A method and device for identifying abnormal sound sources applied to a vehicle transmission system

Abstract

The present application relates to the technical field of noise diagnosis, in particular to a method and device for identifying abnormal sound source applied to a vehicle transmission system, the method comprising: acquiring abnormal sound noise data, and confirming an abnormal sound generation period according to the abnormal sound noise data; acquiring transmission system vibration data, and performing time-frequency domain analysis on the transmission system vibration data and the abnormal sound noise data according to the abnormal sound generation period to obtain abnormal sound time domain data; the transmission system vibration data is obtained by vibration detection of the vehicle transmission system at a previous time; performing cross-correlation identification of the abnormal sound source according to the abnormal sound time domain data to obtain an abnormal sound source identification result; the abnormal sound source identification result is used to represent that the abnormal sound source is a gearbox and / or a drive axle. By considering the state of strong coupling connection between the gearbox and the drive axle in the vehicle transmission system, cross-correlation analysis is performed on the abnormal sound time domain data to identify the abnormal sound source, thereby improving the accuracy of identifying the abnormal sound source of the vehicle identification system.

Description

Technical Field

[0001] This invention belongs to the field of noise diagnosis technology, specifically relating to a method and device for identifying abnormal noise sources in vehicle transmission systems. Background Technology

[0002] Vehicle systems are complex, and abnormal noises can occur in many locations, primarily including the engine compartment, chassis, and interior. Currently, abnormal noise identification and diagnosis are mainly performed through time-frequency domain analysis of noise signals.

[0003] However, in practice, it has been found that the gearbox and drive axle in the vehicle transmission system are strongly coupled. The vibration characteristics of the gearbox and the drive axle are coupled and influence each other. Traditional abnormal noise identification methods are difficult to distinguish which component in the gearbox or drive axle is the source of the abnormal noise, and the accuracy of abnormal noise source identification is not high.

[0004] Therefore, improving the accuracy of identifying abnormal noise sources in vehicle transmission systems is a pressing technical problem that needs to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide a method and apparatus for identifying abnormal noise sources in vehicle transmission systems, which can improve the accuracy of identifying abnormal noise sources in vehicle transmission systems.

[0006] To address the aforementioned technical problems, the first aspect of this invention discloses a method for identifying abnormal noise sources in a vehicle transmission system, wherein the vehicle transmission system includes a gearbox and a drive axle, and the method includes: Obtain abnormal noise data and determine the time period of abnormal noise based on the abnormal noise data; the abnormal noise data is data obtained by collecting noise from the vehicle cab at a previous time. Vibration data of the transmission system is acquired, and time-frequency domain analysis is performed on the vibration data of the transmission system and the noise data of the abnormal noise according to the time period of the abnormal noise to obtain the time domain data of the abnormal noise; the vibration data of the transmission system is the data obtained by vibration detection of the vehicle transmission system at a previous time. Based on the abnormal noise time-domain data, cross-correlation identification of abnormal noise sources is performed to obtain abnormal noise source identification results; the abnormal noise source identification results are used to characterize the abnormal noise source as the gearbox and / or drive axle.

[0007] As an optional implementation, in the first aspect of the present invention, the step of performing time-frequency domain analysis on the vibration data of the transmission system and the noise data of the abnormal noise based on the time period during which the abnormal noise occurs, to obtain the time-domain data of the abnormal noise, includes: The vibration data of the transmission system is truncated according to the time period in which the abnormal noise occurs, and vibration truncated data is obtained. Based on the time period in which the abnormal noise occurs, the noise data is truncated to obtain truncated noise data. Wavelet analysis was performed on the vibration truncation data and noise truncation data to confirm the target abnormal noise frequency band; The vibration truncated data is bandpass filtered according to the target abnormal noise frequency band to obtain vibration time domain data; The noise truncated data is bandpass filtered according to the target abnormal noise frequency band to obtain noise time domain data; The abnormal noise time-domain data includes vibration time-domain data and noise time-domain data.

[0008] As an optional implementation, in the first aspect of the present invention, the vibration time-domain data includes gearbox time-domain data and drive axle time-domain data; The step of performing cross-correlation identification of abnormal noise sources based on the abnormal noise time-domain data to obtain abnormal noise source identification results includes: Calculate the normalized cross-correlation between the time-domain data of the gearbox and the time-domain data of the noise to obtain the first cross-correlation data; Calculate the normalized cross-correlation between the time-domain data of the drive axle and the time-domain data of the noise to obtain the second cross-correlation data; The abnormal noise source is identified based on the first cross-correlation data and the second cross-correlation data, and the abnormal noise source identification result is obtained.

[0009] As an optional implementation, in the first aspect of the present invention, the step of identifying the abnormal noise source based on the first cross-correlation data and the second cross-correlation data to obtain the abnormal noise source identification result includes: Peak values ​​are filtered from the first cross-correlation data to obtain the first cross-correlation peak value; Peak filtering is performed on the second cross-correlation data to obtain the second cross-correlation peak value; If the first cross-correlation peak value is greater than the second cross-correlation peak value, then the abnormal noise source identification result is obtained, which characterizes the abnormal noise source as the gearbox. If the first cross-correlation peak value is less than the second cross-correlation peak value, then the abnormal noise source identification result is obtained, which characterizes the abnormal noise source as the drive bridge; If the first cross-correlation peak value is equal to the second cross-correlation peak value, then the abnormal noise source identification result is obtained, which characterizes the abnormal noise source as the gearbox and drive axle.

[0010] As an optional implementation, in the first aspect of the present invention, the vibration cutoff data includes gearbox cutoff data and drive axle cutoff data; After performing wavelet analysis on the vibration truncation data and noise truncation data to confirm the target abnormal noise frequency band, the method further includes: The target abnormal noise frequency band is divided into frequency bands according to a preset frequency interval to obtain a target frequency band group; the target frequency band group includes multiple target unit frequency bands. For each target unit frequency band, the gearbox truncated data is bandpass filtered according to the target unit frequency band to obtain first time domain data; the drive axle truncated data is bandpass filtered according to the target unit frequency band to obtain second time domain data; the noise truncated data is bandpass filtered according to the target unit frequency band to obtain third time domain data; the normalized correlation between the first time domain data and the third time domain data is calculated and peak filtering is performed to obtain the first target peak value corresponding to the target unit frequency band; the normalized correlation between the second time domain data and the third time domain data is calculated and peak filtering is performed to obtain the second target peak value corresponding to the target unit frequency band. The target abnormal noise frequency bands are updated based on the first target peak value and the second target peak value corresponding to all the target unit frequency bands to obtain the updated target abnormal noise frequency bands.

[0011] As an optional implementation, in the first aspect of the present invention, updating the target abnormal noise frequency band based on the first target peak value and the second target peak value corresponding to all the target unit frequency bands to obtain the updated target abnormal noise frequency band includes: Based on the first target peak value corresponding to all the target unit frequency bands, curve fitting is performed to obtain the gearbox correlation curve; Based on the second target peak value corresponding to all the target unit frequency bands, curve fitting is performed to obtain the drive bridge correlation curve; The target abnormal noise frequency band is updated by reverse calibration based on the gearbox correlation curve and the drive axle correlation curve to obtain the updated target abnormal noise frequency band.

[0012] As an optional implementation, in the first aspect of the present invention, the step of confirming the time period of abnormal noise generation based on the abnormal noise data includes: The abnormal noise data is sent to the data processing terminal of the noise monitoring personnel. Receive the noise playback results fed back by the noise playback personnel through the data processing terminal; The time period during which the abnormal noise occurred was confirmed based on the noise echo results.

[0013] A second aspect of the present invention discloses an abnormal noise source identification device for a vehicle transmission system, the vehicle transmission system including a gearbox and a drive axle, the device comprising: The abnormal noise period confirmation module is used to acquire abnormal noise data and confirm the period during which the abnormal noise occurs based on the abnormal noise data; the abnormal noise data is data obtained by collecting noise from the vehicle cab at a prior time. The abnormal noise data analysis module is used to acquire vibration data of the transmission system, and perform time-frequency domain analysis on the vibration data of the transmission system and the noise data of the abnormal noise according to the time period of the abnormal noise occurrence to obtain the time domain data of the abnormal noise; the vibration data of the transmission system is the data obtained by vibration detection of the vehicle transmission system at a previous time. An abnormal noise source identification module is used to perform cross-correlation identification of abnormal noise sources based on the abnormal noise time-domain data to obtain abnormal noise source identification results; the abnormal noise source identification results are used to characterize the abnormal noise source as the gearbox and / or drive axle.

[0014] As an optional implementation, in the second aspect of the present invention, the abnormal noise data analysis module performs time-frequency domain analysis on the vibration data of the transmission system and the abnormal noise data according to the time period of the abnormal noise generation, and obtains the abnormal noise time-domain data in the following specific ways: The vibration data of the transmission system is truncated according to the time period in which the abnormal noise occurs, and vibration truncated data is obtained. Based on the time period in which the abnormal noise occurs, the noise data is truncated to obtain truncated noise data. Wavelet analysis was performed on the vibration truncation data and noise truncation data to confirm the target abnormal noise frequency band; The vibration truncated data is bandpass filtered according to the target abnormal noise frequency band to obtain vibration time domain data; The noise truncated data is bandpass filtered according to the target abnormal noise frequency band to obtain noise time domain data; The abnormal noise time-domain data includes vibration time-domain data and noise time-domain data.

[0015] As an optional implementation, in a second aspect of the invention, the vibration time-domain data includes gearbox time-domain data and drive axle time-domain data; The abnormal noise source identification module performs cross-correlation identification of abnormal noise sources based on the abnormal noise time-domain data, and the specific methods for obtaining the abnormal noise source identification results include: Calculate the normalized cross-correlation between the time-domain data of the gearbox and the time-domain data of the noise to obtain the first cross-correlation data; Calculate the normalized cross-correlation between the time-domain data of the drive axle and the time-domain data of the noise to obtain the second cross-correlation data; The abnormal noise source is identified based on the first cross-correlation data and the second cross-correlation data, and the abnormal noise source identification result is obtained.

[0016] As an optional implementation, in a second aspect of the present invention, the abnormal noise source identification module identifies the abnormal noise source based on the first cross-correlation data and the second cross-correlation data, and the specific method for obtaining the abnormal noise source identification result includes: Peak values ​​are filtered from the first cross-correlation data to obtain the first cross-correlation peak value; Peak filtering is performed on the second cross-correlation data to obtain the second cross-correlation peak value; If the first cross-correlation peak value is greater than the second cross-correlation peak value, then the abnormal noise source identification result is obtained, which characterizes the abnormal noise source as the gearbox. If the first cross-correlation peak value is less than the second cross-correlation peak value, then the abnormal noise source identification result is obtained, which characterizes the abnormal noise source as the drive bridge; If the first cross-correlation peak value is equal to the second cross-correlation peak value, then the abnormal noise source identification result is obtained, which characterizes the abnormal noise source as the gearbox and drive axle.

[0017] As an optional implementation, in a second aspect of the invention, the vibration cutoff data includes gearbox cutoff data and drive axle cutoff data; The device further includes: The abnormal noise frequency band division module is used to perform wavelet analysis on the vibration truncation data and noise truncation data in the abnormal noise data analysis module to identify the target abnormal noise frequency band, and then divide the target abnormal noise frequency band equally according to a preset frequency interval to obtain a target frequency band group; the target frequency band group includes multiple target unit frequency bands; The unit frequency band analysis module is used to perform bandpass filtering on the gearbox truncated data to obtain first time domain data for each target unit frequency band, perform bandpass filtering on the drive axle truncated data to obtain second time domain data, perform bandpass filtering on the noise truncated data to obtain third time domain data, calculate the normalized correlation between the first time domain data and the third time domain data and perform peak filtering to obtain a first target peak value corresponding to the target unit frequency band, calculate the normalized correlation between the second time domain data and the third time domain data and perform peak filtering to obtain a second target peak value corresponding to the target unit frequency band; The abnormal noise frequency band update module is used to update the target abnormal noise frequency band according to the first target peak value and the second target peak value corresponding to all the target unit frequency bands, so as to obtain the updated target abnormal noise frequency band.

[0018] As an optional implementation, in the second aspect of the present invention, the specific method by which the abnormal noise frequency band update module updates the target abnormal noise frequency band according to the first target peak value and the second target peak value corresponding to all the target unit frequency bands to obtain the updated target abnormal noise frequency band includes: Based on the first target peak value corresponding to all the target unit frequency bands, curve fitting is performed to obtain the gearbox correlation curve; Based on the second target peak value corresponding to all the target unit frequency bands, curve fitting is performed to obtain the drive bridge correlation curve; The target abnormal noise frequency band is updated by reverse calibration based on the gearbox correlation curve and the drive axle correlation curve to obtain the updated target abnormal noise frequency band.

[0019] As an optional implementation, in a second aspect of the present invention, the specific method by which the abnormal noise period confirmation module confirms the period of abnormal noise generation based on the abnormal noise data includes: The abnormal noise data is sent to the data processing terminal of the noise monitoring personnel. Receive the noise playback results fed back by the noise playback personnel through the data processing terminal; The time period during which the abnormal noise occurred was confirmed based on the noise echo results.

[0020] A third aspect of the present invention discloses another abnormal noise source identification device applied to a vehicle transmission system, the device comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the abnormal noise source identification method for vehicle transmission systems disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked by a processor, are used to execute a method for identifying abnormal noise sources in a vehicle transmission system disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the beneficial effects of the present invention are as follows: The system acquires abnormal noise data and identifies the time period of the abnormal noise based on this data. Then, it acquires transmission system vibration data and performs time-frequency domain analysis on the transmission system vibration and abnormal noise data according to the time period of the abnormal noise to obtain the abnormal noise time-domain data. Finally, it performs cross-correlation identification of the abnormal noise source based on the abnormal noise time-domain data, thereby identifying the specific abnormal noise source component in the vehicle's transmission system. By considering the strong coupling connection between the gearbox and drive axle in the vehicle's transmission system, cross-correlation analysis of the abnormal noise time-domain data is performed to identify the abnormal noise source, thereby improving the accuracy of abnormal noise source identification in the vehicle identification system. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating a method for identifying abnormal noise sources in a vehicle transmission system, as disclosed in an embodiment of the present invention. Figure 2 This is a schematic diagram of the abnormal noise period of an embodiment of the present invention; Figure 3 This is a schematic diagram of vibration cutoff data and noise cutoff data according to an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating how noise-truncated data is used to identify the target abnormal noise frequency band in an embodiment of the present invention. Figure 5 This is a schematic diagram illustrating how a target abnormal noise frequency band is identified by truncation data from a gearbox, according to an embodiment of the present invention. Figure 6 This is a schematic diagram illustrating how a target abnormal noise frequency band is identified by truncation of data via a drive bridge, according to an embodiment of the present invention. Figure 7 This is a schematic diagram of the first cross-correlation data of an embodiment of the present invention; Figure 8 This is a schematic diagram of the second cross-correlation data of an embodiment of the present invention; Figure 9 This is a schematic diagram of the gearbox correlation curve and drive axle correlation curve according to an embodiment of the present invention. Figure 10 This is a schematic diagram of the structure of an abnormal noise source identification device for a vehicle transmission system disclosed in an embodiment of the present invention; Figure 11 This is a schematic diagram of another abnormal noise source identification device for a vehicle transmission system disclosed in an embodiment of the present invention; Figure 12 This is a schematic diagram of another abnormal noise source identification device for vehicle transmission systems disclosed in an embodiment of the present invention. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, or product may include a series of steps or units, or may not be limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or processes.

[0027] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0028] Vehicle systems are complex, and abnormal noises can occur in many locations, primarily including the engine compartment, chassis, and interior. Currently, abnormal noise identification and diagnosis are mainly performed through time-frequency domain analysis of noise signals.

[0029] However, in practice, it has been found that the gearbox and drive axle in the vehicle transmission system are strongly coupled. The vibration characteristics of the gearbox and the drive axle are coupled and influence each other. Traditional abnormal noise identification methods are difficult to distinguish which component in the gearbox or drive axle is the source of the abnormal noise, and the accuracy of abnormal noise source identification is not high.

[0030] Therefore, improving the accuracy of identifying abnormal noise sources in vehicle transmission systems is a pressing technical problem that needs to be solved.

[0031] To address the aforementioned technical problems, this invention discloses a method and apparatus for identifying abnormal noise sources in vehicle transmission systems, aiming to improve the accuracy of abnormal noise source identification in vehicle transmission systems. Detailed descriptions follow.

[0032] Example 1 Please see Figure 1 , Figure 1This is a flowchart illustrating a method for identifying abnormal noise sources in a vehicle transmission system, as disclosed in an embodiment of the present invention. Figure 1 The method shown can be applied to an abnormal noise source identification device, which can improve the accuracy of abnormal noise source identification in a vehicle's transmission system. For example... Figure 1 As shown, the vehicle transmission system includes a gearbox and a drive axle. An embodiment of this invention discloses a method for identifying abnormal noise sources in a vehicle transmission system, including but not limited to the following operations: 101. Obtain abnormal noise data and determine the time period of abnormal noise based on the abnormal noise data; the abnormal noise data is the data obtained by collecting noise from the vehicle cab at a previous time. 102. Obtain vibration data of the transmission system, and perform time-frequency domain analysis on the vibration data and noise data of the transmission system according to the time period of abnormal noise occurrence to obtain the time domain data of abnormal noise; the vibration data of the transmission system is the data obtained by vibration detection of the vehicle transmission system at a previous time. 103. Based on the time-domain data of abnormal noise, perform cross-correlation identification of abnormal noise sources to obtain abnormal noise source identification results; the abnormal noise source identification results are used to characterize the abnormal noise source as the gearbox and / or drive axle.

[0033] It should be noted that the transmission system vibration data includes transmission vibration data obtained from vibration testing of the gearbox and drive axle vibration data obtained from vibration testing of the drive axle. Vibration testing of the drive axle is usually performed at the rear axle location to obtain more representative data.

[0034] In this embodiment of the invention, firstly, abnormal noise data is acquired, and the time period of abnormal noise occurrence is determined based on the abnormal noise data; then, vibration data of the transmission system is acquired, and time-frequency domain analysis is performed on the transmission system vibration data and abnormal noise data according to the time period of abnormal noise occurrence to obtain abnormal noise time-domain data; then, cross-correlation identification of abnormal noise sources is performed based on the abnormal noise time-domain data to identify the specific abnormal noise source component in the vehicle transmission system. By considering the strong coupling connection between the gearbox and drive axle in the vehicle transmission system, cross-correlation analysis is performed on the abnormal noise time-domain data to identify the abnormal noise source, thereby improving the accuracy of abnormal noise source identification in the vehicle identification system.

[0035] In an optional embodiment, time-frequency domain analysis is performed on the transmission system vibration data and abnormal noise data based on the time period in which the abnormal noise occurs to obtain abnormal noise time-domain data, including: The vibration data of the transmission system is truncated according to the time period in which the abnormal noise occurs, and the vibration truncated data is obtained. The abnormal noise data is truncated according to the time period in which the abnormal noise occurs, resulting in truncated noise data. Wavelet analysis was performed on the vibration truncation data and noise truncation data to identify the target abnormal noise frequency band; The vibration truncated data is bandpass filtered according to the target abnormal noise frequency band to obtain the vibration time domain data; Based on the target abnormal noise frequency band, the noise truncated data is bandpass filtered to obtain the noise time domain data; Among them, the abnormal noise time domain data includes vibration time domain data and noise time domain data.

[0036] In this optional embodiment, wavelet analysis is performed on the vibration truncation data and noise truncation data to identify the target abnormal noise frequency band. Specifically, this includes, but is not limited to, the following operations: First, continuous wavelet transform is performed on the gearbox truncation data, drive axle truncation data, and noise truncation data respectively to generate their corresponding time-frequency maps. The continuous wavelet transform uses complex Morlet wavelets as basis functions to obtain the phase and amplitude information of the signal in the time-scale plane. Then, based on the generated time-frequency maps, the instantaneous frequency and instantaneous energy of the gearbox truncation data, drive axle truncation data, and noise truncation data at each time point are extracted, thereby constructing the time-frequency energy spectrum matrices corresponding to the three data sources. Next, using the time-frequency energy spectrum matrix of the noise truncation data as a benchmark, correlation analysis is performed with the time-frequency energy spectrum matrices of the gearbox truncation data and drive axle truncation data respectively, calculating the energy correlation coefficient at each time-frequency point. Based on this, time-frequency points with energy correlation coefficients higher than a preset correlation threshold are selected; these time-frequency points constitute one or more highly correlated regions in the time-frequency domain. Finally, the projections of these highly correlated regions onto the frequency axis are merged and smoothed to determine the continuous frequency range with the widest frequency coverage and highest energy concentration as the target abnormal noise frequency band. By introducing a time-frequency point screening mechanism based on energy correlation coefficient, vibration components that highly match the time-frequency characteristics of abnormal noise in the cab can be more precisely extracted. This makes the locked target abnormal noise frequency band more representative and targeted, effectively eliminating interference from background vibration noise unrelated to the abnormal noise, and providing a more accurate frequency boundary for subsequent bandpass filtering.

[0037] In another optional embodiment, during the process of bandpass filtering the vibration truncated data according to the target abnormal noise frequency band to obtain vibration time-domain data, in order to retain the phase information in the original vibration signal to the greatest extent and avoid the influence of nonlinear phase distortion introduced by the filter on subsequent cross-correlation analysis, zero-phase digital filtering technology is preferably adopted. Specifically, the implementation of this zero-phase digital filtering includes: First, designing a bandpass filter with a specific passband frequency range according to the identified target abnormal noise frequency band. The filter type is an infinite impulse response filter or a finite impulse response filter. Then, passing the vibration truncated data to be filtered (including gearbox truncated data and drive axle truncated data) through the bandpass filter in forward time order to obtain the first filtered sequence. Next, reversing the time of the first filtered sequence, that is, reordering the sequence with the end of the sequence as the new beginning. Then, passing the time-reversed sequence through the bandpass filter again for a second filtering. Finally, reversing the time of the output sequence after the second filtering again to restore its original time order, ultimately obtaining vibration time-domain data with zero phase distortion. By performing the aforementioned zero-phase filtering process on the truncated data of the transmission and the drive axle, respectively, zero-phase distortion time-domain data of the transmission and the drive axle can be obtained. The zero-phase filtering technique ensures that the abnormal noise and impact characteristics in the filtered signal are perfectly aligned with the original signal on the time axis, without any time delay. This is crucial for subsequent cross-correlation analysis, which requires accurate calculation of the time correlation between vibration and noise signals, and significantly improves the accuracy and reliability of cross-correlation peak characterization.

[0038] In yet another optional embodiment, the vibration time-domain data includes gearbox time-domain data and drive axle time-domain data; Based on the time-domain data of abnormal noises, cross-correlation identification of abnormal noise sources is performed to obtain the abnormal noise source identification results, including: Calculate the normalized cross-correlation between the time-domain data of the gearbox and the time-domain data of the noise to obtain the first cross-correlation data; The normalized cross-correlation between the time-domain data of the drive axle and the time-domain data of noise is calculated to obtain the second cross-correlation data. The abnormal noise source is identified based on the first cross-correlation data and the second cross-correlation data, and the abnormal noise source identification result is obtained.

[0039] In this optional embodiment, the normalized cross-correlation of the gearbox time-domain data and the noise time-domain data is calculated to obtain the first cross-correlation data, and the normalized cross-correlation of the drive axle time-domain data and the noise time-domain data is calculated to obtain the second cross-correlation data. To improve computational efficiency and adapt to signal sequences of different lengths, a cross-correlation calculation method based on Fast Fourier Transform (FFT) is specifically adopted. The implementation steps of this method include: First, zero-padding is performed on the gearbox time-domain data, drive axle time-domain data, and noise time-domain data respectively, so that their sequence length is extended to an integer power of 2 that is not less than the sum of the original lengths of the three, to meet the requirements of FFT for data length. Second, FFT is performed on the three zero-padding data sequences respectively to transform them from the time domain to the frequency domain, obtaining the corresponding frequency domain complex sequences. Then, according to the cross-correlation theorem, the conjugates of the frequency domain sequences of the gearbox time-domain data and the noise time-domain data are multiplied point-by-point, and the product is subjected to an inverse fast Fourier transform to obtain the cross-correlation function sequence of the gearbox and noise. Similarly, the conjugates of the frequency domain sequences of the drive axle time-domain data and the noise time-domain data are multiplied point-by-point, and the product is subjected to an inverse fast Fourier transform to obtain the cross-correlation function sequence of the drive axle and noise. Finally, the two cross-correlation function sequences are normalized, for example, by subtracting their mean and dividing by their standard deviation, or by dividing by their maximum amplitude, to eliminate the influence of the signal amplitude dimensions, thus obtaining the normalized first cross-correlation data and the second cross-correlation data. By adopting a frequency domain cross-correlation algorithm based on Fast Fourier Transform, the high-speed operation characteristics of Fast Fourier Transform can be fully utilized, reducing the cross-correlation operation with a computational complexity of O(N²) in the time domain to O(N log N) in the frequency domain. This significantly improves the processing speed under large data volumes and provides technical support for real-time or near-real-time abnormal sound source identification applications.

[0040] In another optional embodiment, abnormal noise source identification is performed based on the first cross-correlation data and the second cross-correlation data to obtain the abnormal noise source identification result, including: Peak values ​​were filtered from the first cross-correlation data to obtain the first cross-correlation peak value; Peak filtering was performed on the second cross-correlation data to obtain the peak value of the second cross-correlation. If the first cross-correlation peak value is greater than the second cross-correlation peak value, then the abnormal noise source identification result is obtained, which characterizes the abnormal noise source as the gearbox. If the first cross-correlation peak value is less than the second cross-correlation peak value, then the abnormal noise source identification result is obtained, which characterizes the abnormal noise source as the drive bridge; If the first cross-correlation peak value is equal to the second cross-correlation peak value, then the abnormal noise source identification result is obtained, which characterizes the abnormal noise source as the gearbox and drive axle.

[0041] It should be noted that the abnormal noise source identification results are used to determine the main noise source that produces abnormal noise in the traditional system, and do not mean that components that are not the main noise source do not produce any abnormal noise at all.

[0042] In yet another optional embodiment, the vibration cutoff data includes gearbox cutoff data and drive axle cutoff data; After performing wavelet analysis on the vibration truncation data and noise truncation data to confirm the target abnormal noise frequency band, the abnormal noise source identification method for vehicle transmission systems disclosed in this embodiment of the invention further includes: The target abnormal noise frequency band is divided equally according to a preset frequency interval to obtain a target frequency band group; the target frequency band group includes multiple target unit frequency bands. For each target unit frequency band, the gearbox truncated data is bandpass filtered according to the target unit frequency band to obtain the first time domain data. The drive axle truncated data is bandpass filtered according to the target unit frequency band to obtain the second time domain data. The noise truncated data is bandpass filtered according to the target unit frequency band to obtain the third time domain data. The normalized correlation between the first time domain data and the third time domain data is calculated and peak filtering is performed to obtain the first target peak value corresponding to the target unit frequency band. The normalized correlation between the second time domain data and the third time domain data is calculated and peak filtering is performed to obtain the second target peak value corresponding to the target unit frequency band. The target abnormal noise frequency band is updated based on the first target peak value and the second target peak value corresponding to all target unit frequency bands to obtain the updated target abnormal noise frequency band.

[0043] In this optional embodiment, cross-correlation analysis with small frequency intervals can more accurately identify the main concentrated frequency bands of abnormal noise characteristics. Furthermore, by adjusting the preset frequency intervals from large to small and updating the target abnormal noise frequency band multiple times, the abnormal noise frequency band can be further precisely located.

[0044] In another optional embodiment, the target abnormal noise frequency band is updated based on the first target peak value and the second target peak value corresponding to all target unit frequency bands to obtain the updated target abnormal noise frequency band, including: Curve fitting is performed based on the first target peak value corresponding to all target unit frequency bands to obtain the gearbox correlation curve; Based on the second target peak value corresponding to all target unit frequency bands, curve fitting is performed to obtain the drive bridge correlation curve; The target abnormal noise frequency band is updated by reverse calibration based on the transmission correlation curve and the drive axle correlation curve to obtain the updated target abnormal noise frequency band.

[0045] In yet another optional embodiment, the method of determining the time period of abnormal noise generation based on abnormal noise data includes: The abnormal noise data is sent to the data processing terminal of the noise monitoring personnel. The noise feedback results are received by the noise feedback personnel through the data processing terminal; The time period of the abnormal noise was determined based on the noise echo results.

[0046] For specific examples, please refer to Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 and Figure 8 , Figure 2 This is a schematic diagram of the abnormal noise period according to an embodiment of the present invention. Figure 3 This is a schematic diagram of vibration cutoff data and noise cutoff data according to an embodiment of the present invention. Figure 4 This is a schematic diagram illustrating an embodiment of the present invention that uses noise-truncated data to identify the target abnormal noise frequency band. Figure 5 This is a schematic diagram illustrating an embodiment of the present invention that uses data truncation from the gearbox to identify the target abnormal noise frequency band. Figure 6 This is a schematic diagram illustrating an embodiment of the present invention that uses a drive bridge to truncate data to identify the target abnormal noise frequency band. Figure 7 This is a schematic diagram of the first cross-correlation data according to an embodiment of the present invention. Figure 8 This is a schematic diagram of the second cross-correlation data of an embodiment of the present invention.

[0047] This embodiment performs vibration detection on the rear axle of the drive axle to obtain vibration data. Figure 2 The red portion represents abnormal noise data, and the horizontal axis data within the gray box represents the corresponding time periods when the abnormal noise occurred. The data after truncating the transmission system vibration and abnormal noise data according to the time periods of abnormal noise occurrence is as follows: Figure 3 As shown, Figure 3 The red portion represents noise-truncation data, the green portion represents transmission-truncation data, and the blue portion represents drive axle-truncation data. Figure 3 A schematic diagram of wavelet analysis performed on the data shown to confirm the target abnormal noise frequency band is as follows. Figure 4 , Figure 5 and Figure 6 As shown, the target abnormal noise frequency band in this embodiment is 500-1024 Hz. Based on the target abnormal noise frequency band... Figure 3 The data in the dataset undergoes bandpass filtering to obtain time-domain data for the transmission, drive axle, and noise, respectively. The normalized cross-correlation between the transmission and noise time-domain data is calculated, and the first cross-correlation data is shown below. Figure 7 As shown, the normalized cross-correlation between the time-domain data of the drive axle and the time-domain data of noise is calculated, and the second cross-correlation data obtained is as follows. Figure 8 As shown. For example, through Figure 7 and Figure 8 The comparison revealed that the correlation between transmission vibration and cab noise was 0.2525, while the correlation between rear axle vibration and cab noise was 0.1412. Therefore, the transmission was considered to be the main noise source of the abnormal noise.

[0048] For further details, please refer to Figure 9 , Figure 9 This is a schematic diagram of the gearbox correlation curve and drive axle correlation curve according to an embodiment of the present invention. Using 100 Hz as a preset frequency interval and taking 1000 Hz as the endpoint frequency of the target abnormal noise frequency band (with the smallest difference from 1024 Hz and an integer multiple of 100 Hz), the frequency band from 500-1000 Hz is divided. The first target peak value and the second target peak value of each small frequency band are calculated, and then curve fitting is performed to obtain the gearbox correlation curve and drive axle correlation curve, as shown below. Figure 9 As shown, with frequency on the horizontal axis and the cross-correlation peak value on the vertical axis, the blue curve represents the gearbox correlation curve, and its expression is:

[0049] The yellow curve represents the drive axle (rear axle) correlation curve, and its expression is as follows:

[0050] The abnormal noise frequency band can be reverse-calibrated based on the gearbox correlation curve and the drive axle correlation curve.

[0051] Example 2 Please see Figure 10 , Figure 10 This is a schematic diagram of the structure of an abnormal noise source identification device for a vehicle transmission system disclosed in an embodiment of the present invention. Figure 10 The apparatus shown can be used to perform the method described in Embodiment 1, which improves the accuracy of identifying abnormal noise sources in a vehicle's transmission system. Figure 10 As shown, the vehicle transmission system includes a gearbox and a drive axle. An abnormal noise source identification device for a vehicle transmission system disclosed in this embodiment of the invention includes, but is not limited to: The abnormal noise period confirmation module 201 is used to acquire abnormal noise data and confirm the period of abnormal noise occurrence based on the abnormal noise data; the abnormal noise data is data obtained by collecting noise from the vehicle cab at a previous time. The abnormal noise data analysis module 202 is used to acquire transmission system vibration data and perform time-frequency domain analysis on the transmission system vibration data and abnormal noise data according to the time period of abnormal noise occurrence to obtain abnormal noise time domain data; the transmission system vibration data is the data obtained by vibration detection of the vehicle transmission system at a previous time. The abnormal noise source identification module 203 is used to identify the cross-correlation of abnormal noise sources based on the abnormal noise time domain data to obtain the abnormal noise source identification result; the abnormal noise source identification result is used to characterize the abnormal noise source as the gearbox and / or drive axle.

[0052] It should be noted that the transmission system vibration data includes transmission vibration data obtained from vibration testing of the gearbox and drive axle vibration data obtained from vibration testing of the drive axle. Vibration testing of the drive axle is usually performed at the rear axle location to obtain more representative data.

[0053] In this embodiment of the invention, firstly, abnormal noise data is acquired, and the time period of abnormal noise occurrence is determined based on the abnormal noise data; then, vibration data of the transmission system is acquired, and time-frequency domain analysis is performed on the transmission system vibration data and abnormal noise data according to the time period of abnormal noise occurrence to obtain abnormal noise time-domain data; then, cross-correlation identification of abnormal noise sources is performed based on the abnormal noise time-domain data to identify the specific abnormal noise source component in the vehicle transmission system. By considering the strong coupling connection between the gearbox and drive axle in the vehicle transmission system, cross-correlation analysis is performed on the abnormal noise time-domain data to identify the abnormal noise source, thereby improving the accuracy of abnormal noise source identification in the vehicle identification system.

[0054] In an optional embodiment, the abnormal noise data analysis module 202 performs time-frequency domain analysis on the transmission system vibration data and abnormal noise data based on the time period in which the abnormal noise occurs, and the specific methods for obtaining the abnormal noise time-domain data include: The vibration data of the transmission system is truncated according to the time period in which the abnormal noise occurs, and the vibration truncated data is obtained. The abnormal noise data is truncated according to the time period in which the abnormal noise occurs, resulting in truncated noise data. Wavelet analysis was performed on the vibration truncation data and noise truncation data to identify the target abnormal noise frequency band; The vibration truncated data is bandpass filtered according to the target abnormal noise frequency band to obtain the vibration time domain data; Based on the target abnormal noise frequency band, the noise truncated data is bandpass filtered to obtain the noise time domain data; Among them, the abnormal noise time domain data includes vibration time domain data and noise time domain data.

[0055] In this optional embodiment, wavelet analysis is performed on the vibration truncation data and noise truncation data to identify the target abnormal noise frequency band. Specifically, this includes, but is not limited to, the following operations: First, continuous wavelet transform is performed on the gearbox truncation data, drive axle truncation data, and noise truncation data respectively to generate their corresponding time-frequency maps. The continuous wavelet transform uses complex Morlet wavelets as basis functions to obtain the phase and amplitude information of the signal in the time-scale plane. Then, based on the generated time-frequency maps, the instantaneous frequency and instantaneous energy of the gearbox truncation data, drive axle truncation data, and noise truncation data at each time point are extracted, thereby constructing the time-frequency energy spectrum matrices corresponding to the three data sources. Next, using the time-frequency energy spectrum matrix of the noise truncation data as a benchmark, correlation analysis is performed with the time-frequency energy spectrum matrices of the gearbox truncation data and drive axle truncation data respectively, calculating the energy correlation coefficient at each time-frequency point. Based on this, time-frequency points with energy correlation coefficients higher than a preset correlation threshold are selected; these time-frequency points constitute one or more highly correlated regions in the time-frequency domain. Finally, the projections of these highly correlated regions onto the frequency axis are merged and smoothed to determine the continuous frequency range with the widest frequency coverage and highest energy concentration as the target abnormal noise frequency band. By introducing a time-frequency point screening mechanism based on energy correlation coefficient, vibration components that highly match the time-frequency characteristics of abnormal noise in the cab can be more precisely extracted. This makes the locked target abnormal noise frequency band more representative and targeted, effectively eliminating interference from background vibration noise unrelated to the abnormal noise, and providing a more accurate frequency boundary for subsequent bandpass filtering.

[0056] In another optional embodiment, during the process of bandpass filtering the vibration truncated data according to the target abnormal noise frequency band to obtain vibration time-domain data, in order to retain the phase information in the original vibration signal to the greatest extent and avoid the influence of nonlinear phase distortion introduced by the filter on subsequent cross-correlation analysis, zero-phase digital filtering technology is preferably adopted. Specifically, the implementation of this zero-phase digital filtering includes: First, designing a bandpass filter with a specific passband frequency range according to the identified target abnormal noise frequency band. The filter type is an infinite impulse response filter or a finite impulse response filter. Then, passing the vibration truncated data to be filtered (including gearbox truncated data and drive axle truncated data) through the bandpass filter in forward time order to obtain the first filtered sequence. Next, reversing the time of the first filtered sequence, that is, reordering the sequence with the end of the sequence as the new beginning. Then, passing the time-reversed sequence through the bandpass filter again for a second filtering. Finally, reversing the time of the output sequence after the second filtering again to restore its original time order, ultimately obtaining vibration time-domain data with zero phase distortion. By performing the aforementioned zero-phase filtering process on the truncated data of the transmission and the drive axle, respectively, zero-phase distortion time-domain data of the transmission and the drive axle can be obtained. The zero-phase filtering technique ensures that the abnormal noise and impact characteristics in the filtered signal are perfectly aligned with the original signal on the time axis, without any time delay. This is crucial for subsequent cross-correlation analysis, which requires accurate calculation of the time correlation between vibration and noise signals, and significantly improves the accuracy and reliability of cross-correlation peak characterization.

[0057] In yet another optional embodiment, the vibration time-domain data includes gearbox time-domain data and drive axle time-domain data; The abnormal noise source identification module 203 performs cross-correlation identification of abnormal noise sources based on the abnormal noise time-domain data, and the specific methods for obtaining the abnormal noise source identification results include: Calculate the normalized cross-correlation between the time-domain data of the gearbox and the time-domain data of the noise to obtain the first cross-correlation data; The normalized cross-correlation between the time-domain data of the drive axle and the time-domain data of noise is calculated to obtain the second cross-correlation data. The abnormal noise source is identified based on the first cross-correlation data and the second cross-correlation data, and the abnormal noise source identification result is obtained.

[0058] In this optional embodiment, the normalized cross-correlation of the gearbox time-domain data and the noise time-domain data is calculated to obtain the first cross-correlation data, and the normalized cross-correlation of the drive axle time-domain data and the noise time-domain data is calculated to obtain the second cross-correlation data. To improve computational efficiency and adapt to signal sequences of different lengths, a cross-correlation calculation method based on Fast Fourier Transform (FFT) is specifically adopted. The implementation steps of this method include: First, zero-padding is performed on the gearbox time-domain data, drive axle time-domain data, and noise time-domain data respectively, so that their sequence length is extended to an integer power of 2 that is not less than the sum of the original lengths of the three, to meet the requirements of FFT for data length. Second, FFT is performed on the three zero-padding data sequences respectively to transform them from the time domain to the frequency domain, obtaining the corresponding frequency domain complex sequences. Then, according to the cross-correlation theorem, the conjugates of the frequency domain sequences of the gearbox time-domain data and the noise time-domain data are multiplied point-by-point, and the product is subjected to an inverse fast Fourier transform to obtain the cross-correlation function sequence of the gearbox and noise. Similarly, the conjugates of the frequency domain sequences of the drive axle time-domain data and the noise time-domain data are multiplied point-by-point, and the product is subjected to an inverse fast Fourier transform to obtain the cross-correlation function sequence of the drive axle and noise. Finally, the two cross-correlation function sequences are normalized, for example, by subtracting their mean and dividing by their standard deviation, or by dividing by their maximum amplitude, to eliminate the influence of the signal amplitude dimensions, thus obtaining the normalized first cross-correlation data and the second cross-correlation data. By adopting a frequency domain cross-correlation algorithm based on Fast Fourier Transform, the high-speed operation characteristics of Fast Fourier Transform can be fully utilized, reducing the cross-correlation operation with a computational complexity of O(N²) in the time domain to O(N log N) in the frequency domain. This significantly improves the processing speed under large data volumes and provides technical support for real-time or near-real-time abnormal sound source identification applications.

[0059] In another optional embodiment, the abnormal noise source identification module 203 identifies the abnormal noise source based on the first cross-correlation data and the second cross-correlation data, and the specific method for obtaining the abnormal noise source identification result includes: Peak values ​​were filtered from the first cross-correlation data to obtain the first cross-correlation peak value; Peak filtering was performed on the second cross-correlation data to obtain the peak value of the second cross-correlation. If the first cross-correlation peak value is greater than the second cross-correlation peak value, then the abnormal noise source identification result is obtained, which characterizes the abnormal noise source as the gearbox. If the first cross-correlation peak value is less than the second cross-correlation peak value, then the abnormal noise source identification result is obtained, which characterizes the abnormal noise source as the drive bridge; If the first cross-correlation peak value is equal to the second cross-correlation peak value, then the abnormal noise source identification result is obtained, which characterizes the abnormal noise source as the gearbox and drive axle.

[0060] It should be noted that the abnormal noise source identification results are used to determine the main noise source that produces abnormal noise in the traditional system, and do not mean that components that are not the main noise source do not produce any abnormal noise at all.

[0061] In yet another optional embodiment, the vibration cutoff data includes gearbox cutoff data and drive axle cutoff data; Please see Figure 11 , Figure 11 This is a schematic diagram of another abnormal noise source identification device applied to a vehicle transmission system disclosed in an embodiment of the present invention. The device disclosed in this embodiment of the present invention also includes: The abnormal noise frequency band division module 204 is used to perform wavelet analysis on the vibration truncation data and noise truncation data in the abnormal noise data analysis module 202 to confirm the target abnormal noise frequency band, and then divide the target abnormal noise frequency band equally according to the preset frequency interval to obtain the target frequency band group; the target frequency band group includes multiple target unit frequency bands; The unit frequency band analysis module 205 is used to perform bandpass filtering on the gearbox truncated data to obtain first time domain data for each target unit frequency band, perform bandpass filtering on the drive axle truncated data to obtain second time domain data, perform bandpass filtering on the noise truncated data to obtain third time domain data, calculate the normalized correlation between the first time domain data and the third time domain data and perform peak filtering to obtain the first target peak value corresponding to the target unit frequency band, calculate the normalized correlation between the second time domain data and the third time domain data and perform peak filtering to obtain the second target peak value corresponding to the target unit frequency band. The abnormal noise frequency band update module 206 is used to update the target abnormal noise frequency band according to the first target peak value and the second target peak value corresponding to all target unit frequency bands, so as to obtain the updated target abnormal noise frequency band.

[0062] In this optional embodiment, cross-correlation analysis with small frequency intervals can more accurately identify the main concentrated frequency bands of abnormal noise characteristics. Furthermore, by adjusting the preset frequency intervals from large to small and updating the target abnormal noise frequency band multiple times, the abnormal noise frequency band can be further precisely located.

[0063] In another optional embodiment, the abnormal noise frequency band update module 206 updates the target abnormal noise frequency band according to the first target peak value and the second target peak value corresponding to all target unit frequency bands, and the specific method for obtaining the updated target abnormal noise frequency band includes: Curve fitting is performed based on the first target peak value corresponding to all target unit frequency bands to obtain the gearbox correlation curve; Based on the second target peak value corresponding to all target unit frequency bands, curve fitting is performed to obtain the drive bridge correlation curve; The target abnormal noise frequency band is updated by reverse calibration based on the transmission correlation curve and the drive axle correlation curve to obtain the updated target abnormal noise frequency band.

[0064] In yet another optional embodiment, the specific method by which the abnormal noise period confirmation module 201 confirms the period during which the abnormal noise occurs based on the abnormal noise data includes: The abnormal noise data is sent to the data processing terminal of the noise monitoring personnel. The noise feedback results are received by the noise feedback personnel through the data processing terminal; The time period of the abnormal noise was determined based on the noise echo results.

[0065] For specific examples, please refer to Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 and Figure 8 , Figure 2 This is a schematic diagram of the abnormal noise period according to an embodiment of the present invention. Figure 3 This is a schematic diagram of vibration cutoff data and noise cutoff data according to an embodiment of the present invention. Figure 4 This is a schematic diagram illustrating an embodiment of the present invention that uses noise-truncated data to identify the target abnormal noise frequency band. Figure 5 This is a schematic diagram illustrating an embodiment of the present invention that uses data truncation from the gearbox to identify the target abnormal noise frequency band. Figure 6 This is a schematic diagram illustrating an embodiment of the present invention that uses a drive bridge to truncate data to identify the target abnormal noise frequency band. Figure 7 This is a schematic diagram of the first cross-correlation data according to an embodiment of the present invention. Figure 8 This is a schematic diagram of the second cross-correlation data of an embodiment of the present invention.

[0066] This embodiment performs vibration detection on the rear axle of the drive axle to obtain vibration data. Figure 2 The red portion represents abnormal noise data, and the horizontal axis data within the gray box represents the corresponding time periods when the abnormal noise occurred. The data after truncating the transmission system vibration and abnormal noise data according to the time periods of abnormal noise occurrence is as follows: Figure 3 As shown, Figure 3 The red portion represents noise-truncation data, the green portion represents transmission-truncation data, and the blue portion represents drive axle-truncation data. Figure 3 A schematic diagram of wavelet analysis performed on the data shown to confirm the target abnormal noise frequency band is as follows. Figure 4 , Figure 5 and Figure 6 As shown, the target abnormal noise frequency band in this embodiment is 500-1024 Hz. Based on the target abnormal noise frequency band... Figure 3The data in the dataset undergoes bandpass filtering to obtain time-domain data for the transmission, drive axle, and noise, respectively. The normalized cross-correlation between the transmission and noise time-domain data is calculated, and the first cross-correlation data is shown below. Figure 7 As shown, the normalized cross-correlation between the time-domain data of the drive axle and the time-domain data of noise is calculated, and the second cross-correlation data obtained is as follows. Figure 8 As shown. For example, through Figure 7 and Figure 8 The comparison revealed that the correlation between transmission vibration and cab noise was 0.2525, while the correlation between rear axle vibration and cab noise was 0.1412. Therefore, the transmission was considered to be the main noise source of the abnormal noise.

[0067] For further details, please refer to Figure 9 , Figure 9 This is a schematic diagram of the gearbox correlation curve and drive axle correlation curve according to an embodiment of the present invention. Using 100 Hz as a preset frequency interval and taking 1000 Hz as the endpoint frequency of the target abnormal noise frequency band (with the smallest difference from 1024 Hz and an integer multiple of 100 Hz), the frequency band from 500-1000 Hz is divided. The first target peak value and the second target peak value of each small frequency band are calculated, and then curve fitting is performed to obtain the gearbox correlation curve and drive axle correlation curve, as shown below. Figure 9 As shown, with frequency on the horizontal axis and the cross-correlation peak value on the vertical axis, the blue curve represents the gearbox correlation curve, and its expression is:

[0068] The yellow curve represents the drive axle (rear axle) correlation curve, and its expression is as follows:

[0069] The abnormal noise frequency band can be reverse-calibrated based on the gearbox correlation curve and the drive axle correlation curve.

[0070] Example 3 Please see Figure 12 , Figure 12 This is a schematic diagram of another abnormal noise source identification device for vehicle transmission systems disclosed in an embodiment of the present invention. Figure 12 The apparatus shown can be used to perform the method described in Embodiment 1, which improves the accuracy of identifying abnormal noise sources in a vehicle's transmission system. Figure 12 As shown, the vehicle transmission system includes a gearbox and a drive axle. An abnormal noise source identification device for a vehicle transmission system disclosed in this embodiment of the invention includes, but is not limited to: Memory 301 storing executable program code; Processor 302 coupled to memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute some or all of the steps in the abnormal noise source identification method applied to the vehicle transmission system described in Embodiment 1 of the present invention.

[0071] Example 4 This invention discloses a computer storage medium storing computer instructions. When the computer instructions are invoked by a processor, they are used to execute some or all of the steps in the abnormal noise source identification method applied to a vehicle transmission system described in Embodiment 1 of this invention.

[0072] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules. They may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0073] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0074] Finally, it should be noted that the technical content disclosed in the embodiments of the present invention is only a preferred embodiment of the present invention and is only used to illustrate the technical solutions of the present invention, and not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying abnormal noise sources in a vehicle transmission system, characterized in that, The vehicle transmission system includes a gearbox and a drive axle, and the method includes: Acquire abnormal noise data and determine the time period of abnormal noise occurrence based on the abnormal noise data; the abnormal noise data is data obtained by collecting noise from the vehicle cab at a previous time. Vibration data of the transmission system is acquired, and time-frequency domain analysis is performed on the vibration data of the transmission system and the noise data of the abnormal noise according to the time period of the abnormal noise to obtain the time domain data of the abnormal noise; the vibration data of the transmission system is the data obtained by vibration detection of the vehicle transmission system at a previous time. Based on the abnormal noise time-domain data, cross-correlation identification of abnormal noise sources is performed to obtain abnormal noise source identification results; the abnormal noise source identification results are used to characterize the abnormal noise source as the gearbox and / or drive axle.

2. The method for identifying abnormal noise sources in a vehicle transmission system according to claim 1, characterized in that, The step of performing time-frequency domain analysis on the vibration data of the transmission system and the noise data of the abnormal noise based on the time period of the abnormal noise generation to obtain the time-domain data of the abnormal noise includes: The vibration data of the transmission system is truncated according to the time period in which the abnormal noise occurs, and vibration truncated data is obtained. Based on the time period in which the abnormal noise occurs, the noise data is truncated to obtain truncated noise data. Wavelet analysis was performed on the vibration truncation data and noise truncation data to confirm the target abnormal noise frequency band; The vibration truncated data is bandpass filtered according to the target abnormal noise frequency band to obtain vibration time domain data; The noise truncated data is bandpass filtered according to the target abnormal noise frequency band to obtain noise time domain data; The abnormal noise time-domain data includes vibration time-domain data and noise time-domain data.

3. The method for identifying abnormal noise sources in a vehicle transmission system according to claim 2, characterized in that, The vibration time-domain data includes gearbox time-domain data and drive axle time-domain data; The step of performing cross-correlation identification of abnormal noise sources based on the abnormal noise time-domain data to obtain abnormal noise source identification results includes: Calculate the normalized cross-correlation between the time-domain data of the gearbox and the time-domain data of the noise to obtain the first cross-correlation data; Calculate the normalized cross-correlation between the time-domain data of the drive axle and the time-domain data of the noise to obtain the second cross-correlation data; The abnormal noise source is identified based on the first cross-correlation data and the second cross-correlation data, and the abnormal noise source identification result is obtained.

4. The method for identifying abnormal noise sources in a vehicle transmission system according to claim 3, characterized in that, The step of identifying the abnormal noise source based on the first cross-correlation data and the second cross-correlation data to obtain the abnormal noise source identification result includes: Peak values ​​are filtered from the first cross-correlation data to obtain the first cross-correlation peak value; Peak filtering is performed on the second cross-correlation data to obtain the second cross-correlation peak value; If the first cross-correlation peak value is greater than the second cross-correlation peak value, then the abnormal noise source identification result is obtained, which characterizes the abnormal noise source as the gearbox. If the first cross-correlation peak value is less than the second cross-correlation peak value, then the abnormal noise source identification result is obtained, which characterizes the abnormal noise source as the drive bridge; If the first cross-correlation peak value is equal to the second cross-correlation peak value, then the abnormal noise source identification result is obtained, which characterizes the abnormal noise source as the gearbox and drive axle.

5. The method for identifying abnormal noise sources in a vehicle transmission system according to claim 2, characterized in that, The vibration cutoff data includes gearbox cutoff data and drive axle cutoff data; After performing wavelet analysis on the vibration truncation data and noise truncation data to confirm the target abnormal noise frequency band, the method further includes: The target abnormal noise frequency band is divided into frequency bands according to a preset frequency interval to obtain a target frequency band group; the target frequency band group includes multiple target unit frequency bands. For each target unit frequency band, the gearbox truncated data is bandpass filtered according to the target unit frequency band to obtain first time domain data; the drive axle truncated data is bandpass filtered according to the target unit frequency band to obtain second time domain data; the noise truncated data is bandpass filtered according to the target unit frequency band to obtain third time domain data; the normalized correlation between the first time domain data and the third time domain data is calculated and peak filtering is performed to obtain the first target peak value corresponding to the target unit frequency band; the normalized correlation between the second time domain data and the third time domain data is calculated and peak filtering is performed to obtain the second target peak value corresponding to the target unit frequency band. The target abnormal noise frequency bands are updated based on the first target peak value and the second target peak value corresponding to all the target unit frequency bands to obtain the updated target abnormal noise frequency bands.

6. The method for identifying abnormal noise sources in a vehicle transmission system according to claim 5, characterized in that, The step of updating the target abnormal noise frequency band based on the first target peak value and the second target peak value corresponding to all the target unit frequency bands to obtain the updated target abnormal noise frequency band includes: Based on the first target peak value corresponding to all the target unit frequency bands, curve fitting is performed to obtain the gearbox correlation curve; Based on the second target peak value corresponding to all the target unit frequency bands, curve fitting is performed to obtain the drive bridge correlation curve; The target abnormal noise frequency band is updated by reverse calibration based on the gearbox correlation curve and the drive axle correlation curve to obtain the updated target abnormal noise frequency band.

7. A method for identifying abnormal noise sources in a vehicle transmission system according to any one of claims 1 to 6, characterized in that, The step of confirming the time period of the abnormal noise based on the abnormal noise data includes: The abnormal noise data is sent to the data processing terminal of the noise monitoring personnel. Receive the noise playback results fed back by the noise playback personnel through the data processing terminal; The time period during which the abnormal noise occurred was confirmed based on the noise echo results.

8. A device for identifying abnormal noise sources in a vehicle transmission system, characterized in that, The vehicle transmission system includes a gearbox and a drive axle, and the device includes: The abnormal noise period confirmation module is used to acquire abnormal noise data and confirm the period during which the abnormal noise occurs based on the abnormal noise data; the abnormal noise data is data obtained by collecting noise from the vehicle cab at a prior time. The abnormal noise data analysis module is used to acquire vibration data of the transmission system, and perform time-frequency domain analysis on the vibration data of the transmission system and the noise data of the abnormal noise according to the time period of the abnormal noise occurrence to obtain the time domain data of the abnormal noise; the vibration data of the transmission system is the data obtained by vibration detection of the vehicle transmission system at a previous time. An abnormal noise source identification module is used to perform cross-correlation identification of abnormal noise sources based on the abnormal noise time-domain data to obtain abnormal noise source identification results; the abnormal noise source identification results are used to characterize the abnormal noise source as the gearbox and / or drive axle.

9. A device for identifying abnormal noise sources in a vehicle transmission system, characterized in that, The vehicle transmission system includes a gearbox and a drive axle, and the device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the abnormal noise source identification method applied to a vehicle transmission system according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked by the processor, are used to execute the abnormal noise source identification method applied to a vehicle transmission system as described in any one of claims 1 to 7.

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