Fan acoustic modal decomposition method and system based on multi-measurement Bayesian compressed sensing

Abstract

The present invention discloses a fan acoustic modal decomposition method and system based on multi-measurement Bayesian compressed sensing, the method comprising: step 1, according to the highest order of the circumferential dominant acoustic mode of the single-tone noise of the aircraft engine fan and the modal monitoring range, determining the maximum number of microphones arranged along the circumference of the aircraft engine fan casing and their installation position angles; step 2, randomly selecting a given number of microphone positions from the determined installation position angles, and constructing a multi-measurement vector Bayesian compressed sensing model according to the modal monitoring range and the selected microphone positions, wherein the given number is not greater than the maximum number of microphones; step 3, based on the multi-measurement vector Bayesian compressed sensing model, measuring the circumferential dominant acoustic mode of the aircraft engine fan. The method can automatically adapt to different speed conditions for acoustic modal measurement and has better robustness.

Description

Technical Field

[0001] The invention belongs to the technical field of aircraft engine noise testing and relates to a fan sound modal decomposition method and system based on multi-measurement Bayesian compressed sensing. Background Art

[0002] With the widespread application of high bypass ratio turbofan aircraft engines, fan noise has gradually become the dominant component of aircraft engine noise, which has attracted widespread social attention. According to its formation mode, fan noise is divided into single-tone noise at each blade passing frequency and broadband noise distributed in the entire frequency domain. Among them, in the noise spectrum, the intensity of single-tone noise is significantly greater than the other noise components, and it is the dominant component of fan noise. The refined noise reduction design of aircraft engine fans requires a deep understanding of the generation and propagation of acoustic modes in single-tone noise, and the decomposition of the modal structure of single-tone noise and the measurement of acoustic modes play an important guiding role in this regard.

[0003] The use of an annular microphone array to decompose the circumferential acoustic modes of aircraft engine fan single-tone noise is currently the most commonly used method in the field of aircraft engine fan noise acoustic mode decomposition. The classic uniformly arranged microphone array has the disadvantages of high testing cost and complex testing system, and is difficult to be applied to the real aircraft engine duct acoustic mode measurement. The acoustic mode monitoring compressed sensing model based on non-uniform layout L1 norm sparse regularization is greatly affected by the regularization parameter, often underestimates the amplitude of the dominant mode of rotation-static interference, and does not have the characteristics of parameter adaptation.

[0004] In view of this, there is an urgent need to provide a system and method that can accurately identify the acoustic mode order and amplitude of fan noise. Summary of the invention

[0005] In order to overcome the above problems, the present invention proposes a fan acoustic modal decomposition method and system based on multi-measurement Bayesian compressed sensing, the method comprising: step 1, according to the highest order of the circumferential dominant acoustic mode of the single-tone noise of the aircraft engine fan and the modal monitoring range, determine the maximum number of microphones arranged along the circumference of the aircraft engine fan casing and their installation position angles; step 2, randomly select a given number of microphone positions from the determined installation position angle, and construct a multi-measurement vector Bayesian compressed sensing model according to the modal monitoring range and the selected microphone positions, wherein the given number is not greater than the maximum number of microphones; step 3, based on the multi-measurement vector Bayesian compressed sensing model, measure the circumferential dominant acoustic mode of the aircraft engine fan. The method can automatically adapt to different speed conditions for acoustic modal measurement, and has better robustness, thereby completing the present invention.

[0006] Specifically, the purpose of the present invention is to provide the following aspects:

[0007] In a first aspect, a method for measuring acoustic modes of an aircraft engine fan is provided, the method comprising:

[0008] Step 1, determining the maximum number of microphones arranged along the circumference of the aircraft engine fan casing and their installation position angles according to the highest order of the circumferential dominant sound mode of the aircraft engine fan single-tone noise and the modal monitoring range;

[0009] Step 2, randomly selecting a given number of microphone positions from the determined installation position angles, and constructing a multi-measurement vector Bayesian compressed sensing model according to the modal monitoring range and the selected microphone positions, wherein the given number is not greater than the maximum number of microphones;

[0010] Step 3: Based on the multi-measurement vector Bayesian compressed sensing model, the circumferential dominant acoustic mode of the aircraft engine fan is measured.

[0011] The step 1 comprises the following steps:

[0012] Step 1-1, obtaining the highest order and modal monitoring range of the circumferential dominant sound mode of the single-tone noise of the aircraft engine fan according to the number of rotor blades and stator blades of the aircraft engine fan;

[0013] Step 1-2, determining the maximum number of microphones arranged along the circumference of the aircraft engine fan casing and their installation position angles according to the highest order of the circumferential dominant sound mode of the aircraft engine fan single-tone noise.

[0014] In step 1-1, the circumferential dominant sound mode order of the aircraft engine fan single-tone noise is expressed as follows: ;

[0015] in, It represents the pressure pulsation order caused by the unsteady aerodynamic force caused by the fan's rotation and static interference; is the distorted spatial harmonics generated for the static; is the number of rotor blades; is the number of stator blades.

[0016] Optionally, =1.

[0017] Among them, the highest order of the circumferential dominant sound mode of the single-tone noise of the aircraft engine fan is expressed as , which is 2~3 larger than the maximum absolute value of the circumferential dominant sound mode order m of the single-tone noise of the aircraft engine fan.

[0018] In step 1-1, the modal monitoring range is .

[0019] In step 1-2, the maximum number of microphones arranged along the circumference of the aircraft engine fan case is .

[0020] The step 2 comprises the following steps:

[0021] Step 2-1, according to the given number of microphone installation , selected from the determined microphone installation position Install microphones at different positions and collect sound pressure time domain signals;

[0022] Step 2-2, dividing the sound pressure time domain signal into multiple snapshot signals by a rectangular window;

[0023] Step 2-3, perform fast Fourier transform on the snapshot signals respectively to construct the observation matrix;

[0024] Step 2-4, based on the modal monitoring range and Microphone installation positions to build a perception matrix;

[0025] Step 2-5, construct a multi-measurement vector Bayesian compressed sensing model based on the perception matrix and the observation matrix.

[0026] In step 2-1, the given number of microphones is 6 to 10.

[0027] In a second aspect, a system for measuring acoustic modes of an aircraft engine fan is provided, the system comprising:

[0028] The sound pressure time domain signal measurement module is used to determine the maximum number of microphones arranged along the circumference of the aircraft engine fan casing and their installation position angles according to the highest order of the circumferential dominant sound mode of the aircraft engine fan single-tone noise and the modal monitoring range;

[0029] A perception model building module, which is used to randomly select a given number of microphone positions from the determined installation position angles, and build a multi-measurement vector Bayesian compressed sensing model according to the modal monitoring range and the selected microphone positions;

[0030] The acoustic mode calculation module is used to measure the circumferential dominant acoustic mode of the aircraft engine fan based on a multi-measurement vector Bayesian compressed sensing model.

[0031] The beneficial effects of the present invention include:

[0032] (1) The present invention uses fewer microphone measurement points to achieve the measurement of the acoustic mode of the single-tone noise of the aircraft engine fan. The traditional acoustic mode measurement method requires that the number of microphones is greater than or equal to twice the highest modal order. That is, compared with the traditional acoustic mode measurement method, the method of the present invention reduces the number of installed microphones.

[0033] (2) The present invention uses a fast marginal likelihood maximization algorithm to measure the circumferential dominant acoustic mode of the aircraft engine fan. Compared with the solution method of the classic L1 norm, the measurement accuracy is greatly improved.

[0034] (3) The method for measuring the acoustic modal of an aircraft engine fan provided by the present invention combines the structural sparse prior of the acoustic modal of single-tone noise in the wavenumber domain, and establishes a multi-measurement vector Bayesian compressed sensing measurement model under a small number of measurement points and a non-uniform microphone layout. The method has the characteristics of sparsity adaptation and does not require manual setting of parameters such as sparsity and regularization coefficient. It can automatically adapt to different speed conditions for acoustic modal measurement. It can also effectively improve the accuracy of the circumferential acoustic modal measurement of the single-tone noise of the aircraft engine fan, and realize the accurate identification of the modal order and amplitude of the fan noise. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] By reading the detailed description of the preferred specific embodiments below, various other advantages and benefits of the present invention will become clear to those of ordinary skill in the art. The drawings in the specification are only used for the purpose of illustrating the preferred embodiments and are not considered to be limitations of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative work.

[0036] In the attached picture:

[0037] Figure 1 The flow chart of the method for measuring the acoustic modes of an aircraft engine fan of the present invention is shown;

[0038] Figure 2 A schematic diagram showing the installation position of the microphone in Example 1 is shown;

[0039] Figure 3 The figure shows the sound pressure signal obtained by measuring in Example 1;

[0040] Figure 4 A flow chart showing a method for measuring the circumferential dominant acoustic mode of an aircraft engine fan using a fast marginal likelihood maximization algorithm according to the present invention;

[0041] Figure 5 A comparison diagram of the Bayesian method in Example 1, the classical L1 norm method in Comparative Example 1, and the reference modal amplitude is shown;

[0042] Figure 6 (a) shows the Bayesian method in Example 1, the classical L1 norm method in Comparative Example 1, and the reference mode in Comparison chart of modal amplitude monitoring results;

[0043] Figure 6(b) shows the Bayesian method in Example 1, the classical L1 norm method in Comparative Example 1, and the reference mode in Comparison chart of modal amplitude monitoring results. DETAILED DESCRIPTION

[0044] The following will refer to the attached Figure 1 6 (b) to describe specific embodiments of the present invention in more detail. Although specific embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0045] It should be noted that certain words are used in the specification and claims to refer to specific components. Those skilled in the art should understand that technicians may use different nouns to refer to the same component. This specification and claims do not use the difference in nouns as a way to distinguish components, but use the difference in the functions of the components as the criterion for distinction. As mentioned throughout the specification and claims, "including" or "comprising" is an open term, so it should be interpreted as "including but not limited to". The subsequent description of the specification is a preferred embodiment of the present invention, but the description is based on the general principles of the specification and is not intended to limit the scope of the present invention. The scope of protection of the present invention shall be determined by the attached claims.

[0046] In the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper", "lower", "inner", "outer", "front", "rear", etc. are directions or positional relationships based on the working state of the present invention, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the system or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", "third", and "fourth" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0047] To facilitate understanding of the embodiments of the present invention, further explanation will be given below by taking specific embodiments as examples in conjunction with the accompanying drawings, and each of the accompanying drawings does not constitute a limitation on the embodiments of the present invention.

[0048] On the one hand, if Figure 1 The method for measuring the acoustic modes of an aircraft engine fan is shown, and the method comprises:

[0049] Step 1, determining the maximum number of microphones arranged along the circumference of the aircraft engine fan casing and their installation position angles according to the highest order of the circumferential dominant sound mode of the aircraft engine fan single-tone noise and the modal monitoring range;

[0050] Step 2, randomly selecting a given number of microphone positions from the determined installation position angles, and constructing a multi-measurement vector Bayesian compressed sensing model according to the modal monitoring range and the selected microphone positions, wherein the given number is not greater than the maximum number of microphones;

[0051] Step 3: Based on the multi-measurement vector Bayesian compressed sensing model, a fast marginal likelihood maximization algorithm is used to measure the circumferential dominant acoustic mode of the aircraft engine fan.

[0052] The following is a detailed description of the aircraft engine fan acoustic modal measurement method.

[0053] Step 1: Determine the maximum number of microphones arranged along the circumference of the aircraft engine fan case and their installation position angles based on the highest order of the circumferential dominant sound mode of the aircraft engine fan single-tone noise and the modal monitoring range.

[0054] According to a preferred embodiment, step 1 comprises the following steps:

[0055] Step 1-1, according to the number of aircraft engine fan rotor blades 1 Number of stator blades 2 Obtain the highest order of the circumferential dominant sound mode of the single-tone noise of the aircraft engine fan and modal monitoring range;

[0056] Step 1-2: According to the highest order of the circumferential dominant sound mode of the single-tone noise of the aircraft engine fan Determine the maximum number of microphones arranged along the circumference of the aircraft engine fan case and their installation position angles.

[0057] In step 1-1, the circumferential dominant sound mode order m of the single-tone noise of the aircraft engine fan is expressed as follows: , is the maximum value in m.

[0058] in, It represents the pressure pulsation order caused by the unsteady aerodynamic force caused by the fan's rotation and static interference, and is usually taken as 1; is a non-negative integer, usually the first and second order single-tone noise modes. ; is the number of rotor blades 1; is the number of stator blades 2. When taking a value, take The corresponding minimum value, for example , , , ,enter When The minimum value is 5. When The minimum value of -12, then we get .

[0059] Furthermore, the modal monitoring range is In order to ensure reliability, the general modal measurement range will be slightly larger than the absolute value of the maximum acoustic modal order, that is, Greater than the maximum absolute value of the acoustic mode order , preferably, Maximum absolute value of acoustic modal order Big 2~3. For example, the above , then take the modal monitoring range is 14 or 15. If the modal measurement range is determined to be .

[0060] Furthermore, the maximum number of microphones arranged along the circumference of the aircraft engine fan case is In order to prevent the excessive number of microphones from causing inconvenience to subsequent measurements, the maximum number of microphones arranged along the circumference of the aircraft engine fan case does not need to be too large, usually less than Just 4~5 more will do.

[0061] In step 1-2, the microphones are evenly distributed along the circumference of the aircraft engine fan casing. At this time, the installation angle of the microphones arranged along the circumference of the aircraft engine fan casing is

[0062] , Indicates Installation position angle.

[0063] Step 2: randomly select a given number of microphone positions from the determined installation position angles, and construct a multi-measurement vector Bayesian compressed sensing model according to the modal monitoring range and the selected microphone positions, wherein the given number is not greater than the maximum number of microphones.

[0064] According to a preferred embodiment, step 2 comprises the following steps:

[0065] Step 2-1, according to the given number of microphone installation , selected from the determined microphone installation position Install microphones at different positions and collect sound pressure time domain signals;

[0066] Step 2-2, dividing the sound pressure time domain signal into multiple snapshot signals by a rectangular window;

[0067] Step 2-3, perform fast Fourier transform on the snapshot signal and construct the observation matrix ;

[0068] Step 2-4, based on the modal monitoring range and Microphone installation positions to build a perception matrix ;

[0069] Steps 2-5, based on the perception matrix With the observation matrix , construct a multi-measurement vector Bayesian compressed sensing model.

[0070] In step 2-1, randomly select The installation angle of the microphone is expressed as , yes Microphone No. The mounting angle of the microphone.

[0071] In step 2-1, the collected sound pressure time domain signal is , the subscript is the corresponding microphone number; is the time independent variable; Represents microphone number 1 over time The acquired sound pressure time domain signal changes.

[0072] In step 2-1, the number of microphones generally provided is 6 to 10, for example, 8.

[0073] In step 2-2, the sound pressure time domain signal Divided by rectangular window The nearly stationary signal, The time length of each snapshot signal is equal, that is, , Representative segment signal, .

[0074] In step 2-3, each snapshot signal is subjected to a fast Fourier transform to obtain the spectrum ,in is the frequency; select the blades (rotor blade 1 and stator blade 2) passing frequency The observation vector of each snapshot signal is constructed by the complex amplitude of the sound pressure at ,in For frequency conversion; The observation matrix can be obtained by splicing the observation vectors of the snapshot signals , whose dimensions are .

[0075] In step 2-4, based on the modal monitoring range and microphone installation position Building a perception matrix ,in , For the The mounting angle of the microphones; For the The modal order of the circumferential modal wave; is the total number of microphones; is the number of modal monitoring, ;

[0076] In step 2-5, based on the perception matrix With the observation matrix , construct a multi-measurement vector Bayesian compressed sensing model, that is, ,in is the measurement noise, which follows a zero-mean Gaussian distribution and has a variance of ; is the acoustic mode matrix, with dimension ; Obey the probability density distribution , is the identity matrix.

[0077] Step 3: Based on the multi-measurement vector Bayesian compressed sensing model, a fast marginal likelihood maximization algorithm is used to measure the circumferential dominant acoustic mode of the aircraft engine fan.

[0078] According to a preferred embodiment, Figure 4 The flowchart of the method for measuring the circumferential dominant acoustic mode of an aircraft engine fan using a fast marginal likelihood maximization algorithm is shown, wherein step 3 includes the following steps:

[0079] Step 3-1: Input the perception matrix into the fast marginal likelihood maximization algorithm and the observation matrix , and the noise variance estimate obtained based on the signal-to-noise ratio , perception matrix Each basis vector in The coefficient of variance and the linear regression coefficient Perform initial settings.

[0080] in, ; ; .

[0081] Furthermore, you need to set the maximum number of iterations and the convergence condition. Usually, the maximum number of iterations is set to 1000; the convergence condition is set to , where is the convergence threshold, usually is 0.0001.

[0082] Step 3-2, according to each basis vector The coefficient of variance and the linear regression coefficient Calculate the weight coefficient ,in, .

[0083] Step 3-3, calculate each weight coefficient Impact on the likelihood function ,in, , is the identity matrix, is the weight coefficient of the corresponding basis vector in the previous iteration.

[0084] Step 3-4, select the basis vector that minimizes the likelihood function .

[0085] Step 3-5, based on the basis vector index selected in step 3-4 , update the amplitude estimate mean and variance of the acoustic mode, and update each basis vector The coefficient of variance and the linear regression coefficient .

[0086] First, get the index The estimated mean amplitude at , and then update the estimated mean of the amplitude in the previous iteration , where is the estimated mean of the amplitudes in the previous iteration, is the identity matrix; and The new amplitude estimation mean of the acoustic mode is obtained by combining .

[0087] Furthermore, the updated variance is expressed as , , where is the variance in the previous iteration, Take the real part.

[0088] In steps 3-5, the updated basis vectors The coefficient of variance Expressed as ; Updated linear regression coefficients Expressed as .

[0089] Steps 3-6, convergence judgment When , the amplitude estimate mean and variance of the acoustic mode are output, otherwise steps 3-2 to 3-5 are repeated until the convergence condition is met.

[0090] Furthermore, convergence judgment The estimated mean of the amplitude output is used as the calculation result of the amplitude.

[0091] On the other hand, a system for measuring acoustic modal of an aircraft engine fan is provided, the system comprising:

[0092] The sound pressure time domain signal measurement module is used to measure the highest order of the circumferential dominant sound mode of the single-tone noise of the aircraft engine fan and modal monitoring range, determine the maximum number of microphones arranged along the circumference of the aircraft engine fan case and their installation position angles;

[0093] A perception model building module, which is used to randomly select a given number of microphone positions from the determined installation position angles, and build a multi-measurement vector Bayesian compressed sensing model according to the modal monitoring range and the selected microphone positions;

[0094] The acoustic mode calculation module is used to measure the circumferential dominant acoustic mode of the aircraft engine fan based on a multi-measurement vector Bayesian compressed sensing model and a fast marginal likelihood maximization algorithm.

[0095] The present invention is further described below through specific examples, but these examples are merely exemplary and do not constitute any limitation to the scope of protection of the present invention.

[0096] Example 1

[0097] Figure 2 The rotor blade 1, the stator blade 2, and the full array microphone installation position 3 (i.e., the highest order of the circumferential dominant sound mode of the single-tone noise of the aircraft engine fan) are shown. The position map of the sampled microphone 4 (i.e., a given number of randomly selected microphone positions) is shown.

[0098] (1) Number of rotor blades 1 in this example , Number of stator blades 2 According to the circumferential dominant sound mode order formula of aircraft engine fan single-tone noise , ,Pick Represents the first-order and second-order single-tone noise acoustic modes, and is calculated to be , in order to ensure reliability, the modal measurement range is determined to be .

[0099] The maximum number of microphones arranged along the circumference of the aircraft engine fan case is In order to prevent the inconvenience caused by too many microphones in subsequent measurements, The microphones are evenly distributed along the circumference of the aircraft engine fan casing. At this time, the microphone installation position angle arranged along the circumference of the aircraft engine fan casing is .

[0100] (2) Given the number of microphones installed , from step (1) 8 locations are randomly selected and installed at: , the collected part of the sound pressure time domain signal is as follows Figure 3 As shown in the figure, the time domain sound pressure signals collected by the microphones of the 8 channels are divided into 100 segments of nearly stable signals by rectangular windows, that is, a total of 800 segments of nearly stable signals, that is, a total of 800 segments of snapshot signals, and each snapshot signal is subjected to fast Fourier transform to obtain a spectrum; the blade passing frequency is 3000Hz, then in each snapshot signal, the observation vector composed of the complex sound pressure amplitude of the single-tone noise at a frequency of 3000Hz when the blade passes is: ,in Representative The observation vector of each snapshot signal is constructed as an observation matrix, that is,

[0101] .

[0102] Based on modal monitoring range and microphone installation position Building a perception matrix ,in , For the The installation angle of the microphone For the The modal order of the circumferential modal waves; construct a multi-measurement vector Bayesian compressed sensing model ,in is the measurement noise, which follows a zero-mean Gaussian distribution and has a variance of , is the acoustic mode matrix with a dimension of 31×100.

[0103] (3) If Figure 4 The fast marginal likelihood maximization algorithm is used to measure the circumferential dominant acoustic mode of an aircraft engine fan.

[0104] Input of the perception matrix in the fast marginal likelihood maximization algorithm and the observation matrix , initialize the noise variance estimate obtained based on the signal-to-noise ratio , perception matrix Each basis vector in The coefficient of variance and the linear regression coefficient ; Set the maximum number of iterations to 1000; Set the convergence condition to .

[0105] The final output for the modal and The amplitudes are 406.65Pa and 628.66Pa respectively. Since the true values ​​are 410Pa and 630Pa respectively, the errors are 3.35Pa and 1.34Pa respectively.

[0106] Comparative Example 1

[0107] The amplitude of the circumferential dominant acoustic mode of the aircraft engine fan is obtained in a manner similar to that of Example 1, except that in step (3), the classical L1 norm solution is adopted. Figure 5 The comparison diagram of the Bayesian method in Example 1, the classic L1 norm method in Comparative Example 1, and the reference modal amplitude is shown. The reference modal amplitude is the amplitude set when generating the simulation signal, which is the true value. It can be seen that the acoustic modal amplitude obtained by the classic L1 norm method in Comparative Example 1 is different from the modal amplitude in the and The amplitude errors of are 32.59Pa and 30.771Pa respectively, which are larger than the values ​​of the Bayesian method in Example 1. Under all 800 snapshots, Figure 6 (a) shows the differences between the Bayesian method in Example 1, the classical L1 norm method in Comparative Example 1, and the reference mode in 6(b) shows the comparison of the modal amplitude monitoring results of the Bayesian method in Example 1, the classical L1 norm method in Comparative Example 1, and the reference mode in Compared with the modal amplitude monitoring results of the proposed method, it is obvious that the amplitude calculation result obtained by the Bayesian method is closer to the actual modal amplitude. The proposed method based on multi-measurement vector Bayesian compressed sensing is better than the classic L1 norm method.

[0108] The present invention is described in detail above in combination with preferred embodiments and exemplary examples. However, it should be noted that these specific embodiments are only illustrative explanations of the present invention and do not constitute any limitation on the protection scope of the present invention. Without exceeding the spirit and protection scope of the present invention, various improvements, equivalent substitutions or modifications may be made to the technical content of the present invention and its embodiments, which all fall within the protection scope of the present invention. The protection scope of the present invention shall be subject to the attached claims.

Claims

1. A method for measuring the acoustic modal of an aircraft engine fan, characterized in that: The method comprises: Step 1, determining the maximum number of microphones arranged along the circumference of the aircraft engine fan casing and their installation position angles according to the highest order of the circumferential dominant sound mode of the aircraft engine fan single-tone noise and the modal monitoring range; Step 2, randomly selecting a given number of microphone positions from the determined installation position angles, and constructing a multi-measurement vector Bayesian compressed sensing model according to the modal monitoring range and the selected microphone positions, wherein the given number is not greater than the maximum number of microphones; Step 3, based on the multi-measurement vector Bayesian compressed sensing model, measuring the circumferential dominant acoustic mode of the aircraft engine fan; Wherein, the step 1 comprises the following steps: Step 1-1, obtaining the highest order and modal monitoring range of the circumferential dominant sound mode of the single-tone noise of the aircraft engine fan according to the number of rotor blades and stator blades of the aircraft engine fan; Step 1-2, determining the maximum number of microphones arranged along the circumference of the aircraft engine fan casing and their installation position angles according to the highest order of the circumferential dominant sound mode of the aircraft engine fan single-tone noise; In step 1-1, the circumferential dominant sound mode order of the aircraft engine fan single-tone noise is expressed as follows: ; in, It represents the pressure pulsation order caused by the unsteady aerodynamic force caused by the fan's rotation and static interference; The distorted spatial harmonics produced by the statics; is the number of rotor blades; is the number of stator blades; The step 2 comprises the following steps: Step 2-1, according to the given number of microphone installation , selected from the determined microphone installation position Install microphones at different positions and collect sound pressure time domain signals; Step 2-2, dividing the sound pressure time domain signal into multiple snapshot signals by a rectangular window; Step 2-3, perform fast Fourier transform on the snapshot signals respectively to construct the observation matrix; Step 2-4, based on the modal monitoring range and Microphone installation positions to build a perception matrix; Step 2-5, construct a multi-measurement vector Bayesian compressed sensing model based on the perception matrix and the observation matrix.

2. The method according to claim 1, characterized in that 。 3. The method according to claim 1, characterized in that The highest order of the circumferential dominant sound mode of the single-tone noise of an aircraft engine fan is expressed as , which is higher than the circumferential dominant sound mode order of the single-tone noise of the aircraft engine fan m The maximum absolute value in is 2~3 larger.

4. The method according to claim 1, characterized in that: In step 1-1, the modal monitoring range is .

5. The method according to claim 1, characterized in that In step 1-2, the maximum number of microphones arranged along the circumference of the aircraft engine fan case is .

6. The method according to claim 5, characterized in that In step 2-1, the given number of microphones is 6 to 10.

7. An aircraft engine fan acoustic modal measurement system implementing the aircraft engine fan acoustic modal measurement method of claim 1, characterized in that: The system comprises: The sound pressure time domain signal measurement module is used to determine the maximum number of microphones arranged along the circumference of the aircraft engine fan casing and their installation position angles according to the highest order of the circumferential dominant sound mode of the aircraft engine fan single-tone noise and the modal monitoring range; A perception model building module, which is used to randomly select a given number of microphone positions from the determined installation position angles, and build a multi-measurement vector Bayesian compressed sensing model according to the modal monitoring range and the selected microphone positions, wherein the given number is not greater than the maximum number of microphones; The acoustic mode calculation module is used to measure the circumferential dominant acoustic mode of the aircraft engine fan based on a multi-measurement vector Bayesian compressed sensing model.

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