Gear running-in quality evaluation and abnormal sound positioning method and device based on acoustic-vibration coupling

Abstract

The application discloses a gear running-in quality evaluation and abnormal sound positioning method and device based on sound-vibration coupling, and belongs to the technical field of gear transmission assembly. The application comprises the following steps: obtaining vibration acceleration and noise signals in the running-in process of a radar transmission system, calculating 1 / 3 octave spectrum of gear structure noise and air noise, vibration acceleration level and noise sound pressure level; judging the gear running-in state by calculating Mahalanobis distance of vibration acceleration level and noise level characteristic samples; if the gear running-in condition is poor, selecting a frequency band with the minimum difference between vibration 1 / 3 octave and noise 1 / 3 octave, and carrying out band-pass filtering and envelope demodulation on the signals; and obtaining gear abnormal sound fault identification results through preset expert experience values. The application can analyze abnormal vibration and structure noise generated in the gear running-in process and position gear faults.

Description

Technical Field

[0001] This invention belongs to the field of gear transmission assembly technology, specifically relating to a method and equipment for evaluating gear running-in quality and locating abnormal noises based on acoustic-vibration coupling. Background Technology

[0002] Traditional gear break-in pre-alarm strategies typically use only a fixed threshold method to compare vibration signal characteristic values ​​and analyze the current gear break-in quality. If only one type of sensor data is used for judgment during the break-in process, misjudgments of the break-in quality may occur. Therefore, other sensors can be used to assist in judging the break-in quality.

[0003] For gear fault diagnosis, a single vibration signal is often used to analyze the operating status of gear equipment. However, gear fault signals are often drowned out by strong vibration noise generated by other mechanical structures, making gear equipment condition detection quite difficult. Furthermore, the vibration signals generated by gear faults are quite complex, often accompanied by signal modulation phenomena, further complicating fault diagnosis. Therefore, it is necessary to establish a method for locating gear faults using different types of sensors. Summary of the Invention

[0004] The purpose of this invention is to provide a method and device for evaluating gear running-in quality and locating abnormal noise based on acoustic-vibration coupling, which can analyze abnormal vibrations and structural noise generated during gear running-in and locate gear faults.

[0005] Specifically, on the one hand, the present invention provides a method for evaluating gear running-in quality and locating abnormal noises based on acoustic-vibration coupling, including:

[0006] The vibration acceleration and noise signals of the radar transmission system during the running-in process are obtained, and the 1 / 3 octave band spectrum, vibration acceleration level, and noise sound pressure level of the gear structure noise and air noise are calculated.

[0007] The gear running-in status is determined by calculating the Mahalanobis distance between the vibration acceleration level and noise level feature samples. If the gear running-in status is poor, the frequency band with the smallest difference between the vibration 1 / 3 octave band and the noise 1 / 3 octave band is selected, and the signal is bandpass filtered and envelope demodulated. The gear abnormal noise fault identification result is obtained through preset expert experience values.

[0008] Furthermore, the calculation of the 1 / 3 octave band spectrum, vibration acceleration level, and noise sound pressure level of the gear structure noise and air noise includes:

[0009] 1-1) Divide the spectrum obtained by discrete Fourier transform of the vibration acceleration level into frequency bands according to the 1 / 3 octave band rule, and calculate the vibration acceleration level in the frequency band corresponding to each center frequency, in dB. See the following formula.

[0010]

[0011] Where i is the 1 / 3 octave band sequence number; X m This represents the effective value of the vibrational acceleration of the m-th spectral line within the corresponding frequency band, in m / s². 2 M represents the number of sequence points within the corresponding frequency band; P ref The reference vibration acceleration;

[0012] 1-2) Superimpose the vibration acceleration levels in each frequency band to obtain the total vibration acceleration level in the desired frequency band, in dB, as shown in the following formula.

[0013]

[0014] Where K is the number of points in the 1 / 3 octave band sequence;

[0015] 1-3) Divide the sound signal's sound pressure spectrum peak value into frequency bands according to the 1 / 3 octave band rule, using the discrete Fourier transform. Calculate the noise sound pressure level within each center frequency band, in dB, as shown in the following formula.

[0016]

[0017] In the formula, i is the 1 / 3 octave band sequence number; P m P represents the effective sound intensity of each spectral line within the corresponding frequency band, in Pa; M represents the number of sequence points within the corresponding frequency band; P ref As the reference sound intensity;

[0018] 1-4) Superimpose the peak values ​​of the noise spectrum in each frequency band to obtain the full-band noise sound pressure level in the desired frequency band, in dB, as shown in the following formula.

[0019]

[0020] SPL i SPL is the sound pressure level of the i-th frequency band. Ai This is the weighted correction value for the i-th frequency band A.

[0021] Furthermore, the step of determining the gear running-in state by calculating the Mahalanobis distance of the vibration acceleration level and noise level feature samples includes:

[0022] 2-1) Calculate the Mahalanobis distance of the vibration acceleration level and noise level characteristic samples. The Mahalanobis distance M is calculated from each set of data samples collected. i Plot the Mahalanobis distance curve M, and add a Mahalanobis distance M each time. i Taking the first derivative of (a point on the Mahalanobis distance curve M), we get... If the obtained derivative Greater than the set threshold for the derivative of the Mahalanobis distance curve Right now This indicates a problem with gear meshing, and poor gear running-in. The formula for calculating the Mahalanobis distance M is shown below.

[0023]

[0024] Where N is the dimension of the initial sample matrix, and the initial sample has a dimension of N and two rows, as shown in the following formula.

[0025]

[0026] The vibration level and noise level calculated from each set of collected data samples are used as a feature array. To calculate the covariance matrix, see the following formula.

[0027]

[0028] The Mahalanobis distance M can be calculated from each dimension of the covariance matrix. i .

[0029] Furthermore, if the gear running-in condition is poor, the frequency band with the smallest difference between the vibration 1 / 3 octave band and the noise 1 / 3 octave band is selected, and the signal is bandpass filtered and envelope demodulated. The gear noise fault identification result obtained through preset expert experience values ​​includes:

[0030] If the gear running-in condition is determined by calculating the Mahalanobis distance of the vibration acceleration level and noise level characteristic samples, and the conclusion is that the gear running-in condition is poor, then the minimum difference d of each frequency band of the vibration and noise 1 / 3 octave band is selected, and the frequency band i corresponding to d is selected. The upper and lower limits of the frequency band i corresponding to d are used as the upper and lower cutoff frequencies of the bandpass filter. The data is subjected to envelope analysis, and the current gear running state is obtained through the spectrum envelope diagram. The fault location result of the gear abnormal noise is obtained by analyzing the fault characteristic frequency.

[0031] Using formula (8), select the frequency band i corresponding to d.

[0032] d=min(|L i -SPL i |) (8)

[0033] Among them, L i The vibration acceleration level of the i-th frequency band in 1 / 3 octave band is calculated using formula (1); SPL i The sound pressure level of the i-th frequency band in 1 / 3 octave band is calculated using formula (3).

[0034] The upper and lower limits of the frequency band i corresponding to d are used as the upper and lower cutoff frequencies of the bandpass filter (f). m -f0,f m +f0), the bandpass filter is expressed as:

[0035]

[0036] Furthermore, the gear running-in quality assessment and abnormal noise location method based on acoustic-vibration coupling also includes:

[0037] Based on the calculated acceleration level, sound pressure level, and other vibration parameters, a characteristic vector Z for acoustic-vibration coupling is established.

[0038] The acoustic-vibration coupling feature vector Z is compared with the feature threshold vector γ to obtain their respective scores, and finally the current gear running-in quality assessment result is obtained. The feature threshold vector γ is obtained by statistical calculation of historical data in the feature database.

[0039] Furthermore, the remaining vibration indicators include end face runout, radial runout, vibration intensity, and MPR index.

[0040] Further, comparing the acoustic-vibration coupling feature vector Z with the feature threshold vector γ includes:

[0041] The feature vector Z is compared with the threshold vector γ and the threshold vector γ*par respectively to obtain the evaluation vector S. Based on the different comparisons between each element Z(i) of the acoustic-vibration coupling feature vector Z and the threshold γ(i) and par(i)*γ(i), a different value is assigned to a certain element S(i) of the evaluation vector S.

[0042] The vector par represents the warning coefficient, with a value range of (0, 1). When the value is 1, it means that the value of the calculated feature exceeds the threshold * par but is less than the threshold, and the run-in quality is considered poor. The corresponding feature vector is then assigned a value of 1. The final run-in score is obtained by summing the dot product of the evaluation vector S and the feature weight ω. The lower the score, the worse the run-in quality.

[0043] On the other hand, the present invention also provides a gear running-in quality assessment and abnormal noise location device based on acoustic-vibration coupling. The device includes a memory and a processor. The memory stores a computer program for implementing the gear running-in quality assessment and abnormal noise location method based on acoustic-vibration coupling. The processor executes the computer program to implement the steps of the above method.

[0044] In another aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method.

[0045] The beneficial effects of the gear running-in quality assessment and abnormal noise location method and equipment based on acoustic-vibration coupling of the present invention are as follows:

[0046] The present invention relates to a method and device for gear running-in quality assessment and abnormal noise location based on acoustic-vibration coupling. It utilizes multiple types of noise and vibration signals to detect abnormalities in antenna mount running-in quality, avoiding misjudgments caused by a single sensor. Based on 1 / 3 octave band analysis, the acoustic-vibration coupling identification analyzes the abnormal vibrations and structural noise generated during gear running-in, obtaining the 1 / 3 octave band peak value generated by the gear in a certain common frequency band, as well as the full-band vibration acceleration level and sound pressure level. The selected frequency band is then analyzed using the spectrum and envelope spectrum to accurately locate gear faults.

[0047] The present invention relates to a method and device for evaluating gear running-in quality and locating abnormal noise based on acoustic-vibration coupling. According to expert experience and experimental data distribution characteristics, the importance of extracted features is scored, and a feature threshold learning method and a weight learning method are proposed to achieve the fusion and synergy of multi-dimensional heterogeneous features, thereby realizing the accurate evaluation of dynamic running-in quality. Attached Figure Description

[0048] Figure 1 This is a flowchart of an embodiment of the present invention.

[0049] Figure 2 This is a flowchart of the dynamic run-in quality evaluation process according to an embodiment of the present invention.

[0050] Figure 3 This is a schematic diagram of the smooth operation of the Mahalanobis distance according to an embodiment of the present invention.

[0051] Figure 4 This is a schematic diagram of abnormal operation Mahalanobis distance according to an embodiment of the present invention.

[0052] Figure 5 This is a schematic diagram of the abnormal runtime domain-frequency domain in an embodiment of the present invention. Detailed Implementation

[0053] The present invention will now be described in further detail with reference to the embodiments and the accompanying drawings.

[0054] One embodiment of the present invention is a method for evaluating gear running-in quality and locating abnormal noise based on acoustic-vibration coupling. It analyzes abnormal vibrations and structural noise generated during gear running-in to obtain the 1 / 3 octave peak value, full-band vibration acceleration level, and sound pressure level of the gear within a certain common frequency band. Then, it uses the spectrum and envelope spectrum to perform spectral analysis on the selected frequency band to locate gear faults. Figure 1 As shown.

[0055] 1. Obtain vibration acceleration and noise signals during the run-in process of the radar transmission system, calculate the 1 / 3 octave spectrum of gear structure noise and air noise, as well as vibration acceleration level and noise sound pressure level, to assess the run-in quality.

[0056] 1-1) Divide the spectrum obtained by the discrete Fourier transform of the vibration acceleration level into frequency bands according to the 1 / 3 octave band rule, and calculate the vibration acceleration level in the frequency band corresponding to each center frequency, in dB, as shown in the following formula.

[0057]

[0058] Where i is the 1 / 3 octave band sequence number; X m This represents the effective value of the vibrational acceleration of the m-th spectral line within the corresponding frequency band, in m / s². 2 M represents the number of sequence points within the corresponding frequency band; P ref The reference vibration acceleration is denoted as .

[0059] 1-2) Superimpose the vibration acceleration levels in each frequency band to obtain the total vibration acceleration level in the required frequency band, in dB, as shown in the following formula.

[0060]

[0061] Where K is the number of points in the 1 / 3 octave band sequence.

[0062] 1-3) Divide the spectrum of the sound pressure spectrum peak of the sound signal into frequency bands according to the 1 / 3 octave band rule and obtain it by discrete Fourier transform. Calculate the noise sound pressure level in the frequency band corresponding to each center frequency, in dB, as shown in the following formula.

[0063]

[0064] In the formula, i is the 1 / 3 octave band sequence number; P m P represents the effective sound intensity of each spectral line within the corresponding frequency band, in Pa; M represents the number of sequence points within the corresponding frequency band; P ref The reference sound intensity.

[0065] 1-4) Superimpose the peak values ​​of the noise spectrum in each frequency band to obtain the full-band noise sound pressure level in the required frequency band, in dB, as shown in the following formula.

[0066]

[0067] SPL i SPL is the sound pressure level of the i-th frequency band. Ai This is the weighted correction value for the i-th frequency band A.

[0068] Second, the gear running-in status is determined by calculating the Mahalanobis distance of the vibration acceleration level and noise level characteristic samples. If the gear running-in status is poor, the frequency band with the smallest difference between the vibration 1 / 3 octave band and the noise 1 / 3 octave band is selected, and the signal is bandpass filtered and envelope demodulated. The gear abnormal noise fault identification result is obtained through the preset expert experience value.

[0069] Determining the gear running-in state by calculating the Mahalanobis distance of vibration acceleration level and noise level feature samples includes:

[0070] 2-1) Calculate the Mahalanobis distance of the vibration acceleration level and noise level characteristic samples. The Mahalanobis distance M is calculated from each set of data samples collected. i Plot the Mahalanobis distance curve M, and add a Mahalanobis distance M each time. i Taking the first derivative of (a point on the Mahalanobis distance curve M), we get... If the obtained derivative Greater than the set threshold for the derivative of the Mahalanobis distance curve Right now This indicates a problem with gear meshing, and poor gear running-in. The formula for calculating the Mahalanobis distance M is shown below.

[0071]

[0072] Where N is the dimension of the initial sample matrix, which is sufficiently large. The initial sample matrix has a dimension of N and two rows, as shown in the following formula.

[0073]

[0074] The vibration level and noise level calculated from each set of collected data samples are used as a feature array. To calculate the covariance matrix, see the following formula.

[0075]

[0076] The Mahalanobis distance M can be calculated from each dimension of the covariance matrix. i .

[0077] 2-2) If the conclusion drawn in 2-1) is that the gear running-in condition is not good, then select the frequency bands for the minimum difference d of each frequency band of vibration and noise 1 / 3 octave band, select the frequency band i corresponding to d, and use the upper and lower limits of the frequency band i corresponding to d as the upper and lower cutoff frequencies of the bandpass filter. Perform envelope analysis on the data, obtain the current gear running status through the spectrum envelope diagram, and obtain the gear abnormal noise fault location result by analyzing the fault characteristic frequency, so as to realize the identification of gear abnormal noise fault.

[0078] The frequency band i corresponding to d is selected using formula (8).

[0079] d=min(|L i -SPL i |) (8)

[0080] Among them, L i The vibration acceleration level of the i-th frequency band in 1 / 3 octave band is calculated using formula (1); SPL i The sound pressure level of the i-th frequency band is 1 / 3 octave. The calculation formula is shown in formula (3).

[0081] The upper and lower limits of the frequency band i corresponding to d are used as the upper and lower cutoff frequencies of the bandpass filter (f). m -f0,f m +f0), the bandpass filter is expressed as:

[0082]

[0083] III. Evaluating Gear Running-in Quality

[0084] 3-1) Based on the calculated acceleration level, sound pressure level, and other vibration parameters (including end face runout, radial runout, vibration intensity, and MPR index), establish the characteristic vector Z of acoustic-vibration coupling. For example, vector Z = [runout error, vibration intensity, MPR value, total acceleration level, total sound pressure level]). The calculation of each physical quantity in the vibration parameters is shown in the following formulas.

[0085] a) End face / radial runout value

[0086] The circular runout error at each corner during the entire cycle is:

[0087] L(θ i )=e(θ i )+R(θ i (10)

[0088] Where, e(θ) i R(θ) represents the rotation error. i () represents shape error.

[0089] The runout value per revolution, i.e., the peak-to-peak value of the vibration displacement, can be calculated using the runout error principle, as shown in the following formula:

[0090] P = max(L(θ) i ))-min (L(θ i (11)

[0091] b) The formula for calculating vibration intensity is as follows:

[0092]

[0093] Where vs is the vibration intensity, in mm / s; vx, vy, and vz are the root mean square values ​​of the vibration velocities in the three mutually perpendicular directions X, Y, and Z, respectively, in mm / s; Nx, Ny, and Nz are the number of measuring points in the three directions X, Y, and Z, respectively.

[0094] c) MPR

[0095] Based on the vibration acceleration envelope spectrum, this invention uses the MPR (Mean-Peak Ratio) value of meshing vibration as the evaluation criterion. The magnitude of the MPR value directly reflects the amount of meshing characteristic components in the envelope demodulation spectrum, and its calculation formula is as follows:

[0096]

[0097]

[0098] Where, N h This indicates the harmonic order of the fault characteristic frequency to be calculated, for example, a value of 5; P i A represents the peak value of the spectral line at the harmonic frequency of the i-th fault frequency; s This represents the average spectral value within the range from the lower limit *a* of the i-th order fault frequency resonant band to the upper limit *b* of the i-th order fault frequency resonant band. For example, *a* is defined as the i-th order fault frequency harmonic (-3) times the fault frequency, in Hz, and *b* is defined as the i-th order fault frequency harmonic (+3) times the fault frequency, in Hz; C k This represents the amplitude of the k-th spectral line.

[0099] 3-2) The acoustic-vibration coupling feature vector Z is compared with the feature threshold vector γ to obtain their respective scores. Finally, the current gear running-in quality assessment result is obtained. The feature threshold vector γ is obtained by statistical calculation of historical data in the feature database.

[0100] like Figure 2 As shown, this invention scores the importance of extracted features based on preset expert experience values ​​and experimental data distribution characteristics, obtains feature weights w, and proposes feature threshold learning methods and weight learning methods to achieve the fusion and synergy of multi-dimensional heterogeneous features, thereby achieving accurate evaluation of dynamic run-in quality.

[0101] For example, for a certain type of radar mount transmission system, the vibration acceleration and noise signals during its running-in process are acquired. The Mahalanobis distance of the vibration acceleration level and noise level characteristic samples is calculated, such as... Figure 3 , Figure 4 As shown. Among them, Figure 3 The Mahalanobis distance curve shown is relatively stable, while Figure 4Channel 3 in the Mahalanobis distance curve shown has a large amplitude change, it is determined that the running-in of the transmission system is abnormal, and spectrum and envelope spectrum analysis are carried out. According to the spectrum and envelope spectrum analysis, the time-domain diagram and spectrum of the vibration acceleration are shown in Figure 5 . According to the preset expert experience values, the time-domain performance of a serious gear fault is impact vibration with large amplitude, whose frequency is equal to the rotating frequency of the shaft, and in the frequency domain, sidebands with the interval equal to the shaft rotating frequency appear near the meshing frequency and its higher harmonics; the sidebands generally have a large number, large amplitude and wide distribution. It can thus be determined that the gear has failed at this time, and subsequent processing can be carried out, such as evaluating the gear running-in quality.

[0102] When evaluating the gear running-in quality, feature value calculation is performed on the collected gear running-in data according to the indicators in the following table.

[0103] The collected gear running-in data is shown in Figure 1 the lower left corner, including vibration (acceleration, velocity, displacement) and sound.

[0104] Vibration intensity: 1.355 MPR value: 107.564 Total acceleration level 94.836 Total sound pressure level (A-weighted): 84.405

[0105] The calculated acoustic-vibration coupled eigenvector Z (vector Z = [runout error, vibration intensity, MPR value, overall acceleration level, overall sound pressure level]) is compared with the feature threshold vector γ obtained by statistically calculating the historical data in the feature database, respective scores are obtained, and finally the current gear running-in quality assessment result is obtained.

[0106] Wherein, the eigenvector Z is compared with the threshold vector γ and the threshold vector γ*par respectively to obtain an evaluation vector S. According to different situations where each element Z(i) of the acoustic-vibration coupled eigenvector Z is compared with the threshold γ(i) and par(i)*γ(i), different values are assigned to a certain element S(i) of the evaluation vector S. For example:

[0107] • When Z(i)>γ(i), S(i)=0

[0108] • When Z(i)<γ(i) and Z(i)>par(i)*γ(i), S(i)=1

[0109] • When Z(i)<par(i)*γ(i), S(i)=2

[0110] The vector par represents the early warning coefficient, with a value range of (0, 1), which can be set by the user. When the value is 1, it means that when the calculated feature value exceeds the threshold * par but is less than the threshold, the running-in quality is considered not good, and the corresponding eigenvector is assigned a value of 1. The dot product of the evaluation vector S and the feature weight ω is calculated and summed to obtain the final running-in score. The lower the score, the worse the running-in quality.

[0111] The beneficial effects of the gear running-in quality assessment and abnormal noise location method and equipment based on acoustic-vibration coupling of the present invention are as follows:

[0112] The present invention relates to a method and device for gear running-in quality assessment and abnormal noise location based on acoustic-vibration coupling. It utilizes multiple types of noise and vibration signals to detect abnormalities in antenna mount running-in quality, avoiding misjudgments caused by a single sensor. Based on 1 / 3 octave band analysis, the acoustic-vibration coupling identification analyzes the abnormal vibrations and structural noise generated during gear running-in, obtaining the 1 / 3 octave band peak value generated by the gear in a certain common frequency band, as well as the full-band vibration acceleration level and sound pressure level. The selected frequency band is then analyzed using the spectrum and envelope spectrum to accurately locate gear faults.

[0113] The present invention relates to a method and device for evaluating gear running-in quality and locating abnormal noise based on acoustic-vibration coupling. According to expert experience and experimental data distribution characteristics, the importance of extracted features is scored, and a feature threshold learning method and a weight learning method are proposed to achieve the fusion and synergy of multi-dimensional heterogeneous features, thereby realizing the accurate evaluation of dynamic running-in quality.

[0114] While the present invention has been disclosed above with reference to preferred embodiments, these embodiments are not intended to limit the invention. Any equivalent changes or modifications made without departing from the spirit and scope of the invention are also within the scope of protection of the invention. Therefore, the scope of protection of the present invention should be determined by the claims of this application.

Claims

1. A method for evaluating gear running-in quality and locating abnormal noise based on acoustic-vibration coupling, characterized in that, include: The vibration acceleration and noise signals of the radar transmission system during the running-in process are obtained, and the 1 / 3 octave band spectrum, vibration acceleration level, and noise sound pressure level of the gear structure noise and air noise are calculated. The gear running-in status is determined by calculating the Mahalanobis distance between the vibration acceleration level and noise level feature samples. If the gear running-in status is poor, the frequency band with the smallest difference between the vibration 1 / 3 octave band and the noise 1 / 3 octave band is selected, and the signal is bandpass filtered and envelope demodulated. The gear abnormal noise fault identification result is obtained through preset expert experience values.

2. The method for evaluating gear running-in quality and locating abnormal noise based on acoustic-vibration coupling according to claim 1, characterized in that, The calculation of the gear structure noise and air noise includes the 1 / 3 octave band spectrum, vibration acceleration level, and noise sound pressure level: 1-1) Divide the spectrum obtained by discrete Fourier transform of the vibration acceleration level into frequency bands according to the 1 / 3 octave band rule, and calculate the vibration acceleration level in the frequency band corresponding to each center frequency, in dB. See the following formula. Where i is the 1 / 3 octave band sequence number; X m This represents the effective value of the vibrational acceleration of the m-th spectral line within the corresponding frequency band, in m / s². 2 M represents the number of sequence points within the corresponding frequency band; P ref The reference vibration acceleration; 1-2) Superimpose the vibration acceleration levels in each frequency band to obtain the total vibration acceleration level in the desired frequency band, in dB, as shown in the following formula. Where K is the number of points in the 1 / 3 octave band sequence; 1-3) Divide the sound signal's sound pressure spectrum peak value into frequency bands according to the 1 / 3 octave band rule, using the discrete Fourier transform. Calculate the noise sound pressure level within each center frequency band, in dB, as shown in the following formula. In the formula, i is the 1 / 3 octave band sequence number; P m P represents the effective sound intensity of each spectral line within the corresponding frequency band, in Pa; M represents the number of sequence points within the corresponding frequency band; P ref As the reference sound intensity; 1-4) Superimpose the peak values ​​of the noise spectrum in each frequency band to obtain the full-band noise sound pressure level in the desired frequency band, in dB, as shown in the following formula. SPL i SPL is the sound pressure level of the i-th frequency band. Ai This is the weighted correction value for the i-th frequency band A.

3. The method for evaluating gear running-in quality and locating abnormal noise based on acoustic-vibration coupling according to claim 1, characterized in that, The method of determining the gear running-in state by calculating the Mahalanobis distance of vibration acceleration level and noise level feature samples includes: 2-1) Calculate the Mahalanobis distance of the vibration acceleration level and noise level characteristic samples. The Mahalanobis distance M is calculated from each set of data samples collected. i Plot the Mahalanobis distance curve M, and add a Mahalanobis distance M each time. i Taking the first derivative of (a point on the Mahalanobis distance curve M), we get... If the obtained derivative Greater than the set threshold for the derivative of the Mahalanobis distance curve Right now This indicates a problem with gear meshing, and poor gear running-in. The formula for calculating the Mahalanobis distance M is shown below. Where N is the dimension of the initial sample matrix, and the initial sample has a dimension of N and two rows, as shown in the following formula. The vibration level and noise level calculated from each set of collected data samples are used as a feature array. To calculate the covariance matrix, see the following formula. The Mahalanobis distance M can be calculated from each dimension of the covariance matrix. i .

4. The method for evaluating gear running-in quality and locating abnormal noise based on acoustic-vibration coupling according to claim 2, characterized in that, If the gear running-in condition is poor, the frequency band with the smallest difference between the vibration 1 / 3 octave band and the noise 1 / 3 octave band is selected, and the signal is bandpass filtered and envelope demodulated. The gear abnormal noise fault identification results obtained through preset expert experience values ​​include: If the gear running-in condition is determined by calculating the Mahalanobis distance of the vibration acceleration level and noise level characteristic samples, and the conclusion is that the gear running-in condition is poor, then the minimum difference d of each frequency band of the vibration and noise 1 / 3 octave band is selected, and the frequency band i corresponding to d is selected. The upper and lower limits of the frequency band i corresponding to d are used as the upper and lower cutoff frequencies of the bandpass filter. The data is subjected to envelope analysis, and the current gear running state is obtained through the spectrum envelope diagram. The fault location result of the gear abnormal noise is obtained by analyzing the fault characteristic frequency. Using formula (8), select the frequency band i corresponding to d. d=min(|L i -SPL i |) (8) Among them, L i The vibration acceleration level of the i-th frequency band in 1 / 3 octave band is calculated using formula (1); SPL i The sound pressure level of the i-th frequency band in 1 / 3 octave band is calculated using formula (3). The upper and lower limits of the frequency band i corresponding to d are used as the upper and lower cutoff frequencies of the bandpass filter (f). m -f0,f m +f0), the bandpass filter is expressed as:

5. The method for evaluating gear running-in quality and locating abnormal noise based on acoustic-vibration coupling according to claim 1, characterized in that, Also includes: Based on the calculated acceleration level, sound pressure level, and other vibration parameters, a characteristic vector Z for acoustic-vibration coupling is established. The acoustic-vibration coupling feature vector Z is compared with the feature threshold vector γ to obtain their respective scores, and finally the current gear running-in quality assessment result is obtained. The feature threshold vector γ is obtained by statistical calculation of historical data in the feature database.

6. The method for evaluating gear running-in quality and locating abnormal noise based on acoustic-vibration coupling according to claim 5, characterized in that, The remaining vibration indicators include end face runout, radial runout, vibration intensity, and MPR index.

7. The method for evaluating gear running-in quality and locating abnormal noise based on acoustic-vibration coupling according to claim 5, characterized in that, The step of comparing the acoustic-vibration coupling feature vector Z with the feature threshold vector γ includes: The feature vector Z is compared with the threshold vector γ and the threshold vector γ*par respectively to obtain the evaluation vector S. Based on the different comparisons between each element Z(i) of the acoustic-vibration coupling feature vector Z and the threshold γ(i) and par(i)*γ(i), a different value is assigned to a certain element S(i) of the evaluation vector S. The vector par represents the warning coefficient, with a value range of (0, 1). When the value is 1, it means that the value of the calculated feature exceeds the threshold * par but is less than the threshold, and the run-in quality is considered poor. The corresponding feature vector is then assigned a value of 1. The final run-in score is obtained by summing the dot product of the evaluation vector S and the feature weight ω. The lower the score, the worse the run-in quality.

8. A gear running-in quality assessment and abnormal noise location device based on acoustic-vibration coupling, characterized in that, The device includes a memory and a processor; the memory stores a computer program for implementing a method for gear running-in quality assessment and abnormal noise location based on acoustic-vibration coupling, and the processor executes the computer program to implement the steps of the method according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-7.

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