CN114383716A - In-vehicle noise identification method based on conditional power spectrum analysis - Google Patents
In-vehicle noise identification method based on conditional power spectrum analysis Download PDFInfo
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Abstract
The invention discloses an in-vehicle noise identification method based on conditional power spectrum analysis, which comprises the following steps: determining a main noise source of the vehicle in the running process; establishing a vehicle acoustic transmission path model; determining a layout scheme of microphones, and arranging the microphones; determining a test working condition of the vehicle; testing a noise signal of the vehicle; testing a transfer function between a noise source and a target point in the vehicle by adopting a sound source substitution method; eliminating crosstalk between noise source signals by adopting a conditional power spectrum; and calculating to obtain the spectrum distribution of the noise energy in the vehicle according to the transmission path analysis model. The invention has the following positive effects: 1) according to the determined sensor arrangement scheme, the coherence between the tested noise signal outside the vehicle and the noise signal inside the vehicle is strong, which indicates that the noise signal tested by the sensor completely reflects the noise generated by the noise source of the vehicle, and provides a reference basis for the noise test scheme of the subsequent vehicle.
Description
Technical Field
The invention relates to the field of analysis and optimization design of an automobile acoustic system, in particular to an in-automobile noise identification method based on conditional power spectrum analysis.
Background
The level of the noise in the automobile is one of the important factors influencing the riding comfort of the automobile, and the noise in the automobile is formed by overlapping a plurality of noise sources on the automobile after the noise sources reach the interior of the automobile through different transmission paths. In order to better detect and solve the noise problem of the automobile, noise sources on various parts of the automobile and transmission paths between the noise sources and an automobile cab need to be comprehensively considered, and factors influencing the noise in the automobile need to be analyzed from the source and transmission path angles respectively. In practical engineering practice, a transmission path analysis method is often adopted, excitation of a noise source is obtained through test testing of a sound pressure signal and a transmission path of the noise source according to an inverse matrix method, based on an established transmission path analysis model, the excitation of the noise source is multiplied by a transmission function in a frequency domain to obtain energy contribution of each noise source to noise in a vehicle, and then the energy contributions of different noise sources are superposed to obtain distribution of noise energy in the vehicle in the frequency domain.
The correctness of the proposed model (a vibration transmission path testing method for eliminating crosstalk) is verified by comparing the calculated in-vehicle noise energy with the tested in-vehicle noise energy, and the higher the calculated value of the in-vehicle noise energy is closer to the tested value, the higher the precision of the model is, and the higher the precision of the model has important significance for analyzing the acoustic characteristics and the improvement of the acoustic performance of the automobile.
To improve the accuracy of the model, the interference between the noise signals must first be eliminated. Because strong coherence exists between the test signals and the influence on the calculation result is large, the signals need to be subjected to coherent removal processing before calculation. The conventional method for eliminating signal coherence is a partial coherence analysis method, a conditional power spectrum of a signal is obtained through calculation by the partial coherence analysis method, and then the conditional power spectrum of the signal is brought into a model to identify noise in a vehicle.
Disclosure of Invention
The invention considers the main parts of the automobile generating noise, regards the parts as the noise sources on the automobile, regards the noise at the right ear of the driver in the automobile as the response caused by the excitation of the noise source outside the automobile, and establishes the acoustic Transmission Path Analysis (TPA) model of the automobile based on the noise excitation of the noise source and the response of the noise in the automobile. And identifying the excitation of the noise source by adopting an inverse matrix method according to the sound pressure response and the near-field acoustic transfer function of the noise source obtained in the test. According to the excitation of the noise source and the far-field acoustic transfer function, the energy distribution of the noise in the vehicle can be calculated by bringing the noise source into the TPA model, and the recognition of the noise in the vehicle is realized. When the excitation of a noise source is calculated, in order to reduce the calculation error caused by crosstalk in a test signal, the conditional power spectrum of a working condition signal is calculated by adopting partial coherent analysis to identify the noise in the vehicle. Through comparison with a test value of noise in the vehicle, the error between the recognition result and the test value is small, which shows that the precision of the adopted model is high, and the analysis and the improvement of the acoustic system of the vehicle in the later period are facilitated.
The invention is realized by at least one of the following technical schemes.
A method for recognizing noise in a vehicle based on conditional power spectrum analysis comprises the following steps:
(1) determining a main noise source of the vehicle in the running process;
(2) establishing a vehicle acoustic transmission path model;
(3) arranging microphones around the radiation surfaces of a plurality of sound sources to obtain the volume acceleration of the noise sources;
(5) selecting running conditions which can represent the noise inside and outside the automobile to be tested from constant speed running, full-accelerator acceleration running and engine idling running;
(6) testing a noise signal of the vehicle;
(7) testing and calculating a transfer function by adopting a sound source substitution method;
(8) calculating the conditional power spectrums of different noise source signals by an iterative method:
(9) and inputting a noise source signal into the vehicle acoustic transmission path model, obtaining the self-power spectrum calculated value of the in-vehicle noise signal under different working conditions, and regarding the self-power spectrum calculated value of the in-vehicle noise signal as the recognition result of the in-vehicle noise.
Preferably, the model in step (2) is a TPA (Transfer path analysis, TPA) model:
wherein QiVolumetric acceleration, T, of noise sourceiIs a transfer function of the sound source to the right ear of the driver, PcalCalculating a sound pressure value after the noise generated by all noise sources on the automobile reaches a target point through respective transmission paths; n representsThe number of noise sources.
Preferably, microphones are arranged both near the radiating surface of each noise source and inside the cab, the microphones being fixed with plastic ties or 3M single-sided glue; part of the microphones should be high temperature resistant microphones; the mouth of the microphone is protected by a sponge ball; the microphone is connected to the data acquisition instrument through the patch cord, and the data acquisition instrument is connected to the computer, through the signal analysis software on the computer, realizes record, analysis and calculation to the microphone test signal.
Preferably, the volume acceleration of the noise source is calculated using an inverse matrix method:
Q(f)＝H+(f)P(f)
wherein Q (f) is a volume acceleration matrix of a noise source, H (f) is a transfer function matrix between a sound source and a reference point sound pressure, wherein the + represents the pseudo-inverse of the matrix, and P (f) is a reference point sound pressure matrix;
writing the matrix into an expanded form:
wherein Qi(f) Volumetric acceleration of a noise source i, Hji(f) Is a transfer function between the noise source i and a reference point j, pj(f) Is the sound pressure at reference point j, v is the number of reference points, n represents the number of noise sources, j ∈ v, i ∈ n.
Preferably, the sound pressure signal of the noise source obtained by the test in the step (6) is excited by the noise source by using an inverse matrix method.
Preferably, in step (6), after the sensors are arranged, the vehicle is operated to a selected working condition, sampling frequency and frequency resolution are set on the signal analysis software, the magnitude of the sampling frequency is at least twice of the concerned maximum frequency, the frequency resolution is determined according to test time, when the noise signals outside and inside the vehicle are collected, at least 3 groups of effective data are collected in each working condition, and the measured value of each group of data cannot exceed 3 dB.
Preferably, the point sound source is adopted in the step (7) to replace the real vehicle noise, and a transfer function between the point sound source and the in-vehicle target point is regarded as a transfer function between a single noise source and the in-vehicle target point.
Preferably, the transfer function is calculated using H1Estimation method, H1The calculation formula of the estimation method is as follows:
where X is the input signal, Y is the output signal, SxxFor self-power spectra of input signals, SxyIs a cross power spectrum of the input signal and the output signal; firstly, the cross power spectrum of input and output signals and the self-power spectrum of the input signals are calculated, and then the ratio of the cross power spectrum to the self-power spectrum is calculated, so that the transfer function H between the input and output signals can be obtained.
Preferably, the conditional power spectrum of each noise source in step (8) adopts the set-averaged conditional power spectrum to eliminate the influence of the signal ordering on the calculation result of the conditional power spectrum.
Preferably, in the step (9), a calculated value of noise in the vehicle is obtained by using an energy superposition method, and model accuracy is verified by comparing a relative error between the calculated value and a test value in a frequency domain:
equation (5) load Q for identifying noise source ii(f) Wherein
Is a transfer function matrix, S ', between noise source i and a nearby reference point'i(f) Is a conditional power spectrum matrix, T, of a reference point near a noise source ii(f) Is a transfer function between the noise source i and the target point, Pcal(f) The value is calculated for the sound pressure at the target point and n represents the number of noise sources.
And (4) the arrangement position of the microphone in the step (4) is the position of the reference point. In order to prevent missing important noise sources of vehicles, a group of pretests are firstly carried out after microphones are arranged, coherence calculation is carried out on measured noise signals in the vehicles and noise signals in the vehicles, coherence coefficients of the noise signals outside the vehicles and the noise signals in the vehicles under different frequencies are obtained, the larger the coherence coefficient is, the stronger the coherence is, and the important noise sources of the vehicles are not missed.
The setting of the sampling frequency and the frequency resolution in the step (6) should meet the condition limit of the sampling theorem.
The step (7) of testing the transfer function should avoid the point sound source from directly contacting the ground or the vehicle.
And (4) sequencing the signal arrangement sequence in the step (8) according to the coherence between the signal and the noise in the vehicle, wherein the signal with higher coherence is positioned at the front in the sequence.
Compared with the prior art, the invention has the beneficial effects that:
in the calculation of the excitation of the noise source of the vehicle, the inverse matrix method is suitable for calculating the volume acceleration of the noise source in a anechoic chamber or a semi-anechoic chamber based on tested sound pressure data, and the calculated volume acceleration is the excitation of the noise source, so that a reference basis is provided for the structural improvement of the part.
In the test of the transfer function, a sound source substitution method is adopted to take a point sound source as an assumed sound source, the transfer function between each reference point and the right ear of the driver is obtained, the noise isolation and dissipation capacity of the transfer path is shown, and a reference basis is provided for the improvement of the transfer path. The obtained transfer function between the point sound source and the target point in the vehicle has more accurate test result than the surface radiation sound pressure result of the test object in the traditional method.
In the recognition of the in-vehicle noise, the crosstalk between the vehicle exterior noise signals is considered, in order to reduce the influence of the signal crosstalk on the in-vehicle noise recognition result, the conditional power spectrum of the noise source signal is obtained by adopting a partial coherence analysis method, the accuracy of the model is obviously improved by adopting the in-vehicle noise recognition of the conditional power spectrum, and the adopted calculation method is suitable for the in-vehicle noise recognition with serious signal crosstalk.
Drawings
FIG. 1 is a diagram of a site layout of a microphone of an embodiment;
FIG. 2a is a graph of noise spectra at an engine survey point according to an embodiment;
FIG. 2b is a graph of noise spectra at a tire survey point for an example embodiment;
FIG. 2c is a graph of the noise spectrum at the exhaust pipe survey point;
FIG. 2d is a graph of the noise spectrum at the test point of the muffler of the embodiment;
FIG. 3a is a plot of the transfer function of the engine of the embodiment to the right ear of the driver;
FIG. 3b is a plot of the transfer function of the example tire to the right ear of the driver;
FIG. 3c is a graph of the transfer function of the exhaust pipe to the right ear of the driver according to the embodiment;
FIG. 3d is a graph of the transfer function of the muffler of the embodiment to the right ear of the driver;
FIG. 4 is a flowchart of an algorithm for calculating a conditional spectrum of a signal according to an embodiment;
FIG. 5 is a flowchart of an algorithm for calculating a power spectrum of a signal condition according to an embodiment;
FIG. 6 is a flowchart illustrating the process of noise identification in a vehicle based on conditional power spectrum analysis according to an embodiment;
FIG. 7a is a graph showing the recognition result of the noise in the vehicle under the cruise condition of the embodiment at 40 kmph;
FIG. 7b is a graph showing the recognition result of the noise in the vehicle under the cruise condition of the embodiment at 60 kmph;
FIG. 7c is a graph showing the recognition result of the noise in the vehicle under the cruise condition of the embodiment 80 kmph.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer and clearer, the present invention is further described in detail below by referring to the accompanying drawings and examples.
As shown in fig. 6, a method for recognizing noise in a vehicle based on conditional power spectrum analysis includes the following steps:
(1) determining the main noise source of the vehicle during operation:
before the acoustic performance of the vehicle is evaluated, an experienced vehicle engineer carries out on-site subjective evaluation on the vehicle, and the noise level and the main noise source of the vehicle in different working conditions are evaluated by running the vehicle under the working conditions of idling, cruising, acceleration and deceleration and the like, so that the approximate position of the sound source is determined. As shown in fig. 1, in the present example, after test and evaluation, the engine 1, the tires (2, 3, 4, 5), the exhaust pipe 7, the muffler 6, and the right ear 8 of the driver are selected as main sources of noise in the vehicle;
(2) establishing a vehicle acoustic transmission path model:
after subjective evaluation, a part which has a large contribution to cab noise is regarded as a main noise source on the automobile and is respectively marked as a noise source 1, a noise source 2 and a noise source … n; establishing a Transfer Path Analysis (TPA) model:
in the formula, QiVolumetric acceleration, T, of noise sourceiIs a transfer function of the sound source to the right ear of the driver, PcalCalculating the total sound pressure value of the noise generated by all noise sources on the automobile after reaching the right ear of the driver through respective transmission paths;
in the above formula Qi(f) Load of noise source i, Ti(f) Is a transfer function between the noise source i and the target point, Pcal(f) The value of the sound pressure at the target point is calculated, i ∈ n, and n represents the number of noise sources.
(3) Determining microphone placement
Calculating the volume acceleration Q (f) of the noise source by adopting an inverse matrix method:
Q(f)＝H+(f)P(f)
where h (f) is a transfer function matrix between sound source and reference point sound pressure, p (f) is reference point sound pressure, and where + represents the pseudo-inverse of the matrix, the number of reference points must be 2 times the number of sound sources in order to ensure the reversibility of the matrix. Therefore, based on the above conclusion, the number of the arranged microphones must be 2 times of the number of the noise sources, and for a sound source with a large volume, in order to ensure that the sound pressure of the radiation surface can be completely tested, the sound source can be equivalent to the combination of a plurality of sound sources, then the microphones are arranged near the radiation surfaces of the plurality of sound sources, and the measuring points near the radiation surface of the noise source are taken as reference points;
wherein Qi(f) Volumetric acceleration of a noise source i, Hji(f) Is a transfer function between the noise source i and a reference point j, pj(f) Is the sound pressure at the reference point j, v is the number of reference points, n represents the number of noise sources, j belongs to upsilon, i belongs to n.
(4) Arrangement of microphones
Microphones are placed near the radiating surface of each noise source and within the cab. The microphone is arranged at a relatively fixed position and is fixed well by a plastic binding tape or 3M single-sided adhesive; part of the microphones should be selected from high temperature resistant microphones, so that damage to the microphones caused by overhigh local temperature of the automobile is avoided; the mouth of the microphone is protected by a sponge ball, so that the influence of overlarge local airflow on the test result is prevented. The microphone placement in the driver's cabin should follow the regulations in GB/T18697 and 2002 regarding microphone location. The microphone is connected to the data acquisition instrument through a patch cord, the data acquisition instrument is connected to the computer, and recording, analysis and calculation of the test signal of the microphone are realized through signal analysis software on the computer;
(5) determining the test working condition of the vehicle:
the operating conditions that can represent the noise inside and outside the vehicle to be tested should be selected from three vehicle operating conditions of constant speed driving, full-throttle acceleration driving and engine idling. The working conditions selected in the case are constant-speed cruising working conditions which are respectively 40kmph, 60kmph and 80 kmph;
(6) noise signal of test vehicle:
the method comprises the steps of running a vehicle to a selected working condition according to a specification, setting sampling frequency and frequency resolution on signal analysis software, wherein the sampling frequency is at least twice of the concerned maximum frequency, the frequency resolution is determined according to test time, 1Hz is the best, the vehicle exterior noise signal and the vehicle interior noise signal of the vehicle are collected, at least 3 groups of effective data are collected in each working condition, the measured value of each group of data cannot exceed 3dB, and the sound pressure level of each noise source in each working condition is shown in figures 2a, 2b, 2c and 2 d;
(7) testing and calculating a transfer function:
because the transfer function between the noise source and the right ear of the driver is difficult to directly obtain, a sound source substitution method is adopted, the real noise source of the vehicle is replaced by the point sound source, the point sound source is arranged near the noise source outside the vehicle, the transfer function between the point sound source and the right ear of the driver is tested through the sound production of the point sound source, and the transfer function between the real noise source and the right ear of the driver is approximately obtained. By means of H1And (3) calculating an estimation transfer function:
wherein X is the input signal, Y is the output signal, GxxFor self-power spectra of input signals, GxyIs the cross power spectrum of the input signal and the output signal. Firstly, the cross power spectrum of input and output signals and the self-power spectrum of the input signals are calculated, and then the ratio of the cross power spectrum to the self-power spectrum is calculated, so that the transfer function H between the input and output signals can be obtained. H1The estimation method considers the interference of noise signals to the system, has higher calculation accuracy, and transfer functions between different noise sources and the position of the right ear of the driver are shown in fig. 3a, 3b, 3c and 3 d;
(8) calculating the conditional power spectra of different noise signals by adopting an iterative algorithm:
through the working condition test of the vehicle, n microphones arranged outside the vehicle obtain time domain records x of n signals1(t)，x2(t)，x3(t)，…，xn(t) obtaining a frequency domain record X of the signal after Fourier transform1(f)，X2(f)，X3(f)，…，Xn(f) And calculating the conditional power spectrum of the signal according to the frequency domain record of the signal, wherein the calculation formula comprises the following steps:
Xj.r！＝Xj.(r-1)！-LrjXr.(r-1)！ (2)
in the formula, LrjAs a transfer function between reference point r and reference point j, Srj.(r-1)！Is a conditional cross-power spectrum between the reference point r and the reference point j after removing the influence of the first r-1 reference points, Srr.(r-1)！Is a conditional self-power spectrum, X, of the reference point r after removing the influence of the former r-1 reference pointsj.r！For frequency domain recording of reference point j after removing the influence of the first r reference points on reference point j, Xj.(r-1)！To remove the influence of the first r-1 reference points on the reference point j, the frequency domain record of the reference point j, Xr.(r-1)！In order to remove the influence of the first r-1 reference points on the reference point r, the frequency domain record of the reference point r is carried out, Sij.r！To remove the influence of the first r reference points, the conditional cross-power spectrum between reference point i and reference point j, Sij.(r-1)！To remove the influence of the first r-1 reference points, the conditional cross-power spectrum between reference point i and reference point j, Sir.(r-1)！To removeAfter the influence of the first r-1 reference points, the conditional cross-power spectrum between the reference point i and the reference point r, T is the time domain record of the signal, S'iiThe conditional power spectrum of the signal i after ensemble averaging is obtained, and n represents the number of noise sources;
the iterative calculation process is as follows:
firstly, sequencing and numbering vehicle exterior noise signals;
second, the transfer function L is calculated according to the formula (1)rj；
Thirdly, sequentially calculating the conditional frequency spectrum X of the signal according to the arrangement sequence of the signal according to the formula (2)j.r！Wherein j is the sequence number of the signal, and r < j, the calculation process is shown in FIG. 4;
step four, in the formula (3), i is made to be j, and the conditional self-power spectrum S of the signals is calculated in sequence according to the sequence of the signalsij.r！Wherein j is the sequence number of the signal, and r < j, the calculation process is shown in fig. 5;
fifthly, repeating the calculation processes from the second step to the fourth step after reordering the signals until calculating the conditional power spectrums of the signals in all the arrangement sequences;
step six, substituting the conditional power spectrums of the same signal under different arrangement sequences into a formula (4) to obtain a conditional self-power spectrum under the combined average;
(9) recognizing the noise in the vehicle under different working conditions:
substituting the conditional power spectrum of each signal obtained in step (8) into a formula
In which the excitation Q of the noise source is obtainediThen according to the formula
The self-power spectrum of the in-vehicle noise is calculated, and the calculated value is regarded as the recognition result of the in-vehicle noise, which is shown in fig. 7a, 7b, and 7 c.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (10)
1. A method for recognizing noise in a vehicle based on conditional power spectrum analysis is characterized by comprising the following steps:
(1) determining a main noise source of the vehicle in the running process;
(2) establishing a vehicle acoustic transmission path model;
(3) arranging microphones around the radiation surfaces of a plurality of sound sources to obtain the volume acceleration of the noise sources;
(5) selecting running conditions which can represent the noise inside and outside the automobile to be tested from constant speed running, full-accelerator acceleration running and engine idling running;
(6) testing a noise signal of the vehicle;
(7) testing and calculating a transfer function by adopting a sound source substitution method;
(8) calculating the conditional power spectrums of different noise source signals by an iterative method:
(9) and inputting a noise source signal into the vehicle acoustic transmission path model, obtaining the self-power spectrum calculated value of the in-vehicle noise signal under different working conditions, and regarding the self-power spectrum calculated value of the in-vehicle noise signal as the recognition result of the in-vehicle noise.
2. The method for recognizing noise in a vehicle based on conditional power spectral analysis according to claim 1, wherein: the model in the step (2) is a TPA model:
wherein QiVolumetric acceleration, T, of noise sourceiIs a transfer function of the sound source to the right ear of the driver, PcalCalculating a sound pressure value after the noise generated by all noise sources on the automobile reaches a target point through respective transmission paths; n represents the number of noise sources.
3. The method for recognizing noise in a vehicle based on conditional power spectral analysis according to claim 1, wherein: microphones are arranged near the radiation surface of each noise source and in the cab, and the microphones are fixed by plastic ties or 3M single-sided adhesive; part of the microphones should be high temperature resistant microphones; the mouth of the microphone is protected by a sponge ball; the microphone is connected to the data acquisition instrument through the patch cord, and the data acquisition instrument is connected to the computer, through the signal analysis software on the computer, realizes record, analysis and calculation to the microphone test signal.
4. The method for recognizing noise in a vehicle based on conditional power spectral analysis according to claim 1, wherein: and (3) calculating the volume acceleration of the noise source by adopting an inverse matrix method:
Q(f)＝H+(f)P(f)
wherein Q (f) is a volume acceleration matrix of a noise source, H (f) is a transfer function matrix between a sound source and a reference point sound pressure, wherein the + represents the pseudo-inverse of the matrix, and P (f) is a reference point sound pressure matrix;
writing the matrix into an expanded form:
wherein Qi(f) Volumetric acceleration of a noise source i, Hji(f) Is a transfer function between the noise source i and a reference point j, pj(f) Is the sound pressure at reference point j, v is the number of reference points, n represents the number of noise sources, j ∈ v, i ∈ n.
5. The method for recognizing noise in a vehicle based on conditional power spectral analysis according to claim 1, wherein: and (4) obtaining the excitation of the noise source by the sound pressure signal of the noise source obtained by the test in the step (6) by adopting an inverse matrix method.
6. The method for recognizing noise in a vehicle based on conditional power spectral analysis according to claim 1, wherein: in the step (6), after the sensors are arranged, the vehicle is operated to a selected working condition, sampling frequency and frequency resolution are set on signal analysis software, the size of the sampling frequency is at least twice of the concerned maximum frequency, the frequency resolution is determined according to test time, when the noise signals outside and inside the vehicle are collected, at least 3 groups of effective data are collected in each working condition, and the measured value of each group of data cannot exceed 3 dB.
7. The method for recognizing noise in a vehicle based on conditional power spectral analysis according to claim 1, wherein: in the step (7), the point sound source is adopted to replace the real vehicle noise, and the transfer function between the point sound source and the vehicle interior target point is regarded as the transfer function between the single noise source and the vehicle interior target point.
8. The method for recognizing noise in a vehicle based on conditional power spectral analysis according to claim 7, wherein: the transfer function is calculated by H1Estimation method, H1The calculation formula of the estimation method is as follows:
where X is the input signal, Y is the output signal, SxxFor self-power spectra of input signals, SxyIs a cross power spectrum of the input signal and the output signal; firstly, the cross power spectrum of input and output signals and the self-power spectrum of the input signals are calculated, and then the ratio of the cross power spectrum to the self-power spectrum is calculated, so that the transfer function H between the input and output signals can be obtained.
9. The method for recognizing noise in a vehicle based on conditional power spectral analysis according to claim 1, wherein: and (3) eliminating the influence of the signal sequencing on the calculation result of the conditional power spectrum by adopting the conditional power spectrum after the set averaging in the conditional power spectrum of each noise source in the step (8).
10. The method for recognizing noise in a vehicle based on the conditional power spectral analysis according to any one of claims 1 to 9, comprising: in the step (9), a calculated value of the noise in the vehicle is obtained by adopting an energy superposition method, and the model precision is verified by comparing the relative error of the calculated value and the test value in a frequency domain:
equation (5) load Q for identifying noise source ii(f) Wherein
Is a transfer function matrix, S ', between noise source i and a nearby reference point'i(f) Is a conditional power spectrum matrix, T, of a reference point near a noise source ii(f) Is a transfer function between the noise source i and the target point, Pcal(f) The value is calculated for the sound pressure at the target point and n represents the number of noise sources.
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