CN113108893B - Sound field reconstruction system and method based on sound pressure and particle vibration velocity - Google Patents
Sound field reconstruction system and method based on sound pressure and particle vibration velocity Download PDFInfo
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Abstract
The invention discloses a sound field reconstruction system and method based on sound pressure and particle vibration velocity, wherein the system comprises: the multi-sensor scanning module is used for scanning by using an array formed by the acoustic vector sensors to obtain the sound pressure and particle vibration velocity data of the measuring surface; the multi-channel real-time data acquisition module is used for carrying out multi-channel real-time acquisition on the sound pressure and particle vibration velocity data based on labview; the filtering module is used for filtering data points with amplitude values smaller than a preset value on a measuring surface by using the amplitude filtering coefficient of the sound field measuring surface; and the sound field reconstruction module is used for performing sound field reconstruction by using a near-field acoustic holography method based on an equivalent source method, wherein the sound field reconstruction comprises sound pressure sound field reconstruction, particle vibration velocity sound field reconstruction and sound field reconstruction of a sound intensity field. According to the method, the particle vibration velocity is introduced to reconstruct the sound field on the basis of the original sound pressure-based equivalent source method near-field acoustic holography, so that the precision of sound field reconstruction and the positioning precision of a sound source are effectively improved.
Description
Technical Field
The invention relates to the technical field of sound field reconstruction, in particular to a sound field reconstruction system and method based on sound pressure and particle vibration velocity.
Background
The existing noise monitoring is usually carried out by means of measuring instruments such as a sound level meter, a frequency analyzer and the like, an obtained result only contains one aspect of sound information, and a large amount of noise information cannot be effectively obtained. The sound field distribution condition in a certain space can be obtained through the acoustic imaging technology, the noise source can be accurately and visually positioned, and the running conditions of corresponding equipment and systems can be monitored.
Existing acoustic imaging techniques are mainly implemented by near field acoustic holography (NAH). The NAH technology requires that the distance between the holographic measuring surface and the sound source surface is a fraction of the wavelength, data are collected through a near field, acoustic physical quantities such as sound pressure, vibration velocity, sound intensity and sound power in the sound source and the sound field are reconstructed through a space sound field transformation algorithm, and the sound field can be predicted by comparing the position far away from the holographic surface. Compared with the traditional acoustic holography technology, the NAH technology not only records propagation wave components, but also records evanescent wave components, so that the NAH can reconstruct a sound field on the premise of not being limited by Rayleigh resolution criterion. The reconstruction of various acoustic quantities of a sound source and any point of the whole sound field can be realized only by measuring the complex sound pressure or the vibration velocity of the holographic surface by the NAH, and the NAH technology adopts non-contact measurement, so that the method is suitable for environments which cannot directly measure vibration such as high temperature, high heat and the like. The NAH technology is a powerful tool for sound source identification and sound field visualization, has wide application prospects in mechanical fault diagnosis and mechanical equipment noise measurement and control engineering, and is a powerful tool for engineering technicians to solve the problems of environment and product noise. By virtue of the advantages, the NAH is valued by numerous acoustic researchers and organizations at home and abroad since its birth, and through the development of decades, the NAH has made important progress in the aspects of theory, reconstruction algorithm and application.
Currently, the mainstream near-field acoustic holography carries out sound field reconstruction and prediction by measuring sound pressure, and as is well known, particle vibration velocity is also worth paying attention as one of acoustic quantities. Especially, with the improvement of the method and the requirement of practical engineering, the sound field reconstruction method based on the sound pressure alone cannot meet the requirement of people on the reconstruction accuracy.
Disclosure of Invention
The invention aims to provide a sound field reconstruction system and method based on sound pressure and particle vibration velocity, which introduces particle vibration velocity quantity to reconstruct a sound field on the basis of the original sound pressure-based equivalent source method near-field acoustic holography, constructs a set of sound field reconstruction system including data acquisition, storage and processing, greatly improves the precision of sound field reconstruction and the positioning precision of a sound source, and proves the beneficial effect of the particle vibration velocity on the aspect of sound field reconstruction.
To solve the above technical problem, an embodiment of the present invention provides the following solutions:
in one aspect, a sound field reconstruction system based on sound pressure and particle vibration velocity is provided, including:
the multi-sensor scanning module is used for scanning by using an array formed by the acoustic vector sensors to obtain the sound pressure and particle vibration velocity data of the measuring surface;
the multichannel real-time data acquisition module is used for carrying out multichannel real-time acquisition on the sound pressure and particle vibration velocity data based on Iabview;
the filtering module is used for filtering data points with amplitude values smaller than a preset value on a measuring surface by using the amplitude filtering coefficient of the sound field measuring surface;
and the sound field reconstruction module is used for performing sound field reconstruction by using a near-field acoustic holography method based on an equivalent source method, wherein the sound field reconstruction comprises sound pressure sound field reconstruction, particle vibration velocity sound field reconstruction and sound field reconstruction of a sound intensity field.
Preferably, the multi-sensor scanning module comprises a plurality of acoustic vector sensors, a sensor support, a reference microphone, an XY linear module and a scanning stepping controller;
the acoustic vector sensors are used for simultaneously measuring sound pressure and particle vibration velocity data, the sensor support is used for accommodating and fixing a plurality of acoustic vector sensors, the sensor support is connected with the XY linear module, the scanning stepper controller controls the sensor support to move up and down for scanning, and the scanning stepper controller controls the sensor array to move back and forth to adjust the distance between the sensor array and equipment.
Preferably, the amplitude filter coefficient C p And C u Is defined as:
wherein p is max And u max Respectively the maximum sound pressure and particle vibration speed on the measuring surface, p cutoff And u cutoff Cutting off sound pressure and particle vibration velocity during filtration, wherein C p And C u Take 15 dB.
Preferably, the sound field reconstruction module is specifically configured to:
aiming at different characteristics of sound pressure and particle vibration velocity, a Tikhonov regularization method is used for solving the problem of uncertainty generated in the matrix inversion process, a Bayesian method is used for solving regularization parameters in sound field reconstruction of the sound pressure, broadband sound holography based on sound pressure differential filtering is used for solving sound field reconstruction of the particle vibration velocity, and a sound intensity field is obtained by calculating the reconstructed sound pressure and the particle vibration velocity.
Preferably, the Bayesian method obtains the optimal regularization parameter by minimizing the following function:
wherein σ k Is the k-th singular value, y, of the transfer function matrix G k Is the kth element of the vector y, y ═ U H P and U are left singular matrixes obtained by SVD decomposition of the transfer function matrix, and P is sound pressure on the measuring surface;
let theta be lambda 2 The derivative of J (theta) with respect to theta is obtained:
combining the above formulas, and solving for J (lambda) by gradient descent method 2 ) Finally, the regularization parameter lambda is obtained.
Preferably, the sound field reconstruction module is further specifically configured to:
reconstructing sound pressure P near an equivalent source surface using Bayesian regularization 1 And P 2 ，P 1 And P 2 The sound pressure on two planes separated by delta is the vibration velocity V of mass points near an equivalent source according to a wave equation 1 From P 1 And P 2 The difference approximation of (d) yields:
wherein, omega is wave number, and P is air density; when Δ is sufficiently small, V 1 Equivalent to the real particle vibration speed; then, for V 1 Normalization processing is performed, and the normalized values are used as a filter gamma of particle vibration velocity equivalent source intensity:
on the basis, iterative solution is carried out on the particle vibration velocity equivalent source intensity by utilizing broadband acoustic holography, and the equivalent source intensity q is equivalent by utilizing an equivalent source intensity filter gamma for each iteration preset step number k And (3) carrying out filtering treatment:
q k+1 ＝q k ·γ
and finally, realizing the reconstruction of the particle vibration velocity by using the particle vibration velocity equivalent source strength q meeting the convergence condition.
On one hand, the sound field reconstruction method based on the sound pressure and the particle vibration velocity is provided, and comprises the following steps:
scanning by using an array formed by acoustic vector sensors to obtain the sound pressure and particle vibration velocity data of a measuring surface;
multi-channel real-time acquisition is carried out on sound pressure and particle vibration velocity data based on labview;
filtering data points with amplitude values smaller than a preset value on a measuring surface by using an amplitude value filtering coefficient of the sound field measuring surface;
and performing sound field reconstruction by using a near-field acoustic holography method based on an equivalent source method, wherein the sound field reconstruction comprises sound pressure sound field reconstruction, particle vibration velocity sound field reconstruction and sound field reconstruction of a sound intensity field.
Preferably, the amplitude filtering coefficient C p And C u Is defined as:
wherein p is max And u max Respectively the maximum sound pressure and particle vibration speed on the measuring surface, p cutoff And u cutoff Cutting off sound pressure and particle vibration velocity during filtration, wherein C p And C u Take 15 dB.
Preferably, aiming at different characteristics of sound pressure and particle vibration velocity, a Tikhonov regularization method is used for solving the problem of uncertainty generated in the matrix inversion process, a Bayesian method is used for solving regularization parameters in sound field reconstruction of the sound pressure, broadband sound holography based on sound pressure differential filtering is used for solving sound field reconstruction of the particle vibration velocity, and a sound intensity field is obtained by calculation of the reconstructed sound pressure and the particle vibration velocity;
the Bayesian method obtains optimal regularization parameters by minimizing the following functions:
wherein σ k Is the k-th singular value, y, of the transfer function matrix G k Is the kth element of the vector y, y ═ U H P and U are left singular matrixes obtained by SVD decomposition of the transfer function matrix, and P is sound pressure on the measuring surface;
let theta be lambda 2 The derivative of J (θ two) with respect to θ is obtained:
combining the above formulas, and solving for J (lambda) by gradient descent method 2 ) Finally, the regularization parameter lambda is obtained.
Preferably, the sound field reconstruction step comprises:
reconstructing sound pressure P near an equivalent source surface using Bayesian regularization 1 And P 2 ，P 1 And P 2 The sound pressure on two planes separated by delta is the vibration velocity V of mass points near an equivalent source according to a wave equation 1 From P 1 And P 2 The difference approximation of (d) yields:
wherein, omega is wave number, rho is air density; when Δ is sufficiently small, V 1 Equivalent to the real particle vibration speed; then, for V 1 Normalization processing is performed, and the normalized values are used as a filter gamma of particle vibration velocity equivalent source intensity:
on the basis, iterative solution is carried out on the particle vibration velocity equivalent source intensity by utilizing broadband acoustic holography, and the equivalent source intensity q is equivalent by utilizing an equivalent source intensity filter gamma for each iteration preset step number k And (3) carrying out filtering treatment:
q k+1 ＝q k ·γ
and finally, realizing the reconstruction of the particle vibration velocity by using the particle vibration velocity equivalent source strength q meeting the convergence condition.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
the invention provides a sound field reconstruction system and a sound field reconstruction method for near-field acoustic imaging, which are used for obtaining complete measurement surface data by scanning with fewer sensors; the amplitude filtering coefficient of the measuring surface is provided, and adverse effects caused by measuring errors and sound source instability are eliminated; the particle vibration velocity is added into the calculation of sound field reconstruction, so that the reconstruction precision is improved; the near-field acoustic holography based on the equivalent source method is improved and applied to the calculation of the mass point vibration velocity, the problem of uncertainty in the matrix inversion process is solved by using a standard Tikhonov regularization method, parameters are solved by using a Bayes method in the regularization process of the sound pressure quantity, the equivalent source strength of the mass point vibration velocity quantity is solved by using broadband acoustic holography based on sound pressure differential filtering, and the positioning accuracy of a sound source is improved; finally, the sound pressure quantity and the mass point vibration velocity quantity are combined to obtain a sound intensity field, so that the reconstruction effect is better.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a sound field reconstruction system based on sound pressure and particle vibration velocity according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a multi-sensor scanning module provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram of a calculation process of a sound field reconstruction module according to an embodiment of the present invention;
FIG. 4 is a flowchart of a sound field reconstruction method based on sound pressure and particle vibration velocity according to an embodiment of the present invention;
fig. 5a to 5f are schematic diagrams of measurement results and reconstruction results of sound pressure, particle vibration velocity and sound intensity of a dual sound source of an anechoic chamber according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
An embodiment of the present invention first provides a sound field reconstruction system based on sound pressure and particle velocity, as shown in fig. 1, the system includes:
the multi-sensor scanning module is used for scanning by using an array formed by the acoustic vector sensors to obtain the sound pressure and particle vibration velocity data of the measuring surface;
the multi-channel real-time data acquisition module is used for carrying out multi-channel real-time acquisition on the sound pressure and particle vibration velocity data based on labview;
the filtering module is used for filtering data points with amplitude values smaller than a preset value on a measuring surface by using the amplitude filtering coefficient of the sound field measuring surface;
and the sound field reconstruction module is used for performing sound field reconstruction by using a near-field acoustic holography method based on an equivalent source method, wherein the sound field reconstruction comprises sound pressure sound field reconstruction, particle vibration velocity sound field reconstruction and sound field reconstruction of a sound intensity field.
According to the method, the particle vibration velocity is introduced to carry out sound field reconstruction on the basis of the original near-field acoustic holography based on the equivalent source method of sound pressure, a set of sound field reconstruction system including data acquisition, storage and processing is built, the precision of sound field reconstruction and the positioning precision of a sound source are greatly improved, and the beneficial effect of the particle vibration velocity on the aspect of sound field reconstruction is verified.
Furthermore, the multi-sensor scanning module comprises a plurality of acoustic vector sensors, a sensor bracket, a reference microphone, an XY linear module and a scanning stepping controller;
the acoustic vector sensors are used for simultaneously measuring sound pressure and particle vibration velocity data, the sensor support is used for accommodating and fixing the acoustic vector sensors, the sensor support is connected with the XY linear module, the scanning stepper controller controls the sensor support to move up and down for scanning, and the scanning stepper controller controls the sensor array to move back and forth to adjust the distance between the sensor array and equipment.
FIG. 2 presents a schematic view of a multi-sensor scanning module in accordance with an embodiment of the present invention. Preferably, the transducer holder is about 1.7m long and can accommodate up to 29 acoustic vector transducers (not shown); the sensor support is connected with the XY linear module, and the scanning precision can reach 0.1 mm.
Further, the amplitude filtering coefficient C p And C u Is defined as:
wherein p is max And u max Respectively the maximum sound pressure and particle vibration speed on the measuring surface, p cutoff And u cutoff Cutting off sound pressure and particle vibration velocity during filtration, wherein C p And C u Take 15 dB.
Further, the sound field reconstruction module is specifically configured to:
aiming at different characteristics of sound pressure and particle vibration velocity, a Tikhonov regularization method is used for solving the problem of uncertainty generated in the matrix inversion process, a Bayesian method is used for solving regularization parameters in sound field reconstruction of the sound pressure, broadband sound holography based on sound pressure differential filtering is used for solving sound field reconstruction of the particle vibration velocity, and a sound intensity field is obtained by calculating the reconstructed sound pressure and the particle vibration velocity. The specific calculation process of the sound field reconstruction module is shown in fig. 3.
The Bayesian method obtains an optimal regularization parameter by minimizing the following function:
wherein σ k Is the k-th singular value, y, of the transfer function matrix G k Is the kth element of the vector y, y ═ U H P and U are left singular matrixes obtained by SVD decomposition of the transfer function matrix, and P is sound pressure on the measuring surface;
let theta be lambda 2 The derivative of J (theta) with respect to theta is obtained:
the gradient descent method can be used to solve J (. lamda.) in the formula (3) in the combination formula (4) 2 ) Finally, the regularization parameter lambda is obtained.
Further, the sound pressure P near the equivalent source surface is reconstructed by Bayes regularization 1 And P 2 ，P 1 And P 2 The sound pressure on two planes separated by delta is the vibration velocity V of mass points near an equivalent source according to a wave equation 1 From P 1 And P 2 The difference approximation of (d) yields:
wherein, omega is wave number, rho is air density; when Δ is sufficiently small, V 1 Equivalent to the real particle vibration speed; then, for V 1 Normalization processing is performed, and the normalized values are used as a filter gamma of particle vibration velocity equivalent source intensity:
on the basis, iterative solution is carried out on the particle vibration velocity equivalent source intensity by utilizing broadband acoustic holography, and the equivalent source intensity q is equivalent by utilizing an equivalent source intensity filter gamma for each iteration preset step number k And (3) carrying out filtering treatment:
q k+1 ＝q k ·γ (7)
and finally, realizing the reconstruction of the particle vibration velocity by using the particle vibration velocity equivalent source strength q meeting the convergence condition.
Accordingly, an embodiment of the present invention further provides a sound field reconstruction method based on sound pressure and particle velocity, as shown in fig. 4, the method includes the following steps:
scanning by using an array formed by acoustic vector sensors to obtain the sound pressure and particle vibration velocity data of a measuring surface;
multi-channel real-time acquisition is carried out on sound pressure and particle vibration velocity data based on labview;
filtering data points with amplitude values smaller than a preset value on a measuring surface by using an amplitude value filtering coefficient of the sound field measuring surface;
and performing sound field reconstruction by using a near-field acoustic holography method based on an equivalent source method, wherein the sound field reconstruction comprises sound pressure sound field reconstruction, particle vibration velocity sound field reconstruction and sound field reconstruction of a sound intensity field.
Further, the amplitude filtering coefficient C p And C u Is defined as:
wherein p is max And u max Respectively the maximum sound pressure and particle vibration speed on the measuring surface, p cutoff And u cutoff Cutting off sound pressure and particle vibration velocity during filtration, wherein C p And C u Take 15 dB.
Further, aiming at different characteristics of sound pressure and particle vibration velocity, a Tikhonov regularization method is used for solving the problem of uncertainty generated in the matrix inversion process, a Bayes method is adopted for regularization parameter calculation in sound field reconstruction of the sound pressure, broadband sound holography based on sound pressure differential filtering is used for solving in sound field reconstruction of the particle vibration velocity, and a sound intensity field is obtained by calculation of the reconstructed sound pressure and the particle vibration velocity;
the Bayesian method obtains the optimal regularization parameter by minimizing the following function:
wherein σ k Is the k-th singular value, y, of the transfer function matrix G k Is the kth element of the vector y, y ═ U H P and U are left singular matrixes obtained by SVD decomposition of the transfer function matrix, and P is sound pressure on the measuring surface;
let θ equal λ 2 The derivative of J (theta) with respect to theta is obtained:
the gradient descent method can be used to solve J (. lamda.) in the formula (3) in the combination formula (4) 2 ) Finally, the regularization parameter lambda is obtained.
Further, the sound field reconstruction step includes:
reconstructing sound pressure P near an equivalent source surface using Bayesian regularization 1 And P 2 ，P 1 And P 2 The sound pressure on two planes separated by delta is the vibration velocity V of mass points near an equivalent source according to a wave equation 1 From P 1 And P 2 The difference approximation of (d) yields:
wherein, omega is wave number, rho is air density; when Δ is sufficiently small, V 1 Equivalent to the real particle vibration speed; then, for V 1 Normalization processing is performed, and the normalized values are used as a filter gamma of particle vibration velocity equivalent source intensity:
on the basis, iterative solution is carried out on the particle vibration velocity equivalent source intensity by utilizing broadband acoustic holography, and the equivalent source intensity q is equivalent by utilizing an equivalent source intensity filter gamma for each iteration preset step number k And (3) carrying out filtering treatment:
q k+1 ＝q k ·γ (7)
and finally, realizing the reconstruction of the particle vibration velocity by using the particle vibration velocity equivalent source strength q meeting the convergence condition.
Taking a sound-deadening chamber dual-sound source as an example, sound field reconstruction is performed by using the method of the invention, and fig. 5 a-5 f are schematic diagrams of measurement results and reconstruction results of sound pressure, particle vibration velocity and sound intensity of the sound-deadening chamber dual-sound source. The method can effectively improve the accuracy of sound field reconstruction and the positioning accuracy of the sound source, combines the sound pressure quantity and the particle vibration velocity quantity to obtain the sound intensity field, and has better reconstruction effect.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (2)
1. A sound field reconstruction system based on sound pressure and particle vibration velocity, comprising:
the multi-sensor scanning module is used for scanning by using an array formed by the acoustic vector sensors to obtain the sound pressure and particle vibration velocity data of the measuring surface;
the multi-sensor scanning module comprises a plurality of acoustic vector sensors, a sensor bracket, a reference microphone, an XY linear module and a scanning stepping controller;
the acoustic vector sensors are used for simultaneously measuring sound pressure and particle vibration velocity data, the sensor support is used for accommodating and fixing a plurality of acoustic vector sensors, the sensor support is connected with the XY linear module, the scanning stepping controller controls the sensor support to move up and down for scanning, and the scanning stepping controller controls the sensor array to move back and forth to adjust the distance between the sensor array and equipment;
the multi-channel real-time data acquisition module is used for carrying out multi-channel real-time acquisition on the sound pressure and particle vibration velocity data based on labview;
the filtering module is used for filtering data points with amplitude values smaller than a preset value on a measuring surface by using the amplitude filtering coefficient of the sound field measuring surface;
the amplitude filter coefficient C p And C u Is defined as:
wherein p is max And u max Respectively, maximum on the measuring planeSound pressure and particle vibration velocity, p cutoff And u cutoff Cutting off sound pressure and particle vibration velocity during filtration, wherein C p And C u Taking 15 dB;
the sound field reconstruction module is used for performing sound field reconstruction by using a near-field acoustic holography method based on an equivalent source method, wherein the sound field reconstruction comprises sound pressure sound field reconstruction, particle vibration velocity sound field reconstruction and sound intensity field sound field reconstruction;
the sound field reconstruction module is specifically configured to:
aiming at different characteristics of sound pressure and particle vibration velocity, solving the problem of uncertainty generated in the matrix inversion process by using a Tikhonov regularization method, solving regularization parameters by using a Bayesian method for sound field reconstruction of the sound pressure, solving the sound field reconstruction of the particle vibration velocity by using broadband sound holography based on sound pressure differential filtering, and calculating a sound intensity field by using the reconstructed sound pressure and the particle vibration velocity;
the Bayesian method obtains optimal regularization parameters by minimizing the following functions:
wherein σ k Is the k-th singular value, y, of the transfer function matrix G k Is the kth element of the vector y, y ═ U H P and U are left singular matrixes obtained by SVD decomposition of the transfer function matrix, and P is sound pressure on the measuring surface;
let theta be lambda 2 The derivative of J (theta) with respect to theta is obtained:
combining the above formulas, and solving for J (lambda) by gradient descent method 2 ) Finally obtaining a regularization parameter lambda;
the sound field reconstruction module is further specifically configured to:
reconstructing sound pressure P near an equivalent source surface using Bayesian regularization 1 And P 2 ，P 1 And P 2 The sound pressure on two planes separated by delta is the vibration velocity V of mass points near an equivalent source according to a wave equation 1 From P 1 And P 2 The difference approximation of (d) yields:
wherein, omega is wave number, and P is air density; when Δ is sufficiently small, V 1 Equivalent to the real particle vibration speed; then, for V 1 Normalization processing is performed, and the normalized values are used as a filter gamma of particle vibration velocity equivalent source intensity:
on the basis, iterative solution is carried out on the particle vibration velocity equivalent source intensity by utilizing the broadband acoustic holography, and each iteration of a preset step number carries out filtering processing on the equivalent source intensity qk by utilizing an equivalent source intensity filter gamma:
q k+1 ＝q k ·γ
and finally, realizing the reconstruction of the particle vibration velocity by using the particle vibration velocity equivalent source strength q meeting the convergence condition.
2. A sound field reconstruction method based on sound pressure and particle vibration velocity is characterized by comprising the following steps:
scanning by using an array formed by acoustic vector sensors to obtain the sound pressure and particle vibration velocity data of a measuring surface;
the method comprises the following steps that a multi-sensor scanning module is based on, wherein the multi-sensor scanning module comprises a plurality of acoustic vector sensors, a sensor bracket, a reference microphone, an XY linear module and a scanning stepping controller;
the acoustic vector sensors are used for simultaneously measuring sound pressure and particle vibration velocity data, the sensor support is used for accommodating and fixing a plurality of acoustic vector sensors, the sensor support is connected with the XY linear module, the scanning stepping controller controls the sensor support to move up and down for scanning, and the scanning stepping controller controls the sensor array to move back and forth to adjust the distance between the sensor array and equipment;
multi-channel real-time acquisition is carried out on sound pressure and particle vibration velocity data based on labview;
filtering data points with amplitude values smaller than a preset value on a measuring surface by using an amplitude value filtering coefficient of the sound field measuring surface;
the amplitude filter coefficient C p And C u Is defined as:
wherein p is max And u max Respectively the maximum sound pressure and particle vibration speed on the measuring surface, p cutoff And u cutoff Cutting off sound pressure and particle vibration velocity during filtering, wherein C p And C u Taking 15 dB;
performing sound field reconstruction by using a near-field acoustic holography method based on an equivalent source method, wherein the sound field reconstruction comprises sound pressure sound field reconstruction, particle vibration velocity sound field reconstruction and sound intensity field sound field reconstruction;
aiming at different characteristics of sound pressure and particle vibration velocity, solving the problem of uncertainty generated in the matrix inversion process by using a Tikhonov regularization method, solving regularization parameters by using a Bayesian method for sound field reconstruction of the sound pressure, solving the sound field reconstruction of the particle vibration velocity by using broadband sound holography based on sound pressure differential filtering, and calculating a sound intensity field by using the reconstructed sound pressure and the particle vibration velocity;
the Bayesian method obtains optimal regularization parameters by minimizing the following functions:
wherein σ k Is the k-th singular value, y, of the transfer function matrix G k Is the kth element of the vector y, y ═ U H P and U are left singular matrixes obtained by SVD decomposition of the transfer function matrix, and P is sound pressure on the measuring surface;
let theta be lambda 2 The derivative of J (theta) with respect to theta is obtained:
combining the above formulas, and solving for J (lambda) by gradient descent method 2 ) Finally obtaining a regularization parameter lambda;
the sound field reconstruction step comprises:
reconstructing sound pressure P near an equivalent source surface using Bayesian regularization 1 And P 2 ，P 1 And P 2 The sound pressure on two planes separated by delta is the vibration velocity V of mass points near an equivalent source according to a wave equation 1 From P 1 And P 2 The difference approximation of (d) yields:
wherein, omega is wave number, rho is air density; when Δ is sufficiently small, V 1 Equivalent to the real particle vibration speed; then, for V 1 Normalization processing is performed, and the normalized values are used as a filter gamma of particle vibration velocity equivalent source intensity:
on the basis, iterative solution is carried out on the particle vibration velocity equivalent source intensity by utilizing the broadband acoustic holography, and the equivalent source intensity q is obtained by utilizing an equivalent source intensity filter gamma for each iteration preset step number k And (3) carrying out filtering treatment:
q k+1 ＝q k ·γ
and finally, realizing the reconstruction of the particle vibration velocity by using the particle vibration velocity equivalent source strength q meeting the convergence condition.
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