault signal, where f r1 =8Hz and f n =1700Hz represents the bearing's rotational frequency and natural frequency, respectively. Where A=4 and ζ=100 are the amplitude coefficient and damping coefficient, respectively. t1=mod(t,0.99 / f) d1 ), where f d1 =22.5Hz is the characteristic frequency of bearing failure, and Gaussian white noise is used simultaneously, with n(t) controlling the actual signal-to-noise ratio. During the experimental phase, the metamaterial-reinforced UAV acoustic monitor initially acquired a 0dB bearing failure signal at location 1 for evaluation, with a sampling time of 5 seconds. This is because the reinforcement band of the 6th gap in the metamaterial is related to the bearing's natural frequency f. n The closest proximity is advantageous for amplifying the carrier signal, further enhancing the detectability of fault characteristic frequencies. Therefore, the acoustic signal corresponding to gap 6 is used for in-depth analysis. For comparison, an identical acoustic sensor is placed in free space outside the metamaterial to collect reference signals for performance comparison.

[0048] Figure 6 Comparative analysis of bearing acoustic signals with a signal-to-noise ratio of 0 dB, where the left side is the signal acquired in free space and the right side is the signal acquired in a metamaterial-reinforced UAV acoustic monitor: (a) time-domain waveform, (b) spectrum and (c) envelope spectrum.

[0049] Figure 6(a) A comparison of time-domain waveforms obtained in free space and within the metamaterial gap 6 of this invention is presented. The waveform acquired inside the metamaterial not only shows a significant increase in sound pressure amplitude but also exhibits obvious periodic impact characteristics, with the impact frequency precisely corresponding to the bearing's fault characteristic frequency. The signal spectrum measured in free space, such as... Figure 6 (b) As shown on the left, two closely spaced peaks are displayed near 140Hz, identified as the blade passage frequency of the UAV propeller. Detailed spectral analysis reveals that the harmonic distribution of these frequency components is across the entire spectrum, illustrating the widespread impact of the UAV's self-generated noise on the target signal. The presence of the two distinct frequency peaks is primarily attributed to the UAV's balancing behavior in response to wind interference and other instabilities. This behavior is characterized by differential motor acceleration generating compensating lift, further complicating the sound field and its interference with signal acquisition. The high signal-to-noise ratio of the current sound source ensures that the bearing's inherent frequency and its sideband components remain observable. The spectrum corresponding to the signal obtained within the metamaterial, as shown... Figure 6 As shown in (b), due to low-frequency acoustic diffraction, the blade passing frequency and its associated harmonics of the UAV can still be observed in the spectrum, but their amplitude is reduced to some extent compared to natural space. In addition, the signal component centered at the bearing's natural frequency and its sideband components exhibit significant amplification.

[0050] According to conventional bearing fault diagnosis methods, the acquired signals are further processed, including targeted filtering and demodulation, to obtain the corresponding envelope spectrum. Fault diagnosis is then performed based on a detailed analysis of the fault frequency components in the envelope spectrum. Considering the severe interference from low-frequency UAV sensing elements and the optimized enhancement frequency band provided by metamaterials, a uniform filtering frequency band of 1000-3000Hz was selected. Therefore, Figure 6 (c) A comparison of the envelope spectra corresponding to the two sets of signals is provided. It is evident that the bearing fault frequency and its associated harmonics in the signal obtained within the metamaterial are amplified by more than 60 times compared to the signal obtained in free space. Furthermore, higher harmonic components are clearly observed in the signal within the metamaterial, providing rich fault diagnosis information. It is noteworthy that although high-pass filtering is used to remove the low-frequency UAV blade passage frequency, the envelope spectrum of the free-space signal is still significantly affected by the higher harmonics of the blade passage frequency. In stark contrast, the blade passage frequency component in the signal within the metamaterial is effectively suppressed, and no identifiable components are found in the envelope spectrum, demonstrating the superior performance of this invention.

[0051] In the second embodiment, the formula for the gear fault signal can be expressed as:

[0052]

[0053] To further verify the robustness of the present invention, a gear fault simulation signal can be generated using the above formula, where f r2 =12.9Hz and f m =750Hz represents the rotational frequency and gear meshing frequency, respectively. N = (0, 1, 2, 3, 4, 5) are the harmonics of amplitude modulation and frequency modulation, while M = (1, 2, 3) represents the harmonics of the meshing frequency. A m = [0.8, 0.6, 0.4] represents the m-th harmonic amplitude of the meshing frequency. n is the harmonic order of amplitude modulation and frequency modulation, and B n = [0.8, 0.6, 0.4, 0.2, 0] and C n = [0, 0, 0, 0, 0] represents the nth harmonic amplitude of frequency modulation and amplitude modulation, respectively. Similarly, Gaussian white noise n(t) controls the actual signal-to-noise ratio. Furthermore, it is important to emphasize the meshing frequency f. m The modulation sideband corresponds to the rotation frequency f r2 The interval distribution, this frequency is used as the characteristic frequency of gear failure, i.e., f d2 =12.9Hz.

[0054] As described in the experimental setup above, during the experimental phase, this invention utilized gear fault signals to analyze the changes in fault detection performance under different distances and angles. Gaussian noise was used to adjust the signal-to-noise ratio to -10dB. Generally, although the amplitudes of higher harmonics of the meshing frequency are low, they are less susceptible to noise contamination and may reflect fault characteristics. Therefore, the enhanced signal at the metamaterial gap 4 was selected for analysis, as it is close to the second harmonic of the meshing frequency (2f). m =1500Hz).

[0055] The signal envelope spectra of the metamaterial-reinforced drone acoustic monitor obtained at six selected locations are as follows: Figure 7 As shown. Among them, Figure 7 (a), (c), and (e) show the measurement results at vertical distances of 4 meters, 9 meters, and 15 meters directly above the sound source, respectively. It is evident that distance and sound pressure amplitude are inversely proportional. However, the gear fault characteristic frequency obtained by this invention is still significantly higher than the noise level, even at greater distances. Furthermore, Figure 7 (b), (d), and (f) show the results corresponding to gradually increasing incident angles relative to the sound source. When the incident angle increases to approximately 26 degrees, a significant attenuation of the sound pressure amplitude of about 5 times is observed, despite the vertical distance from the sound source being only 4 meters, although the characteristic frequency of the gear fault remains clearly visible. As the incident angle further increases to 45 degrees and above, the sound pressure amplitude stabilizes at a lower level, but the characteristic frequency of the gear fault becomes difficult to distinguish from the background noise.

[0056] Directional sensitivity analysis of a metamaterial-reinforced UAV acoustic monitor reveals operational guidelines for fault detection applications. Optimal fault detection performance is achieved when the detected object remains within a 30-degree cone relative to the UAV during acoustic signal acquisition. Since industrial UAVs typically possess centimeter-level precision real-time kinematic (RTK) positioning systems, operators can precisely control the monitoring direction. This operational constraint enhances the detection of potential fault characteristics while minimizing false identifications from external signal sources, demonstrating the significant potential of this invention for precise fault location in industrial applications.

[0057] Finally, given the remarkable performance of the method of this invention in mechanical fault detection, it shows great application potential in other fault detection or signal enhancement scenarios. These potential areas include, but are not limited to, pipeline leak detection, structural crack identification, and marine exploration.

[0058] More specifically, a fault detection method applied to the aforementioned metamaterial-reinforced UAV acoustic monitor includes the following steps:

[0059] S1. Set the inspection path of UAV 1, and set a number of detection points on the inspection path to cover the objects to be detected. When UAV 1 flies to the set detection point, it hovers and collects the audio at the current detection point through the UAV acoustic monitor based on metamaterial reinforcement to judge the state of the object to be detected.

[0060] S2. At the detection point of the current detection object, the UAV 1 is set to hover within a 30-degree cone range directly above the detection object to collect audio data, so as to make full use of the strengthening performance of metamaterials. Several acoustic sensors 5 of the UAV acoustic monitor based on metamaterial strengthening are used to collect the sound signals on the detection object to form the audio to be analyzed. For example, at the current detection point, the duration of each audio can be 5-30 seconds, depending on the frequency resolution required.

[0061] S3. Extract audio point data from the audio to form audio data groups corresponding to different enhancement frequency bands of the metamaterial; taking one audio in the metamaterial gap as an example, starting from 0 seconds, collect audio point data from the audio at certain time intervals, and arrange all audio point data from low frequency to high frequency according to the enhancement frequency band corresponding to the metamaterial gap to form audio data groups;

[0062] S4. Select the audio data of the required frequency band for time-frequency pre-analysis based on the physical characteristics of the object being tested; for example, in this embodiment 1, the enhancement frequency band of the 6th gap of the metamaterial is closest to the natural frequency of the bearing of the object being tested, and has the effect of enhancing weak fault signals. Therefore, the audio data group corresponding to the 6th gap is selected for time-frequency analysis. By identifying and judging the fault signal impact characteristics in the time domain signal and the sideband characteristics in the frequency domain signal, the signal-to-noise ratio and effectiveness of the signal are preliminarily judged.

[0063] S5. Further filtering and envelope analysis are performed on the current audio data set. A fixed-band filtering is performed using a bandpass filter, followed by a Hilbert transform to obtain the envelope spectrum. The envelope spectrum is then used to identify fault characteristic frequencies to determine the fault state of the detected object. For example, in this embodiment, bandpass filtering is performed on the audio data of the 6th gap in the metamaterial. The filtering band is the fixed enhancement band corresponding to the metamaterial, with a frequency range of 1000-3000Hz. The filtered signal is then subjected to a Hilbert transform to obtain the envelope signal. A fast Fourier transform is then performed on the envelope signal to directly obtain the envelope spectrum. By identifying the bearing fault characteristic frequencies and the amplitudes of their higher harmonics in the envelope spectrum, the presence of a bearing fault and the severity of the fault can be effectively determined.

[0064] Although the embodiments of the present invention are described with reference to actual solutions, they do not constitute a limitation on the meaning of the present invention. Modifications to the embodiments and combinations with other solutions based on this specification will be obvious to those skilled in the art.

Claims

1. A drone acoustic monitor based on metamaterial reinforcement, characterized by: It comprises an acoustic sensing system and a horn-shaped acoustic metamaterial structure installed in the load area below the unmanned aerial vehicle (1), the acoustic sensing system comprises an acquisition card (4) and a plurality of acoustic sensors (5), one acoustic sensor (5) is installed in the gap between adjacent circular plates (2), and all the acoustic sensors (5) are electrically connected with the acquisition card (4). The number of the circular plates (2) is 18, the plate thickness of the circular plates (2) is 3mm, the spacing between adjacent circular plates (2) is 10mm, the maximum radius of the circular plates (2) is 110mm, and the minimum radius of the circular plates (2) is 10mm.

2. The metamaterial-based stiffened unmanned acoustic monitor of claim 1, wherein: The circular plates (2) are made of polylactic acid material by 3D printing.

3. The metamaterial-based stiffened unmanned acoustic monitor of claim 1, wherein: The acquisition card (4) is electrically connected with the integrated control board of the unmanned aerial vehicle (1), and the integrated control board is electrically connected with the on-board computer system of the unmanned aerial vehicle (1).

4. A method for failure detection applied to the unmanned aerial vehicle acoustic monitor based on metamaterial reinforcement according to any one of claims 1-3, characterized in that, The method comprises the following steps: S1. Set the inspection path of the unmanned aerial vehicle (1), and set a plurality of detection points on the inspection path to cover the required detection object, when the unmanned aerial vehicle (1) flies to the set detection point, hover and collect the audio on the current detection point by the unmanned aerial vehicle acoustic monitor based on metamaterial enhancement to judge the state of the detection object; S2. At the detection point of the current detection object, set the unmanned aerial vehicle (1) to hover in the conical range of 30 degrees above the detection object to collect audio data, so as to make full use of the enhancement performance of the metamaterial, and collect the sound signals on the detection object by a plurality of acoustic sensors (5) of the unmanned aerial vehicle acoustic monitor based on metamaterial enhancement to form the audio to be analyzed; S3. Extract the audio point data on the audio to form the audio data group corresponding to different enhancement frequency bands of the metamaterial; S4. Select the audio data of the required frequency band according to the physical characteristics of the detection object for time-frequency pre-analysis; S5. Further filter and envelope analyze the current audio data group, use a band-pass filter to perform fixed frequency band filtering, then perform Hilbert transformation to obtain envelope spectrum, identify the fault characteristic frequency through the envelope spectrum, and determine the fault state of the detection object.

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