An Acoustic Monitoring Method for Bridge Expansion Joints Based on Beamforming Microphone Array

Abstract

This invention discloses an acoustic monitoring method for bridge expansion joints based on a beamforming microphone array. The method synchronously acquires acoustic signals through multiple channels and achieves high-precision signal alignment using clock offset compensation. Combined with a pre-trigger signal from a vehicle passage detection unit, a noise analysis module is activated. The optimal microphone spacing is dynamically determined based on a sound field coherence and signal-to-noise ratio maximization model, and a stepper motor drives a movable microphone for real-time adjustment. Furthermore, an adaptive beamforming algorithm is used to generate spatial filtering weights, achieving directional enhancement and interference suppression of the sound source at the expansion joint. This invention, through the synergistic optimization of hardware structure and signal processing methods, effectively improves the sensitivity and accuracy of acoustic monitoring of bridge expansion joints, significantly enhances the ability to identify abnormal sound sources, reduces environmental noise interference, and is suitable for intelligent monitoring and diagnosis of bridge expansion joints in long-term service conditions.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring technology for bridge expansion joints, and specifically to an acoustic monitoring method for bridge expansion joints based on a beamforming microphone array. Background Technology

[0002] Expansion joints are crucial connecting devices between bridge spans or between bridge spans and abutments. Their function is to accommodate structural displacements caused by factors such as temperature changes, concrete shrinkage and creep, and loads. The health of expansion joints directly affects driving comfort and bridge structural safety. Loosening, breakage, or jamming of expansion joints not only generates significant impact noise but also causes irreversible damage to the main bridge structure. Therefore, long-term, continuous, and reliable condition monitoring of bridge expansion joints is of great engineering significance.

[0003] Currently, the monitoring technologies for bridge expansion joints are mainly divided into the following two categories: (1) Manual inspection and visual inspection: relying on technicians to conduct regular on-site inspections or using vehicle-mounted or drone-mounted cameras for image acquisition and recognition. This method is highly subjective, inefficient, and cannot achieve real-time monitoring, and it is difficult to detect hidden or minor structural damage; Contact sensor monitoring: including the installation of accelerometers, displacement gauges, strain gauges, etc. near the expansion joint. These methods can usually provide direct structural response data, but their installation process often requires invasive operations such as drilling into the bridge structure, which is complex and costly. The sensors themselves are easily damaged in harsh road environments, and their long-term reliability faces challenges; (2) Acoustic monitoring: this is an emerging non-contact, low-cost monitoring method. Its basic principle is that when a vehicle drives over the expansion joint, it generates a unique acoustic signal. The time-domain and frequency-domain characteristics of this signal are closely related to the health status of the expansion joint. By analyzing this sound signal, the condition of the expansion joint can be assessed and fault diagnosis can be achieved.

[0004] Existing acoustic monitoring methods still have significant limitations in practical applications: (1) The acoustic environment of bridges is complex, and monitoring signals are easily drowned out by strong traffic noise (such as engine roar, tire rolling noise), wind noise, rain noise, etc., resulting in a low signal-to-noise ratio (SNR) and difficulty in extracting effective features; (2) Existing microphone array structures are rigid. Some studies have attempted to use microphone arrays for sound source enhancement and localization, but the microphone spacing is usually fixed in advance and cannot be adjusted. Since the coherence of the sound field is closely related to the frequency and the microphone spacing, the fixed array geometry cannot adapt to the changing environmental noise field and different types of expansion joint sound sources, resulting in a decrease in the performance of beamforming algorithms in some frequency bands and limited ability to suppress noise and enhance target sound sources.

[0005] In summary, existing technologies lack a bridge expansion joint acoustic monitoring system capable of adapting to complex noise environments, dynamically optimizing array configuration to maximize signal-to-noise ratio, and possessing high-precision synchronous acquisition capabilities. This invention aims to overcome these shortcomings and provide an efficient, reliable, and non-contact automated monitoring solution. Summary of the Invention

[0006] Technical Problem: To address the shortcomings of existing technologies, this invention provides an acoustic monitoring method for bridge expansion joints based on a beamforming microphone array. Through mechanical structure optimization, adaptive array adjustment, high-precision signal processing, and closed-loop control, it achieves accurate acquisition and analysis of acoustic signals from expansion joints in complex environments, improves the anti-interference capability, positioning accuracy, and environmental adaptability of the monitoring system, and solves the problems of high noise interference, low positioning accuracy, and poor environmental adaptability in bridge expansion joint monitoring.

[0007] Technical solution: This invention is a method for acoustic monitoring of bridge expansion joints based on beamforming microphone arrays, the method comprising the following steps:

[0008] S1, at least one fixed microphone and at least two movable microphones are arranged near the bridge expansion joint;

[0009] S2 synchronously acquires the acoustic signals of each microphone through a multi-channel analog-to-digital conversion module, and uses a digital signal processing unit to measure and compensate for the clock offset between channels to ensure signal synchronization.

[0010] S3 receives the pre-trigger signal from the vehicle passing detection unit and starts the noise analysis module;

[0011] S4, based on sound field coherence calculation and signal-to-noise ratio maximization model, dynamically determines the optimal microphone spacing;

[0012] S5, the movable microphone is driven by a stepper motor and an encoder to achieve real-time adjustment of the microphone spacing;

[0013] S6 employs an adaptive beamforming algorithm to generate spatial filtering weights, thereby directionally enhancing and suppressing interference in the sound source of the expansion joint, thus enabling the monitoring and analysis of the acoustic signal of the expansion joint.

[0014] in,

[0015] In step S2, the digital signal processing unit adopts a clock offset estimation method based on cross-correlation algorithm, and the compensation accuracy reaches within 1% of the sampling period;

[0016] In step S4, the method for determining the optimal microphone spacing includes the following steps:

[0017] S41, Sound field modeling and data acquisition: Control the microphone to move along the guide rail and acquire the sound pressure time domain signal at the encoder positioning point; Separate the pure noise period of the sound source without expansion joints and the mixed sound period of the sound source with expansion joints by judging the sound energy threshold.

[0018] S42, Dynamic Noise Template Library Construction: This module stores noise spectrum templates for different weather conditions (wind, rain, and clear skies) to quickly output pure noise estimates by matching the current environment. The template library is dynamically updated via an online noise spectrum learning module. The noise analysis module uses Welch's Method to perform spectral analysis on environmental noise signals, outputting noise spectrum characteristics in the 50Hz~2kHz frequency band. Simultaneously, it calls upon the dynamic noise template library, which stores typical noise spectra for wind, rain, and clear skies, to quickly match the current environment and output pure noise estimates. The template library is updated monthly via the online noise spectrum learning module.

[0019] S43, Frequency Domain Coherence Calculation: Define the target sound source of the expansion joint at a frequency... ,spacing coherence function under Environmental noise at frequency ,spacing coherence function under The frequency band weighting function focuses on the characteristic frequency band of the expansion joint. ;

[0020] S44, based on frequency domain coherence calculation and signal-to-noise ratio (SNR) maximization modeling, has the following specific formula:

[0021] Frequency domain coherence calculation: ;

[0022] ;

[0023] In the formula, It is spatial location. At frequency and location The sound pressure or signal of the target sound source at the expansion joint. At frequency and location The sound pressure or signal of the target sound source at the expansion joint. At frequency and location The sound pressure signal of the ambient noise at the location, At frequency and location The sound pressure signal of the ambient noise at the location, and They represent and The complex conjugate, Expectation operator;

[0024] ;

[0025] Indicates the optimal spacing. Take the maximum value within the characteristic frequency band of the expansion joint. The weight attenuation in the frequency band dominated by environmental noise is reduced to The weight allocation strategy Adjust dynamically based on environmental sensor data.

[0026] In step S5, the stepper motor adopts a high-precision positioning control method and is driven by pulse width modulation waveform to achieve micro-step subdivision control, ensuring the accuracy and stability of microphone spacing adjustment.

[0027] In step S6, a minimum variance distortion-free response MVDR adaptive beamformer is used: a fixed-point beamforming method is employed. The decomposition algorithm, which is an improved Gram-Schmidt algorithm, solves for the inverse of the covariance matrix. The specific steps are as follows:

[0028] S61, Receive microphone signal matrix , Represents the field of complex numbers. The number of sampling points. Number of microphones;

[0029] S62, for conduct break down: ,in It is a unitary matrix. It is an upper triangular matrix;

[0030] S63, the formula for generating the steering vector based on the linear sound source model of the expansion joint is:

[0031] ;

[0032] in The sound waves travel from the sound source to the... The absolute propagation delay of each microphone, For signal frequency, , , These are the sound source to the... The distance between the microphones, of which, ; For the speed of sound, ;

[0033] S64, Solve for beam weights: Based on beam weights Satisfying the optimization problem: (Constraints) The solution is: ;

[0034] In the formula, Let be the covariance matrix of the input signal, where , It is an upper triangular matrix. for The conjugate transpose of .

[0035] The bridge expansion joint acoustic monitoring device based on beamforming microphone array for use in the method of the present invention includes:

[0036] (1) Fixed microphone and movable microphone: used to collect acoustic signals of bridge expansion joints;

[0037] (2) Vehicle passing detection unit: used to output a pre-trigger signal when a vehicle approaches the expansion joint;

[0038] (3) Multi-channel analog-to-digital conversion module: used for synchronous acquisition of multi-channel acoustic signals;

[0039] (4) Digital signal processing unit: used for clock offset measurement and compensation, sound field coherence calculation and signal-to-noise ratio maximization analysis;

[0040] (5) Stepper motor and encoder: used to drive the movable microphone and realize real-time adjustment of the microphone spacing;

[0041] (6) Beamforming processing module: used to execute adaptive beamforming algorithm and generate spatial filtering weights to achieve directional enhancement and interference suppression of sound source of expansion joint.

[0042] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0043] By analyzing the environmental noise spectrum characteristics in real time and calculating the optimal microphone spacing based on the signal-to-noise ratio maximization model, the spatial filtering performance of the array is always optimally configured for the current noise environment.

[0044] By combining a high-precision MVDR adaptive beamforming algorithm, the main beam can be formed with extreme accuracy in the direction of the expansion joint, while a null is formed in the direction of the noise wave, thereby suppressing strong environmental interference (such as traffic flow noise and wind and rain noise) to the greatest extent.

[0045] The system can autonomously distinguish noise characteristics under different weather conditions (sunny, rainy, windy) and adjust the optimal spacing calculation model and beamforming weight allocation strategy accordingly. This makes the present invention not a rigid system with fixed parameters, but an intelligent solution that can be adapted to local conditions, ensuring long-term stability and monitoring effectiveness in various complex environments. Attached Figure Description

[0046] Figure 1 A flowchart illustrating the technical process of an acoustic monitoring method for bridge expansion joints based on beamforming microphone arrays;

[0047] Figure 2 Flowchart for optimizing microphone array spacing;

[0048] Figure 3 Flowchart for microphone array beamforming;

[0049] Figure 4 This is a layout diagram of an acoustic monitoring device for bridge expansion joints based on a beamforming microphone array.

[0050] Figure 5 This is a schematic diagram of the working operation of a bridge expansion joint acoustic monitoring device based on a beamforming microphone array. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] The device used in the acoustic monitoring method for bridge expansion joints based on beamforming microphone array of the present invention includes:

[0053] (1) Fixed microphone and movable microphone: used to collect acoustic signals of bridge expansion joints;

[0054] (2) Vehicle passing detection unit: used to output a pre-trigger signal when a vehicle approaches the expansion joint;

[0055] (3) Multi-channel analog-to-digital conversion module: used for synchronous acquisition of multi-channel acoustic signals;

[0056] (4) Digital signal processing unit: used for clock offset measurement and compensation, sound field coherence calculation and signal-to-noise ratio maximization analysis;

[0057] (5) Stepper motor and encoder: used to drive the movable microphone and realize real-time adjustment of the microphone spacing;

[0058] (6) Beamforming processing module: used to execute adaptive beamforming algorithm and generate spatial filtering weights to achieve directional enhancement and interference suppression of sound source of expansion joint.

[0059] See Figure 4This is a typical arrangement of the bridge expansion joint acoustic monitoring device based on a beamforming microphone array according to the present invention. The arrangement is as follows:

[0060] The microphone guide rail extends approximately perpendicular to the length of the bridge expansion joint and is fixedly installed on the roadside or maintenance access of the bridge. Microphones are mounted on this guide rail and precisely driven by stepper motors to move along it, adjusting the microphone spacing in real time. Vehicle detection units (such as lidar or loop detectors) are positioned upstream of the road at a certain distance from the expansion joint to generate pre-trigger signals as vehicles approach.

[0061] The signal processing unit, as the control core of the system, is electrically connected to the stepper motor on the guide rail, the microphone array, and the vehicle detection unit. (See also...) Figure 5 Its workflow is as follows: receiving a pre-trigger signal from the vehicle passage detection unit and starting the noise analysis module; dynamically calculating the optimal microphone spacing under the current environment based on the real-time acquired acoustic signals and built-in algorithms; generating control commands to drive the stepper motor to adjust the microphone position; then executing an adaptive beamforming algorithm to enhance and suppress the sound source signal of the expansion joint, and finally outputting the monitoring results.

[0062] This invention proposes an acoustic monitoring method for bridge expansion joints based on a beamforming microphone array. Taking the monitoring of a D80 type expansion joint (design displacement of ±80mm) as an example, the main technical process of the implementation scheme is as follows (see...). Figure 1 ):

[0063] S1, see also Figure 4 The device is installed by placing a fixed microphone and at least two movable microphones near the bridge expansion joint. The microphones are mounted on a guide rail and are precisely driven by stepper motors to move along the rail, adjusting the microphone spacing in real time. Vehicle detection units (such as lidar or loop detectors) are deployed upstream of the road at a certain distance from the expansion joint to generate a pre-trigger signal when a vehicle approaches.

[0064] Specifically, all components are mounted on a linear guide rail with a total length of 2.5 meters. The guide rail is made of corrosion-resistant aluminum alloy and extends perpendicularly to the length of the D80 expansion joint. It is installed on the outer side of the bridge shoulder or crash barrier, approximately 5 meters from the centerline of the expansion joint. The vehicle passage detection unit uses a laser rangefinder sensor, installed 10 meters in front of the expansion joint, to output a pre-trigger signal when a vehicle approaches.

[0065] S2 synchronously acquires the acoustic signals of each microphone through a multi-channel analog-to-digital conversion module, and uses a digital signal processing unit to measure and compensate for the clock offset between channels to ensure signal synchronization.

[0066] Specifically, the multi-channel analog-to-digital conversion module uses a 24-bit ADC (Analog-to-Digital Converter) with a sampling frequency of 44.1 kHz to synchronously acquire signals from each microphone. The digital signal processing unit uses an FPGA (Field-Programmable Gate Array) and a clock offset estimation method based on cross-correlation algorithm, achieving a compensation accuracy of less than 1% of the sampling period.

[0067] S3 receives the pre-trigger signal from the vehicle passing detection unit and starts the noise analysis module.

[0068] Specifically, the system is triggered when a vehicle passes through the LiDAR detection area. The noise analysis module immediately starts and calls the dynamic noise template library. For example, if the current weather is light rain, the system will match the "rainy day" noise template (characterized by significant energy in the 200-500Hz frequency band).

[0069] S4, based on the spectral characteristics of environmental noise, identifies pure noise periods and mixed sound periods (including periods containing sound sources from expansion joints and environmental noise) through a noise analysis module. Combining sound field coherence calculation and a signal-to-noise ratio maximization model, it dynamically determines the optimal microphone spacing.

[0070] See Figure 2 Specifically, the method for determining the optimal microphone spacing includes the following steps:

[0071] S41, Sound Field Modeling and Data Acquisition: Control the microphone to move along the guide rail, at the encoder positioning point ( Acquire sound pressure time-domain signals; separate pure noise periods (sound sources without expansion joints) and mixed sound periods (sound sources including expansion joints) by judging sound energy threshold.

[0072] S42, Dynamic Noise Template Library Construction: Stores noise spectrum templates for different weather conditions (wind, rain, sunny), quickly outputs pure noise estimates by matching the current environment; the template library is dynamically updated through an online noise spectrum learning module; the noise analysis module uses Welch's Method to perform spectrum analysis on environmental noise signals, outputting noise spectrum characteristics in the 50Hz~2kHz frequency band, and simultaneously calls the "Dynamic Noise Template Library" (stores typical noise spectra for wind, rain, and sunny weather), quickly matches the current environment and outputs pure noise estimates (the template library is updated monthly through the "Online Noise Spectrum Learning Module");

[0073] S43, Frequency Domain Coherence Calculation: Define the target sound source of the expansion joint at a frequency... ,spacing coherence function under Environmental noise at frequency ,spacing coherence function under The frequency band weighting function focuses on the characteristic frequency band of the expansion joint. ;

[0074] S44, based on frequency domain coherence calculation and SNR (signal-to-noise ratio) maximization modeling, has the following specific formula:

[0075] Frequency domain coherence calculation: ;

[0076] ;

[0077] In the formula, It is spatial location. At frequency and location The sound pressure or signal of the target sound source (expansion joint) at the location. At frequency and location The sound pressure or signal of the target sound source (expansion joint) at the location. At frequency and location The sound pressure signal of the ambient noise at the location, At frequency and location The sound pressure signal of the ambient noise at the location, and They represent and The complex conjugate, It is the expectation operator.

[0078] ;

[0079] Indicates the optimal spacing. Take the maximum value within the characteristic frequency band of the expansion joint. The weight attenuation in the frequency band dominated by environmental noise is reduced to The weight allocation strategy Adjust dynamically based on environmental sensor data.

[0080] Specifically, focusing on the characteristic frequency band (800-1600 Hz) of the D80 expansion joint, a frequency band weighting function is set. The value is 1 within this range, and it decreases to 0.2 in other frequency bands (especially the 200-500 Hz range where rain noise is significant).

[0081] S5, the movable microphone is driven by a stepper motor and an encoder to achieve real-time adjustment of the microphone spacing;

[0082] Specifically, the stepper motor adopts a high-precision positioning control method and is driven by pulse width modulation waveform to achieve micro-step subdivision control, ensuring the accuracy and stability of microphone spacing adjustment.

[0083] S6 employs an adaptive beamforming algorithm to generate spatial filtering weights, thereby directionally enhancing and suppressing interference in the sound source of the expansion joint, thus enabling the monitoring and analysis of the acoustic signal of the expansion joint.

[0084] See Figure 3 Specifically, an MVDR adaptive beamformer is used: a fixed-point beamforming method is employed. The decomposition algorithm (improved Gram-Schmidt algorithm) solves for the inverse of the covariance matrix. The specific steps are as follows:

[0085] S61, Receive microphone signal matrix , Represents the field of complex numbers. The number of sampling points. Number of microphones;

[0086] S62, for conduct break down: ,in It is a unitary matrix. It is an upper triangular matrix;

[0087] S63, the formula for generating the steering vector based on the linear sound source model of the expansion joint is:

[0088] ;

[0089] in The sound waves travel from the sound source to the... The absolute propagation delay of each microphone, For signal frequency, , , These represent the distances from the sound source to the 0th, 1st, and 2nd microphones, respectively. ( ; (The speed of sound is taken as 340 m / s).

[0090] S64, Solve for beam weights: Based on beam weights Satisfying the optimization problem: (Constraints) The solution is: ;

[0091] In the formula, Let be the covariance matrix of the input signal, where , It is an upper triangular matrix. for The conjugate transpose of .

[0092] After calculating the spatial filtering weights, a main beam is formed in the direction of the expansion joint to enhance the signal, while nulls are formed in the directions of traffic noise and wind and rain noise. After processing, the target signal gain is increased by about 12 dB, and the main interference noise is suppressed by more than 15 dB, significantly improving the signal-to-noise ratio and facilitating subsequent condition diagnosis and anomaly identification.

[0093] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A method for acoustic monitoring of bridge expansion joints based on beamforming microphone arrays, characterized in that, The method specifically includes the following steps: S1, at least one fixed microphone and at least two movable microphones are arranged near the bridge expansion joint; S2 synchronously acquires the acoustic signals of each microphone through a multi-channel analog-to-digital conversion module, and uses a digital signal processing unit to measure and compensate for the clock offset between channels to ensure signal synchronization. S3 receives the pre-trigger signal from the vehicle passing detection unit and starts the noise analysis module; S4, based on the sound field coherence calculation and signal-to-noise ratio maximization model, dynamically determines the optimal microphone spacing, including the following steps: S41, Sound field modeling and data acquisition: Control the microphone to move along the guide rail and acquire the sound pressure time domain signal at the encoder positioning point; Separate the pure noise period of the sound source without expansion joints and the mixed sound period of the sound source with expansion joints by judging the sound energy threshold. S42, Dynamic Noise Template Library Construction: This module stores noise spectrum templates for different weather conditions (wind, rain, and clear skies) to quickly output pure noise estimates by matching the current environment. The template library is dynamically updated via an online noise spectrum learning module. The noise analysis module uses Welch's Method to perform spectral analysis on environmental noise signals, outputting noise spectrum characteristics in the 50Hz~2kHz frequency band. Simultaneously, it calls upon the dynamic noise template library, which stores typical noise spectra for wind, rain, and clear skies, to quickly match the current environment and output pure noise estimates. The template library is updated monthly via the online noise spectrum learning module. S43, Frequency Domain Coherence Calculation: Define the target sound source of the expansion joint at a frequency... ,spacing coherence function under Environmental noise at frequency ,spacing coherence function under The frequency band weighting function focuses on the characteristic frequency band of the expansion joint. ; S44, based on frequency domain coherence calculation and signal-to-noise ratio (SNR) maximization modeling, has the following specific formula: Frequency domain coherence calculation: ; ； In the formula, It is spatial location. At frequency and location The sound pressure or signal of the target sound source at the expansion joint. At frequency and location The sound pressure or signal of the target sound source at the expansion joint. At frequency and location The sound pressure signal of the ambient noise at the location, At frequency and location The sound pressure signal of the ambient noise at the location, and They represent and The complex conjugate, It is the expectation operator; ； Indicates the optimal spacing. Take the maximum value within the characteristic frequency band of the expansion joint. The weight attenuation in the frequency band dominated by environmental noise is reduced to The weight allocation strategy Adjust dynamically based on environmental sensor data; S5, the movable microphone is driven by a stepper motor and an encoder to achieve real-time adjustment of the microphone spacing; S6 employs an adaptive beamforming algorithm to generate spatial filtering weights, thereby directionally enhancing and suppressing interference in the sound source of the expansion joint, thus enabling the monitoring and analysis of the acoustic signal of the expansion joint.

2. The method for acoustic monitoring of bridge expansion joints based on beamforming microphone array according to claim 1, characterized in that, In step S2, the digital signal processing unit adopts a clock offset estimation method based on cross-correlation algorithm, and the compensation accuracy reaches within 1% of the sampling period.

3. The method for acoustic monitoring of bridge expansion joints based on beamforming microphone array according to claim 1, characterized in that, In step S5, the stepper motor adopts a high-precision positioning control method and is driven by pulse width modulation waveform to achieve micro-step subdivision control, ensuring the accuracy and stability of microphone spacing adjustment.

4. The method for acoustic monitoring of bridge expansion joints based on beamforming microphone array according to claim 1, characterized in that, In step S6, a minimum variance distortion-free response MVDR adaptive beamformer is used: a fixed-point beamforming method is employed. The solution algorithm is an improved Gram-Schmidt algorithm for finding the inverse of the covariance matrix. The specific steps are as follows: S61, Receive microphone signal matrix , Represents the field of complex numbers. The number of sampling points. Number of microphones; S62, for conduct break down: ,in It is a unitary matrix. It is an upper triangular matrix; S63, the formula for generating the steering vector based on the linear sound source model of the expansion joint is: ， in The sound waves travel from the sound source to the... The absolute propagation delay of each microphone, For signal frequency, , , These are the sound source to the... The distance between the microphones, of which, ; For the speed of sound, ; S64, Solve for beam weights: Based on beam weights Satisfying the optimization problem: Constraints The solution is: ; In the formula, Let be the covariance matrix of the input signal, where , It is an upper triangular matrix. for The conjugate transpose of .

5. A bridge expansion joint acoustic monitoring device based on a beamforming microphone array for use in the method of any one of claims 1 to 4, characterized in that, include: (1) Fixed microphone and movable microphone: used to collect acoustic signals of bridge expansion joints; (2) Vehicle passing detection unit: used to output a pre-trigger signal when a vehicle approaches the expansion joint; (3) Multi-channel analog-to-digital conversion module: used for synchronous acquisition of multi-channel acoustic signals; (4) Digital signal processing unit: used for clock offset measurement and compensation, sound field coherence calculation and signal-to-noise ratio maximization analysis; (5) Stepper motor and encoder: used to drive the movable microphone and realize real-time adjustment of the microphone spacing; (6) Beamforming processing module: used to execute adaptive beamforming algorithm and generate spatial filtering weights to achieve directional enhancement and interference suppression of sound source of expansion joint.

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