CN113392543A - High-precision identification method for noise source of asynchronous motor - Google Patents
High-precision identification method for noise source of asynchronous motor Download PDFInfo
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Abstract
The invention relates to a high-precision identification method of an asynchronous motor noise source, which comprises the following steps: s1: establishing an electromagnetic, fluid and solid coupling model of the asynchronous motor, acquiring electromagnetic, mechanical and aerodynamic excitation sources of the asynchronous motor as input, analyzing a vibration transmission path, acquiring surface vibration response characteristic data of a shell of the asynchronous motor, and acquiring sound field radiation model data of the asynchronous motor by a boundary element method; s2: performing DFT transformation on the obtained motor shell simulation sound field image, and determining a sparse expression matrix and sparsity capable of restoring a complete motor sound field based on a DFT dictionary and comparing peak signal-to-noise ratios of a reconstructed image and an original image under different sparsity degrees; s3: designing measuring point distribution and an observation matrix according to the determined optimal sparsity, the characteristics of the noise intensity area of the asynchronous motor and an equidistant distribution mode; s4: based on the distribution of the measuring points, the positions of the measuring points are arranged, and sound intensity discrete measurement is carried out on each measuring point; and (3) carrying out post-processing reconstruction on the sparse discrete measurement point data by adopting a full variation augmented Lagrange alternating direction algorithm, and finally realizing high-resolution imaging of the sound intensity sparse measurement.
Description
Technical Field
The invention relates to a high-precision identification method of a noise source, in particular to a high-precision identification method of an asynchronous motor noise source.
Background
Electric motors have been widely used in various aspects of social operations, such as transportation, national defense and military, aerospace, industrial production, automotive electronics, and household appliances. The motor also presents an annoying vibration and noise problem while providing power to the electromechanical device. For a large motor, severe vibration not only accompanies strong noise, but also damages the motor structure, affects production, and even causes huge economic loss. For small and medium-sized motors, especially micro motors, the noise level is very interesting because the motors have gone deep into people's daily life. For a motor company, the vibration noise level of the motor is reduced in the design stage, so that the production cost can be reduced, and the product competitiveness is effectively improved. In the military field, the vibration noise of the motor also plays a key role in the sound stealth of military equipment.
In order to solve the noise problem of the motor, firstly, the noise source needs to be identified. The identification of the noise source is to analyze each noise source of the machine equipment, know the vibration mechanism of the machine equipment and locate the position of the noise source. For most asynchronous machines, the noise originates mainly from electromagnetic noise, mechanical noise and aerodynamic noise. A sound intensity method, a vibration measurement method, and the like are generally used in the conventional noise source identification method. The sound intensity method is greatly influenced by the environment, has low precision and can only be preliminarily evaluated. The vibration measurement method is to calculate the radiation efficiency of different surfaces to obtain the noise value, and at present, it is still difficult to theoretically accurately determine the radiation efficiency of different structures of the motor. The complexity of sound source composition and the conventional identification method of the current motor noise source are difficult to accurately identify the noise source, and the subsequent noise optimization work is directly influenced, so that the high-precision identification method for researching the motor noise source has important value.
The asynchronous motor noise source identification system takes the collected information as a basis, and if the accuracy of the identification result is ensured by a large collection amount, the pressure of the system on data transmission and storage is increased; if the acquisition amount is reduced, the key information may be lost, and the identification of the noise source is missed. Therefore, the information acquisition is realized by a compression sampling technology, the aim of reducing the data acquisition amount while not losing key information can be achieved, the waste of a large amount of sampling information in the traditional sampling is effectively avoided, the sampling complexity is reduced, and the system cost is reduced.
Disclosure of Invention
The invention aims to avoid and solve the technical problems and provides a method for acquiring shell sound field response data by coupling an electromagnetic, fluid and solid excitation source to act on an asynchronous motor, carrying out post-processing reconstruction on the data, displaying a shell sound field cloud chart at high precision and accurately predicting the excitation source.
In order to achieve the purpose, the technical scheme of the invention is as follows: a high-precision identification method of a noise source comprises the following steps:
step S1: acquiring data of each excitation source by establishing an electromagnetic, fluid and solid coupling model of the asynchronous motor, thereby acquiring surface vibration response characteristic data of the shell of the asynchronous motor, taking the surface vibration response characteristic data as input data, and acquiring sound field radiation model data of the asynchronous motor by a boundary element method;
step S2: performing Discrete Fourier Transform (DFT) on the asynchronous motor simulation sound field image obtained in the step S1, determining a sparse expression matrix capable of restoring the complete sound field of the asynchronous motor based on a DFT dictionary, and comparing peak signal-to-noise ratios of a plurality of reconstructed images and an original image under different sparsity degrees to obtain sparsity closest to an original image;
step S3: according to the optimal sparsity of the complete sound field of the asynchronous motor determined in the step S2, combining a sparse random matrix, designing measuring point distribution and observation matrixes with different densities based on the characteristics of the noise intensity area of the asynchronous motor and by using an equidistant distribution mode;
step S4: based on the measuring point distribution determined in the step S3, carrying out position arrangement of the measuring points, and carrying out sound intensity discrete measurement on each measuring point; and (3) carrying out post-processing reconstruction on the sparse discrete measurement point data by adopting a full variation augmented Lagrange alternating direction algorithm, and finally realizing high-resolution imaging of the sound intensity sparse measurement.
In an embodiment of the present invention, the step S1 is implemented as follows:
s11: acquiring electromagnetic excitation source data received by a stator, mechanical excitation source data received by a rotor and a bearing and aerodynamic excitation source data received by a fan by establishing an electromagnetic, fluid and solid coupling model of an asynchronous motor;
s12: electromagnetic, mechanical and aerodynamic excitation sources of the asynchronous motor are used as input, a vibration transmission path is analyzed, and vibration is transmitted to the asynchronous motor shell to initiate surface vibration response characteristic data of the asynchronous motor shell; and (3) taking the surface vibration response characteristic data of the asynchronous motor shell as input, and acquiring the sound field radiation model data of the asynchronous motor by a boundary element method.
In an embodiment of the present invention, the step S11 is implemented as follows:
s111: establishing a geometric model of the asynchronous motor, setting segmentation parameters, dividing a motor grid, and calculating characteristic data of a motor magnetic field and an electromagnetic excitation source through a finite element model of the motor;
s112: and (3) importing the three-dimensional geometric model of the asynchronous motor into rigid body dynamics software, establishing a constraint and drive relationship, importing electromagnetic force into a moving part in the asynchronous motor, setting a contact model, and acquiring mechanical excitation source data of a rotor and a bearing.
S113: and (3) introducing the three-dimensional geometric model of the asynchronous motor into fluid dynamics software, carrying out grid division on fan blades in the asynchronous motor, setting air flow field conditions, and acquiring aerodynamic excitation source data received by the fan.
In an embodiment of the present invention, the step S12 is implemented as follows:
s121: carrying out modal analysis on the asynchronous motor by means of a finite element analysis method;
s122: analyzing the transmission paths of the excitation sources as follows: electromagnetic excitation source: stator-housing; rotor imbalance: rotor-stator-housing; bearing: bearing-rotor-stator-housing; aerodynamic excitation source: fan-housing.
S123: by means of a harmonic response analysis module in finite element analysis software, electromagnetic excitation source data are led in a stator, a mechanical excitation source is led in a corresponding part of a rotor and a bearing, and an aerodynamic excitation source is led in a fan, so that electromagnetic, flow and solid excitation source coupling is realized, and further, the surface vibration response characteristic data of the asynchronous motor shell under the coupling action of the electromagnetic, flow and solid excitation sources are obtained;
s124: and importing the vibration response characteristic data of the asynchronous motor shell and the triangular surface mesh of the surface of the asynchronous motor shell by using a boundary element analysis module in the acoustic simulation platform to obtain a sound field response distribution cloud picture.
In an embodiment of the present invention, the step S2 is implemented as follows:
s21: carrying out Discrete Fourier Transform (DFT) on the obtained asynchronous motor simulation sound field image to obtain a spectrogram with the same size as the image;
s22: obtaining different sparsity corresponding to the sound intensity spectrogram of the asynchronous motor based on the DFT dictionary and reconstructing the image;
s23: and comparing the peak signal-to-noise ratio PSNR of the reconstructed image and the original image to obtain the sparsity of the reconstructed image with the maximum PSNR value, namely the sparsity closest to the original image.
In an embodiment of the present invention, the content of step S3 is as follows:
the number of the measuring points is the sparseness, different measuring points are distributed in the sound intensity region according to the noise characteristics of the asynchronous motor and are distributed in an equidistant mode, and an observation matrix is formed
By the formula
And (4) determining.
In an embodiment of the present invention, the content of step S4 is as follows:
s41: establishing a reconstructed signal mathematical model;
min TV(X)subject to Y＝AX
wherein Xi, j is a matrix value of a (i, j) point, and X is a signal needing to be reconstructed;
s42: converting the model into a form without constraint by using an augmented Lagrange method and introducing a relaxation variable omega;
s43: by adopting an alternating direction transformation method, the problem can be converted into two sub-problems, and the solution is carried out by an iteration method.
Problem with the w child:
the u sub-problem:
drawings
FIG. 1 is a flow chart of a high-precision identification method of an asynchronous motor noise source.
Detailed Description
The technical scheme of the invention is specifically explained below with reference to the accompanying drawings.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The invention discloses a high-precision identification method for electromagnetic and solid excitation sources of an asynchronous motor, which is a flow chart of a high-precision identification method for noise sources of the asynchronous motor shown in figure 1 and comprises the following steps:
step S1: acquiring data of each excitation source by establishing an electromagnetic, fluid and solid coupling model of the asynchronous motor, thereby acquiring surface vibration response characteristic data of the shell of the asynchronous motor, taking the surface vibration response characteristic data as input data, and acquiring sound field radiation model data of the asynchronous motor by a boundary element method;
step S2: performing Discrete Fourier Transform (DFT) on the asynchronous motor simulation sound field image obtained in the step S1, determining a sparse expression matrix capable of restoring the complete sound field of the asynchronous motor based on a DFT dictionary, and comparing peak signal-to-noise ratios of a plurality of reconstructed images and an original image under different sparsity degrees to obtain sparsity closest to an original image;
step S3: according to the optimal sparsity of the complete sound field of the asynchronous motor determined in the step S2, combining a sparse random matrix, designing measuring point distribution and observation matrixes with different densities based on the characteristics of the noise intensity area of the asynchronous motor and by using an equidistant distribution mode;
step S4: based on the measuring point distribution determined in the step S3, carrying out position arrangement of the measuring points, and carrying out sound intensity discrete measurement on each measuring point; and (3) carrying out post-processing reconstruction on the sparse discrete measurement point data by adopting a full variation augmented Lagrange alternating direction algorithm, and finally realizing high-resolution imaging of the sound intensity sparse measurement.
Further, step S1 includes:
establishing a two-dimensional model according to the geometric dimension of the asynchronous motor structure, adding material attributes of all parts of the motor, distributing materials of all parts of the two-dimensional model, dividing unit grids, defining boundary conditions, applying loads, and calculating the motor magnetic field and electromagnetic force according to a finite element model of the motor electromagnetic field. Establishing a three-dimensional model according to the axial dimension of the asynchronous motor structure, setting segmentation parameters, dividing motor grids, and calculating the magnetic field and the electromagnetic force of the motor according to a finite element model of the motor. And (3) importing the three-dimensional geometric model of the asynchronous motor into rigid body dynamics software, establishing a constraint and drive relationship, importing electromagnetic force into a moving part in the asynchronous motor, setting a contact model, and acquiring mechanical excitation source data of a rotor and a bearing. And (3) introducing the three-dimensional geometric model of the asynchronous motor into fluid dynamics software, carrying out grid division on fan blades in the asynchronous motor, setting air flow field conditions, and acquiring aerodynamic excitation source data received by the fan.
Carrying out modal analysis on the asynchronous motor by means of a finite element analysis method; analyzing the transmission paths of the excitation sources as follows: electromagnetic excitation source: stator-housing; electrical rotor imbalance: rotor-stator-housing; bearing: bearing-rotor-stator-housing; aerodynamic excitation source: fan-housing. Electromagnetic excitation source data are introduced to the stator by a harmonic response analysis module in finite element analysis software, a mechanical excitation source is introduced to a corresponding part of the rotor and the bearing, and aerodynamic excitation source data are introduced to the fan. Realizing the coupling of electromagnetic, fluid and solid excitation sources, and further acquiring the surface vibration response characteristic data of the asynchronous motor shell under the coupling action of the electromagnetic, fluid and solid excitation sources; and importing the vibration response characteristic data of the asynchronous motor shell and the triangular surface mesh of the surface of the asynchronous motor shell by using a boundary element analysis module in the acoustic simulation platform to obtain a sound field response distribution cloud picture.
Further, step S2 includes:
the algorithm principle of compressed sensing is researched, and a sparse measurement imaging method of sound intensity is theoretically established. Based on the sound field response distribution cloud chart obtained by the original excitation signal of the asynchronous motor in the step S1, the rows and columns of the sound field response distribution cloud chart are transformed by a Discrete Fourier Transform (DFT) method, so as to obtain a spectrogram with the same size as the diagram, wherein a two-dimensional Discrete Fourier Transform (DFT) formula is as follows:
the discrete signal is expressed as:
θN(k)＝DFTN[x(n)]＝Ψx n,k＝0,1,…,N-1
in the formula, Ψ is an orthogonal discrete fourier transform basis, x is a corresponding sparse transform coefficient of the signal θ, and k is the sparsity of x. The Fourier transform base is shown as the following formula:
based on the DFT dictionary, different sparsity corresponding to the noise intensity distribution cloud picture of the asynchronous motor can be obtained through sparsification processing of the original signal. And restoring the spectrogram based on different sparsity to obtain a reconstructed image under different sparsity. And selecting the image with the highest similarity by comparing the peak signal-to-noise ratio (PSNR) of the reconstructed image and the original image to obtain the sparsity of the image. The peak signal-to-noise ratio PSNR is given by:
where MSE is the mean square error, and a (i, j) and a' (i, j) are the gray scale values of the original image and the reconstructed image at point (i, j), respectively. The larger the PSNR value is, the higher the similarity between the two images is.
Further, step S3 includes:
based on the sound intensity distribution characteristics of the asynchronous motor simulation sound field, the positions of the random measuring points are guided and designed by combining the sparse random matrix, and a mathematical formula corresponding to the observation matrix is deduced. Obtaining the number of the measuring points according to the sparsity obtained in the step S2, and distributing the measuring points in an equidistant mode, so that the observation matrix
By the formula
Determining。
Is a zero matrix of M × N, in the matrix
Optionally selecting k positions in all column vectors, and making the value of k positions be 1. According to the characteristics that the noise of the motor mainly comes from the stator, the rotor, the bearing and the fan, a plurality of measuring points are arranged in the sound intensity point area, and a small number of measuring points are arranged in the area with weaker sound intensity and gentle fluctuation.
Further, the full variation augmented lagrangian alternating direction algorithm in step S4 specifically includes:
and (4) according to the random measuring point position distribution of the sound intensity of the asynchronous motor determined in the step (S3), carrying out position arrangement of the measuring points, and carrying out sound intensity discrete measurement on each measuring point. Firstly, a mathematical model is established, as shown in the following formula:
min TV(X)subject to Y＝AX
in the formula, Xi,XjIs the matrix value at point (i, j) and X is the signal that needs to be reconstructed.
The model is then transformed into an unconstrained form using the augmented Lagrangian method and introducing a relaxation variable ω, as shown in the following equation:
and finally, converting the problem into two sub-problems by adopting an alternating direction transformation method to solve, namely solving w and u, and sequentially iterating after solving w and u based on an iteration mode.
The w subproblem is shown by the following formula:
the u problem is shown by the following formula:
after-treatment is carried out on sparse discrete measurement point data based on a full-variation augmented Lagrange alternating direction algorithm, the compressed sensing experiment reconstruction of the complete sound field of the asynchronous motor is completed, and finally high-resolution imaging of sound intensity sparse measurement is realized.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that are not thought of through the inventive work should be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope defined by the claims.
Claims (8)
1. A high-precision identification method for an asynchronous motor noise source is characterized by comprising the following steps:
step S1: acquiring data of each excitation source by establishing an electromagnetic, fluid and solid coupling model of the asynchronous motor, thereby acquiring surface vibration response characteristic data of the shell of the asynchronous motor, taking the surface vibration response characteristic data as input data, and acquiring sound field radiation model data of the asynchronous motor by a boundary element method;
step S2: performing Discrete Fourier Transform (DFT) on the asynchronous motor simulation sound field image obtained in the step S1, determining a sparse expression matrix capable of restoring the complete sound field of the asynchronous motor based on a DFT dictionary, and comparing peak signal-to-noise ratios of a plurality of reconstructed images and an original image under different sparsity degrees to obtain sparsity closest to an original image;
step S3: according to the optimal sparsity of the complete sound field of the asynchronous motor determined in the step S2, combining a sparse random matrix, designing measuring point distribution and an observation matrix with different densities based on the characteristics of the noise intensity area of the asynchronous motor and by using an equidistant distribution mode;
step S4: based on the measuring point distribution determined in the step S3, carrying out position arrangement of the measuring points, and carrying out sound intensity discrete measurement on each measuring point; and (3) carrying out post-processing reconstruction on the sparse discrete measurement point data by adopting a full variation augmented Lagrange alternating direction algorithm, and finally realizing high-resolution imaging of the sound intensity sparse measurement.
2. The method for identifying the noise source of the asynchronous motor according to claim 1, wherein the method comprises the following steps: the step S1 specifically includes the following steps:
s11, acquiring electromagnetic excitation source data of a stator, mechanical excitation source data of a rotor and a bearing and aerodynamic excitation source data of a fan by establishing an electromagnetic, fluid and solid coupling model of the asynchronous motor;
s12, taking electromagnetic, mechanical and aerodynamic excitation sources of the asynchronous motor as input, analyzing a vibration transmission path, and acquiring vibration transmission to the asynchronous motor shell and initiating the surface vibration response characteristic data of the asynchronous motor shell; and (3) taking the surface vibration response characteristic data of the asynchronous motor shell as input, and acquiring the sound field radiation model data of the asynchronous motor by a boundary element method.
3. The method for identifying the noise source of the asynchronous motor according to claim 2, wherein the method comprises the following steps: the step S11 specifically includes the following steps:
s111: establishing a geometric model of the asynchronous motor, setting segmentation parameters, dividing a motor grid, and calculating characteristic data of a motor magnetic field and an electromagnetic excitation source through a finite element model of the motor;
s112: importing the three-dimensional geometric model of the asynchronous motor into rigid body dynamics software, establishing a constraint and drive relationship, importing electromagnetic force into a moving part in the asynchronous motor, setting a contact model, and acquiring mechanical excitation source data received by a rotor and a bearing;
s113: and (3) introducing the three-dimensional geometric model of the asynchronous motor into fluid dynamics software, carrying out grid division on fan blades in the asynchronous motor, setting air flow field conditions, and acquiring aerodynamic excitation source data received by the fan.
4. The method for identifying the noise source of the asynchronous motor according to claim 2, wherein the method comprises the following steps: the step S12 specifically includes the following steps:
s121: carrying out modal analysis on the asynchronous motor by means of a finite element analysis method;
s122: analyzing the transmission paths of the excitation sources as follows: electromagnetic excitation source: stator-housing; rotor imbalance: rotor-stator-housing; bearing: bearing-rotor-stator-housing; aerodynamic excitation source: fan-housing.
S123: by means of a harmonic response analysis module in finite element analysis software, electromagnetic excitation source data are led in a stator, a mechanical excitation source is led in a corresponding part of a rotor and a bearing, and an aerodynamic excitation source is led in a fan, so that electromagnetic, flow and solid excitation source coupling is realized, and further, the surface vibration response characteristic data of the asynchronous motor shell under the coupling action of the electromagnetic, flow and solid excitation sources are obtained;
s124: and importing the vibration response characteristic data of the asynchronous motor shell and the triangular surface mesh of the surface of the asynchronous motor shell by using a boundary element analysis module in the acoustic simulation platform to obtain a sound field response distribution cloud picture.
5. The method for identifying the noise source of the asynchronous motor according to claim 1, wherein the method comprises the following steps: the step S2 specifically includes the following steps:
s21: carrying out Discrete Fourier Transform (DFT) on the obtained asynchronous motor simulation sound field image to obtain a spectrogram with the same size as the image;
s22: obtaining different sparsity corresponding to the sound intensity spectrogram of the asynchronous motor based on the DFT dictionary and reconstructing the image;
s23: and comparing the peak signal-to-noise ratio PSNR of the reconstructed image and the original image to obtain the sparsity of the reconstructed image with the maximum PSNR value, namely the sparsity closest to the original image.
6. The method for identifying the noise source of the asynchronous motor according to claim 1, wherein the method comprises the following steps: the specific content of step S3 is:
the number of the measuring points is the sparseness, different measuring points are distributed in the sound intensity region according to the noise characteristics of the asynchronous motor and are distributed in an equidistant mode, and an observation matrix is formed
By the formula
And (4) determining.
7. The method for identifying the noise source of the asynchronous motor according to claim 1, wherein the method comprises the following steps: the step S4 specifically includes the following steps:
s41: establishing a reconstructed signal mathematical model:
minTV(X)subject to Y＝AX
wherein Xi, j is a matrix value of a (i, j) point, and X is a signal needing to be reconstructed;
s42: the model is transformed into an unconstrained form using the augmented lagrange method and introducing a relaxation variable omega,
s43: by adopting an alternating direction transformation method, the problem can be converted into two sub-problems to be solved, namely W and U are solved, and the W and the U are solved firstly and then sequentially through iteration in an iteration mode.
8. The method for identifying the noise source of the asynchronous motor according to claim 7, wherein the method comprises the following steps: the step S43 includes two sub-questions,
problem with the w child:
the u sub-problem:
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