CN114383716B - 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 of: determining a dominant noise source of the vehicle during operation; establishing a vehicle acoustic transfer path model; determining an arrangement 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 according to the transmission path analysis model to obtain the frequency spectrum distribution of the noise energy in the vehicle. 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, so that the noise signal tested by the sensor completely reflects the noise generated by the noise source of the vehicle, and a reference basis is provided for the noise test scheme of the subsequent vehicle.
Description
Technical Field
The invention relates to the field of analysis and optimal design of an automobile acoustic system, in particular to an in-automobile noise identification method based on conditional power spectrum analysis.
Background
The noise level in the automobile is one of 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 reaching the inside of the automobile through different transmission paths. In order to better detect and solve the noise problem of the automobile, the noise sources at all parts of the automobile and the transmission paths between the noise sources and the cab in the automobile need to be comprehensively considered, and factors influencing the noise in the automobile are analyzed from the angles of the sources and the transmission paths respectively. In practical engineering practice, a transmission path analysis method is often adopted, noise source sound pressure signals and transmission paths are tested through experiments, excitation of noise sources is obtained through calculation according to an inverse matrix method, then energy contribution of each noise source to noise in a vehicle is obtained through multiplying the excitation of the noise sources and a transmission function in a frequency domain based on an established transmission path analysis model, and then energy contributions of different noise sources are overlapped 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 when the calculated value and the tested value of the in-vehicle noise energy are closer, the higher the model accuracy is, and the higher model accuracy has important significance for analyzing the acoustic characteristics and the acoustic performance improvement of the automobile.
To improve the accuracy of the model, first the interference between the noise signals must be eliminated. Because stronger coherence exists between test signals, the influence on the calculation result is larger, and therefore, the signals are subjected to decoherence treatment before calculation. The current common method for eliminating the signal coherence is a partial coherence analysis method, a conditional power spectrum of the signal is obtained through calculation by the partial coherence analysis method, and then the conditional power spectrum of the signal is put into a model to identify the noise in the vehicle.
Disclosure of Invention
The invention considers the main parts of the automobile for generating noise, regards the parts as 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 sources outside the automobile, and establishes an acoustic Transmission Path Analysis (TPA) model of the automobile based on the noise excitation of the noise sources and the noise response in the automobile. And identifying excitation of the noise source by adopting an inverse matrix method according to the sound pressure response of the noise source and the near-field acoustic transfer function obtained in the test. According to the excitation of the noise source and the far-field acoustic transfer function, the noise source is brought into the TPA model, so that the energy distribution of the noise in the vehicle can be calculated, and the recognition of the noise in the vehicle is realized. When the excitation of the noise source is calculated, in order to reduce calculation errors caused by crosstalk in the test signal, partial coherence analysis is adopted to calculate a condition power spectrum of the working condition signal so as to identify noise in a vehicle. By comparing the model with the test value of the noise in the vehicle, the error between the identification result and the test value is smaller, which indicates that the adopted model has higher precision and is beneficial to the analysis and improvement of the acoustic system of the vehicle in the later period.
The invention is realized at least by one of the following technical schemes.
An in-vehicle noise identification method based on conditional power spectrum analysis comprises the following steps:
(1) Determining a dominant noise source of the vehicle during operation;
(2) Establishing a vehicle acoustic transfer path model;
(3) Microphones are arranged around the radiation surfaces of a plurality of sound sources, and the volume acceleration of the noise sources is obtained;
(5) Selecting running conditions capable of representing noise inside and outside the automobile to be tested from constant speed running, full accelerator accelerating 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 iteration method:
(9) And inputting the noise source signal into a vehicle acoustic transfer path model to obtain a self-power spectrum calculated value of the noise signal in the vehicle under different working conditions, and regarding the self-power spectrum calculated value of the noise signal in the vehicle as an identification result of the noise in the vehicle.
Preferably, the model in step (2) is a TPA (Transfer path analysis, TPA) model:
wherein Q is i Is the volume acceleration of the noise source, T i As a transfer function of sound source to the right ear of driver, P cal Sound pressure calculation values after noise generated by all noise sources on the automobile reaches a target point through respective transmission paths; n represents the number of noise sources.
Preferably, 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 adhesives; part of microphones should be selected as high temperature resistant microphones; the microphone port 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, and through the signal analysis software on the computer, the record, analysis and calculation to microphone test signal are realized.
Preferably, the volumetric 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 the noise source, H (f) is a transfer function matrix between the sound source and the sound pressure of the reference point, wherein +represents the pseudo inverse of the matrix, and P (f) is the sound pressure matrix of the reference point;
writing the matrix into an expanded form:
wherein Q is i (f) For the volumetric acceleration of noise source i, H ji (f) Is the transfer function between the noise source i and the reference point j, p j (f) For sound pressure at reference point j, v is the number of reference points, n represents the number of noise sources, j e v, i e n.
Preferably, the noise source sound pressure signal obtained by testing in the step (6) can be excited by a noise source by adopting an inverse matrix method.
Preferably, in step (6), after the sensor is arranged, the vehicle is operated to a selected working condition, sampling frequency and frequency resolution are set on the signal analysis software, the size of the sampling frequency is at least twice as large as the maximum frequency concerned, the frequency resolution is determined according to the test time, at least 3 groups of effective data are collected in each working condition when the noise signal outside the vehicle and the noise signal inside the vehicle are collected, and the measured value of each group of data cannot exceed 3dB.
Preferably, in the step (7), the point sound source is used to replace the real vehicle noise, and the transfer function between the point sound source and the target point in the vehicle is regarded as the transfer function between the single noise source and the target point in the vehicle.
Preferably, the transfer function is calculated using H 1 Estimation method, H 1 The calculation formula of the estimation method is as follows:
wherein X is an input signal, Y is an output signal, S xx For inputting informationNumber self-power spectrum, S xy A cross power spectrum for the input signal and the output signal; firstly, calculating the cross power spectrum of the input signal and the output signal and the self power spectrum of the input signal, and then calculating the ratio of the cross power spectrum to the self power spectrum to obtain the transfer function H between the input signal and the output signal.
Preferably, the conditional power spectrum of each noise source in the step (8) uses the condition power spectrum after the aggregate average to eliminate the influence of the signal ordering on the calculation result of the condition power spectrum.
Preferably, in the step (9), an energy superposition method is adopted to obtain a calculated value of the noise in the vehicle, and the model accuracy is verified by comparing the relative error between the calculated value and the test value in the frequency domain:
equation (5) load Q for identifying noise source i i (f) WhereinIs a transfer function matrix between the noise source i and the nearby reference point, S' i (f) A conditional power spectrum matrix for reference points near the noise source i, T i (f) For the transfer function between the noise source i and the target point, P cal (f) For the sound pressure calculation value of the target point, n represents the number of noise sources.
And (3) 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 a vehicle, a group of pre-tests are performed after microphones are arranged, and coherence computation is performed on measured noise signals in the vehicle and noise signals in the vehicle to obtain coherence coefficients of noise signals outside the vehicle and noise signals in the vehicle under different frequencies, wherein the larger the coherence coefficient is, the stronger the coherence is, and the important noise sources of the vehicle are not missed.
The setting of the sampling frequency and the frequency resolution in the step (6) should conform to the condition limit of the sampling theorem.
The test of the transfer function of the step (7) should avoid the point sound source from directly contacting the ground or the vehicle.
The arrangement sequence of the signals in the step (8) is ordered according to the coherence of the signals and noise in the vehicle, and the signals with larger coherence are more forward 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 based on the tested sound pressure data in the anechoic chamber or the semi-anechoic chamber, and the calculated volume acceleration is the excitation of the noise source and provides a reference basis for the structural improvement of the part.
In the test of the transfer function, a point sound source is used as an assumed sound source by adopting a sound source substitution method, so that the transfer function between each reference point and the right ear of the driver is obtained, the isolation and dissipation capacity of the transfer path to noise 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 object tested by the traditional method.
In the identification of the noise in the vehicle, the crosstalk between noise signals outside the vehicle is considered, in order to reduce the influence of the signal crosstalk on the noise identification result in the vehicle, a partial coherence analysis method is adopted to obtain a conditional power spectrum of the noise source signal, the in-vehicle noise identification performed by adopting the conditional power spectrum obviously improves the accuracy of a model, and the adopted calculation method is suitable for the in-vehicle noise identification with serious signal crosstalk.
Drawings
FIG. 1 is a station layout of an embodiment microphone;
FIG. 2a is a graph of the noise spectrum at an embodiment engine test point;
FIG. 2b is a graph of the noise spectrum at a tire measurement point of an example embodiment;
FIG. 2c is a graph of the noise spectrum at the exhaust pipe measurement point;
FIG. 2d is a graph of the noise spectrum at the muffler measurement point of the embodiment;
FIG. 3a is a graph of the transfer function of an example engine with the right ear of a driver;
FIG. 3b is a graph of the transfer function of an example tire and the right ear of a driver;
FIG. 3c is a graph of the transfer function of the exhaust pipe and the right ear of the driver according to the embodiment;
FIG. 3d is a graph of the transfer function of an embodiment muffler and 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 signal condition power spectrum according to an embodiment;
FIG. 6 is a flow chart of in-vehicle noise identification based on conditional power spectrum analysis according to an embodiment;
FIG. 7a is a graph of the result of in-vehicle noise identification under an example 40kmph cruise condition;
FIG. 7b is a graph of the result of in-vehicle noise identification under example 60kmph cruise conditions;
FIG. 7c is a graph of the result of in-vehicle noise identification under an example 80kmph cruise condition.
Detailed Description
The present invention will be described in further detail below with reference to the accompanying drawings and examples in order to make the objects, technical solutions and advantages of the present invention more clear and obvious.
As shown in fig. 6, an in-vehicle noise recognition method based on conditional power spectrum analysis includes the following steps:
(1) Determining a dominant noise source during operation of the vehicle:
before acoustic performance evaluation is performed on a vehicle, a vehicle engineer with rich experience performs on-site subjective evaluation on the vehicle, and the approximate position of a sound source is determined by evaluating the noise level and main noise sources in the vehicle under different working conditions by running the vehicle under the working conditions of idling, cruising, acceleration and deceleration and the like. As shown in fig. 1, in this case, after testing and evaluation, an engine 1, tires (2, 3, 4, 5), an exhaust pipe 7, a muffler 6 and a right ear 8 of a driver are selected as main sources of noise in a vehicle;
(2) Establishing a vehicle acoustic transfer path model:
after subjective evaluation, the parts with larger contribution to the noise of the cab are regarded as main noise sources on the automobile and are respectively marked as a noise source 1, a noise source 2 and a … noise source n; modeling transfer path analysis (Transfer path analysis, TPA):
in which Q i Is the volume acceleration of the noise source, T i As a transfer function of sound source to the right ear of driver, P cal The total sound pressure calculation value after the noise generated by all noise sources on the automobile reaches the right ear of the driver through the respective transmission paths;
q in the above formula i (f) Is the load of noise source i, T i (f) For the transfer function between the noise source i and the target point, P cal (f) For the sound pressure calculation value of the target point, i e n, n represents the number of noise sources.
(3) Determining arrangement of microphones
The volume acceleration Q (f) of the noise source is calculated by adopting an inverse matrix method:
Q(f)＝H + (f)P(f)
wherein H (f) is a transfer function matrix between sound sources and sound pressure of reference points, P (f) is sound pressure of reference points, wherein +represents pseudo-inverse of the matrix, and the number of the reference points is 2 times that of the sound sources in order to ensure reversibility of the matrix. Therefore, the number of the microphones is 2 times of the number of the noise sources based on the conclusion, and for sound sources with larger volumes, in order to ensure that the sound pressure of the radiation surface can be completely tested, the sound sources 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 surfaces of the noise sources are regarded as reference points;
wherein Q is i (f) For the volumetric acceleration of noise source i, H ji (f) Is the transfer function between the noise source i and the reference point j, p j (f) For sound pressure at reference point j, v is the number of reference points, n represents the number of noise sources, j ε v, i ε n.
(4) Arrangement microphone
Microphones are disposed near the radiation surface of each noise source and in the cab. The microphone should be installed at a relatively fixed position, and well fixed by a plastic ribbon or 3M single-sided adhesive tape; the high-temperature resistant microphones are selected for part of microphones, so that damage to the microphones caused by the fact that the local temperature of an automobile is too high is avoided; the microphone port is protected by a sponge ball, so that the influence of local excessive air flow on the test result is prevented. The microphone arrangement in the cab should follow the specifications in GB/T18697-2002 regarding the position of the microphones. The microphone is connected to the data acquisition instrument through the patch cord, the data acquisition instrument is connected to the computer, and the recording, analysis and calculation of the microphone test signals are realized through signal analysis software on the computer;
(5) Determining a test condition of the vehicle:
the running conditions which can represent the noise inside and outside the automobile to be tested are selected from the three running conditions of constant speed running, full accelerator accelerating running and idling running of the engine. The working conditions selected in the scheme are all constant-speed cruising working conditions, namely 40 kmh, 60 kmh and 80 kmh respectively;
(6) Testing noise signals of a vehicle:
according to the specification, running the vehicle to a selected working condition, setting a sampling frequency and a frequency resolution on signal analysis software, wherein the size of the sampling frequency is at least twice as large as the maximum frequency concerned, the frequency resolution is determined according to the test time, taking 1Hz as the best, starting to collect an off-board noise signal and an on-board noise signal of the vehicle, at least collecting 3 groups of effective data in each working condition, and 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 transfer functions:
because the transfer function of the noise source and the right ear of the driver is difficult to directly obtain, a sound source substitution method is adopted, a point sound source is utilized to replace a real noise source of the vehicle, the point sound source is arranged near the noise source outside the vehicle, the transfer function of 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 of the real noise source and the right ear of the driver is approximately obtained. By H 1 And (3) calculating transfer functions by an estimation method:
wherein X is an input signal, Y is an output signal, G xx G is the self-power spectrum of the input signal xy Is the cross-power spectrum of the input signal and the output signal. Firstly, calculating the cross power spectrum of the input signal and the output signal and the self power spectrum of the input signal, and then calculating the ratio of the cross power spectrum to the self power spectrum to obtain the transfer function H between the input signal and the output signal. H 1 The estimation method considers the interference of noise signals to the system, has higher calculation accuracy, and the transfer functions between different noise sources and the right ear position of the driver are shown in fig. 3a, 3b, 3c and 3 d;
(8) Calculating the conditional power spectrums of different noise signals by adopting an iterative algorithm:
through the working condition test of the vehicle, n microphones arranged outside the vehicle obtain the time domain record x of n signals 1 (t)，x 2 (t)，x 3 (t)，…，x n (t) obtaining a frequency domain record X of the signal after Fourier transformation 1 (f)，X 2 (f)，X 3 (f)，…，X n (f) According to the frequency domain record of the signal, calculating the conditional power spectrum of the signal, wherein the calculation formula comprises:
X j.r！ ＝X j.(r-1)！ -L rj X r.(r-1)！ (2)
wherein L is rj As a transfer function between reference point r and reference point j, S rj.(r-1)！ Is the conditional cross power spectrum between the reference point r and the reference point j after the influence of the previous r-1 reference points is removed, S rr.(r-1)！ For the reference point r, the conditional self-power spectrum after the influence of the previous r-1 reference points is removed, X j.r！ To remove the influence of the first r reference points on the reference point j, the frequency domain record of the reference point j is recorded, X j.(r-1)！ To remove the influence of the previous r-1 reference points on the reference point j, the frequency domain record of the reference point j is recorded, X r.(r-1)！ To remove the influence of the previous r-1 reference points on the reference point r, the frequency domain record of the reference point r is recorded, S ij.r！ To remove the influence of the first r reference points, the conditional cross-power spectrum between reference point i and reference point j, S ij.(r-1)！ To remove the influence of the previous r-1 reference points, the conditional cross-power spectrum between reference point i and reference point j, S ir.(r-1)！ In order to remove the influence of the previous r-1 reference points, the conditional cross-power spectrum between the reference point i and the reference point r is that T is the time domain record of the signal, S' ii For the conditional power spectrum of the signal i after the aggregate average, n represents the number of noise sources;
the iterative calculation process is as follows:
the method comprises the steps of firstly, sequencing and numbering noise signals outside a vehicle;
second, calculating a transfer function L according to the formula (1) rj ；
Step three, according to the formula (2), the conditional frequency spectrum X of the signals is calculated according to the arrangement sequence of the signals j.r！ Where j is the sequence number of the signal, and r < j, the calculation process is shown in FIG. 4;
fourth, let i=j in formula (3), sequentially calculate according to the arrangement order of the signalsConditional self-power spectrum S of signal ij.r！ Where j is the sequence number of the signal, and r < j, the calculation process is shown in FIG. 5;
fifth, repeating the calculation process from the second step to the fourth step after re-sequencing the signals until the conditional power spectrum of the signals under all the arrangement sequences is calculated;
step six, entering the conditional power spectral bands of the same signal under different arrangement sequences into a formula (4) to obtain a conditional self-power spectrum under average combination;
(9) And identifying noise in the vehicle under different working conditions:
inputting the conditional power spectral band of each signal obtained in step (8) into a formulaExcitation Q of the resulting noise source i Then according to formula->The self-power spectrum of the noise in the vehicle is calculated, and the calculated value is regarded as the recognition result of the noise in the vehicle, and the recognition result is shown in fig. 7a, 7b and 7 c.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not exhaustive or to limit the invention to the precise form 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 understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (4)
1. The in-vehicle noise identification method based on the conditional power spectrum analysis is characterized by comprising the following steps of:
(1) Determining a dominant noise source of the vehicle during operation;
(2) Establishing a vehicle acoustic transfer path model, wherein the model is a TPA model:
wherein Q is i Is the volume acceleration of the noise source, T i As a transfer function of sound source to the right ear of driver, P cal Sound pressure calculation values after 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) Microphones are arranged around the radiation surfaces of a plurality of sound sources, and the volume acceleration of the noise sources is obtained; 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 the noise source, H (f) is a transfer function matrix between the sound source and the sound pressure of the reference point, wherein +represents the pseudo inverse of the matrix, and P (f) is the sound pressure matrix of the reference point;
writing the matrix into an expanded form:
wherein Q is i (f) For the volumetric acceleration of noise source i, H ji (f) Is the transfer function between the noise source i and the reference point j, p j (f) For sound pressure at reference point j, v is the number of reference points, n represents the number of noise sources, j e v, i e n;
(5) Selecting running conditions capable of representing noise inside and outside the automobile to be tested from constant speed running, full accelerator accelerating 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; the point sound source is adopted to replace real vehicle noise, the transfer function between the point sound source and the target point in the vehicle is regarded as the transfer function between a single noise source and the target point in the vehicle, and the transfer function is calculated by adopting H 1 Estimation method, H 1 The calculation formula of the estimation method is as follows:
wherein X is an input signal, Y is an output signal, S xx For the self-power spectrum of the input signal S xy A cross power spectrum for the input signal and the output signal; firstly, calculating a cross power spectrum of an input signal and an output signal and a self power spectrum of the input signal, and then calculating the ratio of the cross power spectrum to the self power spectrum to obtain a transfer function H between the input signal and the output signal;
(8) The method comprises the steps of calculating the conditional power spectrums of different noise source signals through an iteration method, wherein the conditional power spectrums of all noise sources adopt the conditional power spectrums after the collection and the average to eliminate the influence of signal sequencing on the calculation result of the conditional power spectrums: through the working condition test of the vehicle, n microphones arranged outside the vehicle obtain the time domain record x of n signals 1 (t)，x 2 (t)，x 3 (t)，…，x n (t) obtaining a frequency domain record X of the signal after Fourier transformation 1 (f)，X 2 (f)，X 3 (f)，…，X n (f) According to the frequency domain record of the signal, calculating the conditional power spectrum of the signal, wherein the calculation formula comprises:
X j.r！ ＝X j.(r-1)！ -l rj X r.(r-1)！ (2)
wherein L is rj For referenceTransfer function between point r and reference point j, S rj.(r-1)！ Is the conditional cross power spectrum between the reference point r and the reference point j after the influence of the previous r-1 reference points is removed, S rr.(r-1)！ For the reference point r, the conditional self-power spectrum after the influence of the previous r-1 reference points is removed, X j.r！ To remove the influence of the first r reference points on the reference point j, the frequency domain record of the reference point j is recorded, X j.(r-1)！ To remove the influence of the previous r-1 reference points on the reference point j, the frequency domain record of the reference point j is recorded, X r.(r-1)！ To remove the influence of the previous r-1 reference points on the reference point r, the frequency domain record of the reference point r is recorded, S ij.r！ To remove the influence of the first r reference points, the conditional cross-power spectrum between reference point i and reference point j, S ij.(r-1)！ To remove the influence of the previous r-1 reference points, the conditional cross-power spectrum between reference point i and reference point j, S ir.(r-1)！ In order to remove the influence of the previous r-1 reference points, the conditional cross-power spectrum between the reference point i and the reference point r is that T is the time domain record of the signal, S' ii For the conditional power spectrum of the signal i after the aggregate average, n represents the number of noise sources;
the iterative calculation process is as follows:
the method comprises the steps of firstly, sequencing and numbering noise signals outside a vehicle;
second, calculating a transfer function L according to the formula (1) rj ；
Step three, according to the formula (2), the conditional frequency spectrum X of the signals is calculated according to the arrangement sequence of the signals j.r！ Wherein j is the arrangement sequence number of the signals, and r is less than j;
fourth, let i=j in formula (3), sequentially calculate the conditional self-power spectrum S of the signal according to the arrangement order of the signal ij.r！ Wherein j is the arrangement sequence number of the signals, and r is less than j;
fifth, repeating the calculation process from the second step to the fourth step after re-sequencing the signals until the conditional power spectrum of the signals under all the arrangement sequences is calculated;
step six, entering the conditional power spectral bands of the same signal under different arrangement sequences into a formula (4) to obtain a conditional self-power spectrum under average combination;
(9) Inputting a noise source signal into a vehicle acoustic transfer path model to obtain a self-power spectrum calculated value of the noise signal in the vehicle under different working conditions, regarding the self-power spectrum calculated value of the noise signal in the vehicle as a recognition result of the noise in the vehicle, obtaining a calculated value of the noise in the vehicle by adopting an energy superposition method, and verifying model accuracy by comparing relative errors of the calculated value and a test value in a frequency domain:
equation (5) load Q for identifying noise source i i (f) WhereinIs a transfer function matrix between the noise source i and the nearby reference point, S' i (f) A conditional power spectrum matrix for reference points near the noise source i, T i (f) For the transfer function between the noise source i and the target point, P cal (f) For the sound pressure calculation value of the target point, n represents the number of noise sources.
2. The method for identifying noise in a vehicle based on conditional power spectrum analysis according to claim 1, wherein: microphones are distributed near the radiation surface of each noise source and in a cab, and are fixed by plastic strapping or 3M single-sided adhesive tape; part of microphones should be selected as high temperature resistant microphones; the microphone port 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, and through the signal analysis software on the computer, the record, analysis and calculation to microphone test signal are realized.
3. The method for identifying noise in a vehicle based on conditional power spectrum analysis according to claim 1, wherein: and (3) the sound pressure signal of the noise source obtained by the test in the step (6) can be excited by the noise source by adopting an inverse matrix method.
4. The method for identifying noise in a vehicle based on conditional power spectrum analysis according to claim 1, wherein: in the step (6), after the arrangement of the sensors is finished, the vehicle is operated to a selected working condition, sampling frequency and frequency resolution are set on the signal analysis software, the sampling frequency is at least twice as high as the maximum frequency concerned, the frequency resolution is determined according to the test time, at least 3 groups of effective data are collected in each working condition when the noise signals outside the vehicle and the noise signals inside the vehicle are collected, and the measured value of each group of data cannot exceed 3dB.
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