Accurate positioning system and method for operation noise source of chassis transmission system of engineering vehicle

Abstract

The invention discloses an engineering vehicle chassis transmission system operation noise source accurate positioning system and method, and the method comprises the steps: S1, installing a vibration sensor on a key part of an engineering vehicle chassis transmission system, and collecting the vibration signal of each key part; s2, carrying an acoustic sensor on a mobile device, wherein the acoustic sensor can sequentially move to the corresponding position of each key component along with the mobile device to carry out acoustic signal acquisition; s3, performing cross-correlation analysis on the acquired acoustic signals and vibration signals, calculating cross-correlation coefficients among the signals acquired by different sensors, and preliminarily determining position information of a noise source by comparing the cross-correlation coefficients of all key components; and S4, performing cepstrum analysis on sensor signals near the preliminarily positioned noise source position, identifying periodic components in the signals, verifying the position of the noise source, and accurately positioning the noise source in combination with a cross-correlation analysis result and a cepstrum verification result.

Description

Technical Field

[0001] The present invention relates to the technical field of noise source positioning, and in particular to a system and method for accurately positioning an operating noise source of a chassis transmission system of an engineering vehicle. Background Art

[0002] With the continuous development of the modern engineering machinery industry, the noise problem of the chassis transmission system of engineering vehicles has received increasing attention. Especially during the operation process, engineering vehicles often experience abnormal conditions such as increased noise or abnormal noise. The positioning and analysis of noise sources is an important way to improve the chassis optimization design of engineering vehicles, and is an important link in the control of operating noise and improvement of comfort of engineering vehicles. Traditional noise source positioning technology usually relies on fixed sensor arrays to detect and locate noise sources. The positioning accuracy of these technologies is limited by the layout and number of sensors, which greatly limits their performance in complex noise environments. Especially in dynamic and changeable actual operating environments, the location of noise sources may change at any time, and traditional fixed sensor arrays cannot be adjusted in real time, resulting in insufficient positioning accuracy and efficiency. In addition, existing noise source positioning methods usually rely on a single type of sensor, such as acoustic sensors or vibration sensors. The limitation is that they cannot fully capture the multi-dimensional information of the noise source, affecting the accuracy of the positioning results.

[0003] Therefore, how to combine multiple sensors with innovative algorithms to achieve high-precision positioning of operational noise sources in a dynamic environment has become a technical problem that needs to be solved urgently in this field in recent years. Summary of the invention

[0004] Purpose of the invention: In order to overcome the deficiencies in the prior art, the present invention provides a system and method for accurately locating the operating noise source of the chassis transmission system of an engineering vehicle, which can solve the problem of insufficient noise source positioning accuracy caused by static sensor arrays and single sensors.

[0005] Technical solution: To achieve the above-mentioned purpose, a method for accurately locating the operating noise source of a chassis transmission system of an engineering vehicle of the present invention comprises the following steps: Step S1: installing a vibration sensor on a key component of the chassis transmission system of an engineering vehicle to collect vibration signals of each key component; Step S2: the acoustic sensor is mounted on a mobile device, and the acoustic sensor can move to the corresponding position of each key component in sequence with the mobile device to collect acoustic signals; Step S3: performing cross-correlation analysis on the collected acoustic signals and vibration signals, calculating the cross-correlation coefficients between the signals collected by different sensors, and preliminarily determining the location information of the noise source by comparing the cross-correlation coefficients of each key component; Step S4: performing inverse spectrum analysis on the sensor signal near the position of the preliminarily located noise source, identifying the periodic components in the signal, verifying the position of the noise source, and accurately locating the noise source by combining the cross-correlation analysis results and the inverse spectrum verification results; In step S3, when performing cross-correlation analysis on the collected acoustic signals and vibration signals, a fixed working condition and a variable working condition are distinguished, and analysis is performed separately under the two working conditions.

[0006] Further, in step S3, if it is a fixed working condition, the following steps are specifically included: Step S3.11: spectrum analysis; perform fast Fourier transform on the time domain signals collected by all sensors to obtain a spectrum diagram; Step S3.12: select sensitive frequency bands; select the peak with the highest amplitude from the spectrum of all sensors as the center frequency of the first sensitive frequency band, and expand to both sides with the peak frequency as the center until the amplitude drops to α of the peak value, determine the upper and lower limits of the frequency band, and record the upper and lower limit frequencies of the frequency band as the first sensitive frequency band; the value of α is between 0 and 1; then select the second highest, the third highest, the fourth highest, the fifth ... The peak values ​​of the three highs and subsequent heights are used as the center frequencies of the subsequent sensitive frequency bands. The above process is repeated to obtain the subsequent sensitive frequency bands until the amplitude of the remaining peaks is lower than α times of the current frequency band; Step S3.13: Determine the reference sensor; The sensor where the sensitive frequency band determined in the previous step is located is used as the reference sensor; Step S3.14: Correlation calculation; Using the sensitive frequency band of the reference sensor as the standard, find the corresponding frequency band in the frequency spectrum of other sensors, extract the time domain signals of the reference sensor and other sensors in the corresponding frequency band, calculate the mutual correlation coefficients of each sensor in the corresponding frequency band, and construct a correlation coefficient sensitivity matrix. The calculation formula is as follows:

[0007]

[0008] Among them, r XF is the correlation coefficient between two signals X and F, X i and F i are the sampling values ​​of signals X and F at the i-th time point, and are the average values ​​of signals X and F respectively, and n is the total number of data points of the signal; step S3.15: repeat the above correlation calculation process for each sensitive frequency band; select subsequent sensitive frequency bands in turn, calculate the correlation value of each sensor, and construct a correlation coefficient sensitivity matrix for each sensitive frequency band; step S3.16: obtain multiple correlation coefficient sensitivity matrices through calculation of multiple sensitive frequency bands, and each matrix reflects the correlation between each sensor signal in different frequency bands; then determine the location of the noise source based on the matrix.

[0009] Further, in step S3, if it is a variable working condition, the specific steps include the following: Step S3.21: pre-process the collected acoustic signal and vibration signal; divide the continuous signal into a series of shorter time slices, namely time windows; start from the tth second of the signal, take Δt seconds as the step length, and stop at the tth second before the end, and define the time window T; the length of each time window is Δt seconds, and a certain margin is left between the time windows; Step S3.22: Window processing and feature extraction: for each channel, according to the time window T, intercept the short-time segment of the signal, and the signal segment in each time window will be used for the subsequent Feature extraction; perform de-averaging on the signal in each time window to eliminate the constant score, calculate the characteristic index for each time window signal after de-averaging, and obtain the real-time characteristic matrix of each channel regarding the characteristic index; Step S3.23: calculate the mutual correlation coefficient, for each pair of channel characteristic matrices, calculate the mutual correlation coefficient between the corresponding characteristic matrices of each channel; Step S3.24: arrange the calculated mutual correlation coefficients in channel order to generate a dynamic correlation matrix; Step S3.25: obtain the dynamic correlation matrix of multiple indicators through correlation calculation, and then determine the location of the noise source based on the matrix.

[0010] Furthermore, the method for determining the location of the noise source based on the matrix is ​​as follows: if the maximum correlation coefficients in all matrices appear in the same sensor, or the maximum correlation coefficients in more than half of the matrices appear in the same sensor, the location of the sensor is the location of the operating noise source; if the maximum correlation coefficient does not appear in the same sensor in more than half of the matrices, the average value of the maximum correlation coefficients of the same sensor is calculated, and the location of the sensor with the largest average value is taken as the location of the operating noise source.

[0011] Furthermore, in step S3.22, the following characteristic indexes are calculated:

[0012] Kurtosis:

[0013] Form Factor:

[0014] Crest Factor:

[0015] Pulse Factor:

[0016] Skewness:

[0017] Among them, x i is the ith data point of the signal, is the mean of the signal, N is the total number of data points of the signal, and RMS is the root mean square value.

[0018] Further, in step S3.23, the characteristic matrix of each pair of channels includes a real-time kurtosis matrix, a real-time waveform factor matrix, a real-time peak factor matrix, a real-time pulse factor matrix and a real-time skewness matrix, and the calculation formula of the mutual correlation coefficient between the characteristic matrices corresponding to each channel is as follows:

[0019]

[0020] Among them, x 1 and x 2 is the eigenvalue sequence of the two channels, and are their means respectively, and N is the total number of data points of the signal.

[0021] Furthermore, in step S4, the sensor signal near the initially located noise source is subjected to inverse spectrum analysis to verify the location of the operating noise source, and the operating noise source is accurately located by combining the correlation analysis result and the inverse spectrum verification result; specifically, the following steps are included: step S4.1: selecting the sensor signal of the initially located noise source; step S4.2: performing a fast Fourier transform on the selected sensor signal x(t) to obtain a spectrum X(f):

[0022] X(f) = F{x(t)};

[0023] Take the logarithm of the amplitude of the spectrum to obtain the logarithmic spectrum log|X(f)|, perform inverse Fourier transform on the logarithmic spectrum to obtain the inverse spectrum C(q): C(q) = F -1 {log|X(f)|}; Step S4.3: Perform cepstrum analysis to identify obvious peaks in the cepstrum, which correspond to periodic components in the signal; Step S4.4: Calculate the corresponding periodic frequency based on the peak position in the cepstrum:

[0024]

[0025] Wherein, q is the inverse frequency; Step S4.5: Match the periodic frequency in the inverse spectrum diagram with the known characteristic frequency of the noise source to confirm the location of the noise source.

[0026] Furthermore, a precise positioning system for the operating noise source of the chassis transmission system of an engineering vehicle includes a signal transceiver unit, which includes a vibration sensor, an acoustic sensor, a passive radio frequency tag and an antenna; includes a signal processing unit, which processes the signals received from the acoustic sensor and the vibration sensor; includes an analysis control unit, which includes a reader and a controller; includes a guide rail unit, which includes a motor, a guide rail and a slide seat; the guide rail is installed on a test bench, the engineering vehicle chassis is placed on the test bench, and the engineering vehicle chassis is located above the guide rail; the slide seat is installed on the guide rail and can slide along the guide rail under the drive of the motor; the vibration sensor and the passive radio frequency tag are installed on the engineering vehicle chassis. Vibration sensors collect vibration signals of key components of the transmission system of the vehicle chassis; acoustic sensors, antennas and readers are installed on the slide, the antenna receives signals from the passive radio frequency tag wirelessly, and the reader receives signals from the antenna; the reader transmits the received signal to the analysis and control unit, which analyzes the signal from the passive radio frequency tag, calculates the position of the key components of the transmission system based on the analysis result, and controls the motor through the controller; the motor drives the slide to move to the corresponding position of each key component in turn, so that the acoustic sensor collects acoustic signals; and then the signal processing unit processes the signals collected by the acoustic sensor and the vibration sensor.

[0027] Furthermore, the test bench is provided with a bump simulation unit, which includes a vibration generator, a vibration collector and a vibration simulation table; the vibration generator is arranged under the wheel of the engineering vehicle chassis, and the vibration generator can apply vibration to the wheel. When the wheel vibrates, the engineering vehicle chassis is also driven to vibrate through the suspension to simulate the working condition of the engineering vehicle chassis under bumpy conditions; the vibration simulation table is located on the test bench, and the guide rail is installed on the vibration simulation table; the vibration collector can collect vibration information of the engineering vehicle chassis, and transmit the vibration information of the engineering vehicle chassis to the vibration simulation table, so that the vibration simulation table drives the guide rail and the engineering vehicle chassis to vibrate synchronously, and the acoustic sensor on the guide rail can remain relatively still with the engineering vehicle chassis when collecting acoustic signals.

[0028] Beneficial effects: The system and method for accurately locating the operating noise source of the chassis transmission system of an engineering vehicle of the present invention have the following beneficial effects:

[0029] 1) Combining multiple signal collection methods such as vibration sensors, acoustic sensors and passive radio frequency tags, and adopting innovative correlation algorithms, it effectively overcomes the problem of insufficient noise source positioning accuracy caused by a single sensor and algorithm in the existing technology;

[0030] 2) Since engineering vehicles are prone to encounter bumpy roads in actual working environments, an additional bump simulation unit is provided to simulate the working conditions of the engineering vehicle chassis in a bumpy environment and to explore the location of noise sources of the engineering vehicle chassis in a bumpy environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Attached Figure 1 This is a schematic diagram of the structure of the precise positioning system for the operating noise source of the chassis transmission system of an engineering vehicle;

[0032] Attached Figure 2 It is a schematic diagram of the structure of the guide rail and the slide seat;

[0033] Attached Figure 3 This is a flow chart of the method for accurately locating the noise source of the chassis transmission system of an engineering vehicle;

[0034] Attached Figure 4 This is a flow chart of the noise source location method under certain working conditions;

[0035] Attached Figure 5 It is a flow chart of the noise source location method under variable working conditions;

[0036] Attached Figure 6 This is the flow chart of the inverse spectrum verification method. DETAILED DESCRIPTION

[0037] The present invention will be further described below in conjunction with the accompanying drawings.

[0038] As attached Figures 1 to 6 The method for accurately locating the operating noise source of the chassis transmission system of an engineering vehicle comprises the following steps:

[0039] Step S1: Install vibration sensors 1 on key components of the chassis transmission system of an engineering vehicle to collect vibration signals of each key component in real time;

[0040] Step S2: The acoustic sensor 4 is mounted on the mobile device, and the acoustic sensor 4 can move with the mobile device to the corresponding position of each key component in turn to collect acoustic signals, that is, the acoustic sensor 4 stops under each key component in turn to obtain acoustic signals related to the noise source;

[0041] Step S3: performing cross-correlation analysis on the collected acoustic signals and vibration signals, calculating the cross-correlation coefficients between the signals collected by different sensors, and preliminarily determining the location information of the noise source by comparing the cross-correlation coefficients of each key component;

[0042] Step S4: Perform inverse spectrum analysis on the sensor signal near the initially located noise source to identify the periodic components in the signal, verify the location of the noise source, and accurately locate the noise source by combining the cross-correlation analysis results and the inverse spectrum verification results.

[0043] In addition, in step S3, when cross-correlation analysis is performed on the collected acoustic signals and vibration signals, a fixed working condition and a variable working condition are distinguished, and analysis is performed separately under the two working conditions.

[0044] If the working condition is fixed in step S3, locating the noise source under the fixed working condition specifically includes the following steps:

[0045] Step S3.11: spectrum analysis: performing fast Fourier transform on the time domain signals collected by all sensors to obtain a spectrum diagram;

[0046] Step S3.12: Select a sensitive frequency band; select the peak with the highest amplitude from the frequency spectra of all sensors as the center frequency of the first sensitive frequency band, and expand to both sides with the peak frequency as the center until the amplitude drops to α of the peak value, determine the upper and lower limits of the frequency band, and record the upper and lower limit frequencies of the frequency band as the first sensitive frequency band;

[0047] The value of α is between 0 and 1, for example

[0048] Then, the peak with the second highest amplitude is selected from the spectrum of all sensors as the center frequency of the second sensitive frequency band. With the peak frequency as the center, the band is expanded to both sides until the amplitude drops to α of the peak value, and the upper and lower limits of the band are determined. The upper and lower limit frequencies of the band are recorded as the second sensitive frequency band.

[0049] Then, the third highest, fourth highest and subsequent peaks are selected in turn as the center frequencies of subsequent sensitive frequency bands, and the above process is repeated to obtain subsequent sensitive frequency bands until the amplitude of the remaining peak is lower than α times of the current frequency band;

[0050] Step S3.13: Determine a reference sensor; use the sensor in the sensitive frequency band determined in the previous step as the reference sensor;

[0051] Step S3.14: Correlation calculation: Taking the sensitive frequency band of the reference sensor as the standard, find the corresponding frequency band in the frequency spectrum of other sensors, extract the time domain signals of the reference sensor and other sensors in the corresponding frequency band, calculate the mutual correlation coefficient of each sensor in the corresponding frequency band, and construct the correlation coefficient sensitivity matrix. The calculation formula is as follows:

[0052]

[0053] Among them, r XFis the correlation coefficient between two signals X and F, X i and F i are the sampling values ​​of signals X and F at the i-th time point, and are the average values ​​of signals X and F respectively, and n is the total number of data points of the signal;

[0054] Step S3.15: Repeat the above correlation calculation process for each sensitive frequency band; then select subsequent sensitive frequency bands in turn, calculate the correlation value of each sensor, and construct a correlation coefficient sensitivity matrix for each sensitive frequency band;

[0055] Step S3.16: Multiple correlation coefficient sensitivity matrices are calculated through multiple sensitive frequency bands, and each matrix reflects the correlation between the various sensor signals in different frequency bands; based on these matrices, the location of the noise source can be determined by the following rules:

[0056] Consistency determination: If the maximum correlation coefficients in all matrices appear in the same position, that is, the same sensor, then the location of the sensor can be determined as the source of the operating noise; if the maximum correlation coefficients in more than half of the matrices appear in the same position, then the location of the sensor can also be determined as the source of the operating noise.

[0057] Average value determination: If the maximum correlation coefficient does not appear at the same position in more than half of the matrices, the average value of the maximum correlation coefficients at the same position is calculated, and the position with the largest average value is taken as the location of the operating noise source.

[0058] If the operating condition is variable in step S3, locating the noise source under the variable operating condition specifically includes the following steps:

[0059] Step S3.21: pre-process the collected acoustic and vibration signals; divide the continuous signal into a series of shorter time slices, i.e., time windows; start from the tth second of the signal, take Δt seconds as the step length, and stop at the tth second before the last second, defining the time window T; the length of each time window is Δt seconds, and a margin of about t seconds is left between time windows;

[0060] Step S3.22: Window processing and feature extraction: For each channel, including the acoustic channel and the vibration channel, a short-time segment of the signal is intercepted according to the time window T. The signal segment in each time window will be used for subsequent feature extraction; the signal in each time window is de-averaged to eliminate the constant score, and the following feature indicators are calculated for each time window signal after de-averaging to obtain a real-time feature matrix of each channel with respect to the feature indicators;

[0061] Kurtosis:

[0062] Form Factor:

[0063] Crest Factor:

[0064] Pulse Factor:

[0065] Skewness:

[0066] Among them, x i is the ith data point of the signal, is the mean value of the signal, N is the total number of data points of the signal, and RMS value is the root mean square value;

[0067] Step S3.23: Calculate the mutual correlation coefficient. For each pair of channel feature matrices, including the real-time kurtosis matrix, the real-time waveform factor matrix, the real-time peak factor matrix, the real-time pulse factor matrix, and the real-time skewness matrix, calculate the mutual correlation coefficient between the feature matrices corresponding to each channel. The calculation formula for the mutual correlation coefficient between the feature matrices corresponding to each channel is as follows:

[0068]

[0069] Among them, x 1 and x 2 is the eigenvalue sequence of the two channels, and are their means respectively, N is the total number of data points of the signal;

[0070] Step S3.24: Arrange the calculated mutual correlation coefficients according to the channel order to generate a dynamic correlation matrix; this matrix can intuitively show the correlation between the corresponding features of different channels;

[0071] Step S3.25: The dynamic correlation matrix of multiple indicators is obtained by correlation calculation, and each matrix reflects the correlation between the sensor signals under different indicators; based on these matrices, the location of the operating noise source can be determined by the following rules:

[0072] The method of determining the location of the noise source based on the matrix is ​​as follows:

[0073] Consistency determination: If the maximum correlation coefficients in all matrices appear in the same position, that is, the same sensor, then the location of the sensor can be determined to be the location of the operating noise source; if the maximum correlation coefficients in more than half of the matrices appear in the same position, then the location of the sensor can also be determined to be the location of the operating noise source;

[0074] Average value determination: If the maximum correlation coefficient does not appear at the same position in more than half of the matrices, the average value of the maximum correlation coefficients at the same position is calculated, and the position with the largest average value is taken as the location of the operating noise source.

[0075] In step S4, a cepstrum analysis is performed on the sensor signal near the initially located noise source to verify the location of the operating noise source, and the operating noise source is accurately located by combining the correlation analysis result and the cepstrum verification result; specifically, the following steps are included:

[0076] Step S4.1: Select the signal of the sensor where the noise source is located;

[0077] Step S4.2: Perform a fast Fourier transform on the selected sensor signal x(t) to obtain a spectrum X(f):

[0078] X(f) = F{x(t)};

[0079] Take the logarithm of the amplitude of the spectrum to obtain the logarithmic spectrum log|X(f)|, perform inverse Fourier transform on the logarithmic spectrum to obtain the inverse spectrum C(q): C(q) = F -1 {log|X(f)|};

[0080] Step S4.3: Performing a cepstral graph analysis to identify obvious peaks in the cepstral graph, which correspond to periodic components in the signal;

[0081] Step S4.4: Peak frequency calculation: According to the peak position in the inverse spectrum, calculate the corresponding periodic frequency:

[0082]

[0083] Among them, q is the inverse frequency and q is the time dimension;

[0084] Step S4.5: Periodic feature matching: Match the periodic frequency in the inverse spectrum with the known characteristic frequency of the noise source to confirm the location of the noise source.

[0085] As attached Figure 1 and 2As shown in , the present invention also provides a precise positioning system for the operating noise source of the chassis transmission system of an engineering vehicle, including a signal transceiver unit, a signal processing unit, an analysis control unit and a guide rail unit. The signal transceiver unit includes a vibration sensor 1, an acoustic sensor 4, a passive radio frequency tag 2 and an antenna 5. The signal processing unit includes a signal preprocessing module and a correlation analysis module, and the signal processing unit can process the signals received from the acoustic sensor 4 and the vibration sensor 1. The analysis control unit includes a reader and a controller, and the reader is responsible for receiving and parsing the signal from the antenna 5. The system transmits the tag information to the controller, and the controller controls the movement of the guide rail unit. The guide rail unit includes a motor, a guide rail 3 and a slide 6.

[0086] The vibration sensor 1 in the signal transceiver unit is installed on the key components of the transmission system of the engineering vehicle, and all vibration sensors 1 are located on the same axis to collect vibration signals of various components of the transmission system in real time for subsequent correlation analysis. The acoustic sensor 4 is installed on the guide rail 3 system and moves with the movement of the guide rail 3 to collect acoustic signals in real time.

[0087] The passive RFID tags 2 in the signal transceiver unit are attached to the key components of the transmission system, containing the location information and movement instructions of the components. These tags are used to calibrate the position of each component and provide spatial reference information for the subsequent noise source location. By reading these passive RFID tags 2 with a reader, the system can obtain the location information of the components in real time, and accurately locate the noise source by combining acoustic and vibration signals.

[0088] The antenna 5 in the signal transceiver unit is responsible for wireless communication with the passive radio frequency tag 2. Through the antenna 5, the system can receive the signal sent by the passive radio frequency tag 2, ensuring that the system obtains accurate component position information so as to control the guide rail unit to be positioned at the corresponding position. The interaction between the antenna 5 and the reader enables the signal transceiver unit to work in coordination with the guide rail unit, ensuring that during the positioning process, the slide 6 can adjust its position according to the positioning information of the component, thereby achieving accurate detection of the noise source.

[0089] The guide rail 3 is installed on the test bench, the engineering vehicle chassis is placed on the test bench, and the engineering vehicle chassis is located above the guide rail 3; the slide seat 6 is installed on the guide rail 3, and can slide along the guide rail 3 driven by the motor; the vibration sensor 1 and the passive radio frequency tag 2 are installed on the key components of the transmission system of the engineering vehicle chassis, and the vibration sensor 1 collects vibration signals of each key component.

[0090] The acoustic sensor 4, antenna 5 and reader are installed on the slide 6. The antenna 5 receives the signal sent by the passive radio frequency tag 2 in a wireless manner, and the reader receives the signal from the antenna 5. The reader transmits the received signal to the analysis and control unit, which analyzes the signal sent by the passive radio frequency tag 2, calculates the position of the key components of the transmission system according to the analysis result, and controls the motor through the controller. The motor drives the slide 6 to move to the corresponding position of each key component in turn, so that the acoustic sensor 4 collects the acoustic signal. Then the signal processing unit processes the signals collected by the acoustic sensor 4 and the vibration sensor 1.

[0091] Existing vehicle noise source location systems usually only simulate the working conditions of vehicles driving on flat roads, but for engineering vehicles, their operating environment is more complex, which means that engineering vehicles often need to operate on bumpy roads. On bumpy roads and flat roads, the gravity conditions of various key components in the transmission system on the chassis of engineering vehicles will be different, so the noise source conditions on the chassis of engineering vehicles may also be different. At present, there is a lack of research solutions for locating the noise sources of the chassis of engineering vehicles on bumpy roads.

[0092] Therefore, in the present invention, a bump simulation unit is also provided on the test bench, and the bump simulation unit includes a vibration generator, a vibration collector and a vibration simulation bench. The vibration generator is provided under each wheel of the engineering vehicle chassis, and the vibration generator can apply vibration to the wheel, and the wheel is connected to the engineering vehicle chassis through the suspension, so when the wheel vibrates, the engineering vehicle chassis is also driven to vibrate through the suspension to simulate the working condition of the engineering vehicle chassis under bumpy conditions.

[0093] The vibration simulation table is installed on the test table, and the guide rail 3 is correspondingly installed on the vibration simulation table. The vibration collector can collect the vibration information of the chassis of the engineering vehicle, and transmit the vibration information of the chassis of the engineering vehicle to the vibration simulation table, so that the vibration simulation table drives the guide rail 3 to vibrate synchronously with the chassis of the engineering vehicle, so that the acoustic sensor 4 on the guide rail 3 can remain relatively still with the chassis of the engineering vehicle when collecting acoustic signals. After the signal collection is completed, the precise positioning method of the operating noise source of the chassis transmission system of the engineering vehicle of the present invention is used to calculate to locate the noise source of the chassis of the engineering vehicle.

[0094] Since the acoustic sensor 4 in the present invention needs to be moved to the bottom of each key component of the engineering vehicle chassis, the acoustic sensor 4 cannot be directly connected to the bottom of the engineering vehicle chassis, and cannot directly follow the vibration of the engineering vehicle chassis. Therefore, if a vibration simulation platform is not provided, when the engineering vehicle chassis vibrates, the engineering vehicle chassis will be relatively displaced with the guide rail 3, which will cause the acoustic signal collected by the acoustic sensor 4 on the guide rail 3 to deviate, and the algorithm in the present invention cannot be used for calculation. Therefore, an additional vibration simulation platform must be provided to keep the guide rail 3 and the acoustic sensor 4 on the engineering vehicle chassis stationary, so that the acoustic sensor 4 can collect the correct acoustic signal.

[0095] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for accurately locating the operating noise source of the chassis transmission system of an engineering vehicle, characterized in that: The following steps are involved: Step S1: installing vibration sensors (1) on key components of the chassis transmission system of an engineering vehicle to collect vibration signals of each key component; Step S2: The acoustic sensor (4) is mounted on the mobile device, and the acoustic sensor (4) can move with the mobile device to the corresponding position of each key component in sequence to collect acoustic signals; Step S3: performing cross-correlation analysis on the collected acoustic signals and vibration signals, calculating the cross-correlation coefficients between the signals collected by different sensors, and preliminarily determining the location information of the noise source by comparing the cross-correlation coefficients of each key component; Step S4: Performing a cepstrum analysis on the sensor signal near the initially located noise source to identify the periodic components in the signal, verify the location of the noise source, and accurately locate the noise source by combining the cross-correlation analysis result and the cepstrum verification result; In step S3, when cross-correlation analysis is performed on the collected acoustic signals and vibration signals, a fixed working condition and a variable working condition are distinguished, and analysis is performed separately under the two working conditions.

2. The method for accurately locating the operating noise source of the chassis transmission system of an engineering vehicle according to claim 1, characterized in that: In step S3, if it is a fixed working condition, the specific steps include: Step S3.11: spectrum analysis: performing fast Fourier transform on the time domain signals collected by all sensors to obtain a spectrum diagram; Step S3.12: Select a sensitive frequency band; select the peak with the highest amplitude from the frequency spectra of all sensors as the center frequency of the first sensitive frequency band, expand to both sides with the peak frequency as the center until the amplitude drops to α of the peak, determine the upper and lower limits of the frequency band, and record the upper and lower limit frequencies of the frequency band as the first sensitive frequency band; the value of α is between 0 and 1; then select the second highest, third highest and subsequent peaks from the frequency spectra of all sensors in turn as the center frequencies of subsequent sensitive frequency bands, repeat the above process to obtain subsequent sensitive frequency bands, until the amplitude of the remaining peak is lower than α times of the current frequency band; Step S3.13: Determine a reference sensor; use the sensor in the sensitive frequency band determined in the previous step as the reference sensor; Step S3.14: Correlation calculation; Taking the sensitive frequency band of the reference sensor as the standard, find the corresponding frequency band in the spectrum of other sensors, extract the time domain signals of the reference sensor and other sensors in the corresponding frequency band, calculate the mutual correlation coefficient of each sensor in the corresponding frequency band, and construct the correlation coefficient sensitivity matrix. The calculation formula is as follows: Among them, r XF is the correlation coefficient between two signals X and F, X i and F i are the sampling values ​​of signals X and F at the i-th time point, and are the average values ​​of signals X and F respectively, and n is the total number of data points of the signal; Step S3.15: Repeat the above correlation calculation process for each sensitive frequency band; select subsequent sensitive frequency bands in turn, calculate the correlation value of each sensor, and construct a correlation coefficient sensitivity matrix of each sensitive frequency band; Step S3.16: multiple correlation coefficient sensitivity matrices are obtained by calculating multiple sensitive frequency bands, each matrix reflects the correlation between each sensor signal in a different frequency band; then the position of the noise source is determined based on the matrix.

3. The method for accurately locating the operating noise source of the chassis transmission system of an engineering vehicle according to claim 1, characterized in that: In step S3, if it is a variable operating condition, the following steps are specifically included: Step S3.21: pre-process the collected acoustic and vibration signals; divide the continuous signal into a series of shorter time slices, i.e., time windows; define the time window T starting from the tth second of the signal and taking Δt seconds as the step length until the tth second before the last; the length of each time window is Δt seconds, and a certain margin is left between time windows; Step S3.22: Window processing and feature extraction: For each channel, according to the time window T, a short-time segment of the signal is intercepted, and the signal segment in each time window will be used for subsequent feature extraction; the signal in each time window is de-averaged to eliminate the constant score, and the feature index is calculated for each time window signal after de-averaging to obtain the real-time feature matrix of each channel with respect to the feature index; Step S3.23: Calculate the mutual correlation coefficient. For each pair of channel feature matrices, calculate the mutual correlation coefficient between the feature matrices corresponding to each channel. Step S3.24: Arrange the calculated mutual correlation coefficients according to the channel order to generate a dynamic correlation matrix; Step S3.25: A dynamic correlation matrix of multiple indicators is obtained through correlation calculation, and then the location of the noise source is determined based on the matrix.

4. A method for accurately locating the operating noise source of a chassis transmission system of an engineering vehicle according to claim 2 or 3, characterized in that: The method of determining the location of the noise source based on the matrix is ​​as follows: If the maximum correlation coefficients in all matrices appear in the same sensor, or if the maximum correlation coefficients in more than half of the matrices appear in the same sensor, the location of the sensor is the location of the operating noise source; If the maximum correlation coefficient does not appear in the same sensor in more than half of the matrices, the maximum correlation coefficients of the same sensor are averaged, and the location of the sensor with the largest average value is taken as the location of the operating noise source.

5. The method for accurately locating the operating noise source of the chassis transmission system of an engineering vehicle according to claim 3, characterized in that: In step S3.22, the following characteristic indices are calculated: Kurtosis: Form Factor: Crest Factor: Pulse Factor: Skewness: Among them, x i is the ith data point of the signal, is the mean of the signal, N is the total number of data points of the signal, and RMS is the root mean square value.

6. The method for accurately locating the operating noise source of the chassis transmission system of an engineering vehicle according to claim 5, characterized in that: In step S3.23, the characteristic matrix of each pair of channels includes a real-time kurtosis matrix, a real-time waveform factor matrix, a real-time peak factor matrix, a real-time pulse factor matrix and a real-time skewness matrix. The calculation formula of the mutual correlation coefficient between the characteristic matrices corresponding to each channel is as follows: Among them, x1 and x2 are the eigenvalue sequences of the two channels, and are their means respectively, and N is the total number of data points of the signal.

7. The method for accurately locating the operating noise source of the chassis transmission system of an engineering vehicle according to claim 1, characterized in that: In step S4, a cepstrum analysis is performed on the sensor signal near the initially located noise source to verify the location of the operating noise source, and the operating noise source is accurately located by combining the correlation analysis result and the cepstrum verification result; specifically, the following steps are included: Step S4.1: Select the signal of the sensor where the noise source is located; Step S4.2: Perform a fast Fourier transform on the selected sensor signal x(t) to obtain a spectrum X(f): X(f) = F{x(t)}; Take the logarithm of the amplitude of the spectrum to obtain the logarithmic spectrum log|X(f)|, perform inverse Fourier transform on the logarithmic spectrum to obtain the inverse spectrum C(q): C(q) = F -1 {log|X(f)|}; Step S4.3: Performing a cepstral graph analysis to identify obvious peaks in the cepstral graph, which correspond to periodic components in the signal; Step S4.4: Calculate the corresponding periodic frequency according to the peak position in the inverse spectrum: Where q is the inverse frequency; Step S4.5: Match the periodic frequency in the inverse spectrum with the known characteristic frequency of the noise source to confirm the location of the noise source.

8. The precise positioning system for the operating noise source of the chassis transmission system of an engineering vehicle according to claim 1 is characterized by: It comprises a signal transceiver unit, which comprises a vibration sensor (1), an acoustic sensor (4), a passive radio frequency tag (2) and an antenna (5); A signal processing unit is included, which processes signals received from the acoustic sensor (4) and the vibration sensor (1); It includes an analysis control unit, which includes a reader and a controller; The guide rail unit comprises a motor, a guide rail (3) and a slide seat (6); The guide rail (3) is installed on the test bench, and the engineering vehicle chassis is placed on the test bench, and the engineering vehicle chassis is located above the guide rail (3); the slide seat (6) is installed on the guide rail (3), and can slide along the guide rail (3) under the drive of the motor; the vibration sensor (1) and the passive radio frequency tag (2) are installed on the key components of the transmission system of the engineering vehicle chassis, and the vibration sensor (1) collects vibration signals of each key component; An acoustic sensor (4), an antenna (5) and a reader are mounted on a slide (6); the antenna (5) receives a signal sent by a passive radio frequency tag (2) in a wireless manner, and the reader receives a signal from the antenna (5); the reader transmits the received signal to an analysis control unit, which analyzes the signal sent by the passive radio frequency tag (2), calculates the position of key components of the transmission system based on the analysis result, and controls the motor through a controller; the motor drives the slide (6) to move to the corresponding position of each key component in turn, so that the acoustic sensor (4) collects the acoustic signal; and then the signal processing unit processes the signals collected by the acoustic sensor (4) and the vibration sensor (1).

9. The precise positioning system for the operating noise source of the chassis transmission system of an engineering vehicle according to claim 8, characterized in that: The test bench is provided with a bump simulation unit, which includes a vibration generator, a vibration collector and a vibration simulation bench; the vibration generator is arranged under the wheel of the engineering vehicle chassis, and the vibration generator can apply vibration to the wheel. When the wheel vibrates, the engineering vehicle chassis is also driven to vibrate through the suspension to simulate the working condition of the engineering vehicle chassis under bumpy conditions; The vibration simulation table is located on the test table, and the guide rail (3) is installed on the vibration simulation table; the vibration collector can collect vibration information of the chassis of the engineering vehicle and transmit the vibration information of the chassis of the engineering vehicle to the vibration simulation table, so that the vibration simulation table drives the guide rail (3) and the chassis of the engineering vehicle to vibrate synchronously, and the acoustic sensor (4) on the guide rail (3) can remain relatively still with the chassis of the engineering vehicle when collecting acoustic signals.

Images