Rear axle noise quality on-line detection system

Abstract

The invention discloses a rear axle noise quality online detection system, and relates to the technical field of rear axle noise detection, and the system comprises a static analysis module which comprises a static noise analysis unit, a static operation analysis unit and a rear axle static quality analysis unit; according to the static noise abnormal coefficients and the static operation abnormal coefficients of all the automobiles in the static state, a rear axle static noise correlation index is obtained through calculation; the dynamic analysis module comprises a dynamic noise analysis unit, a dynamic operation analysis unit and a rear axle dynamic quality analysis unit; according to the dynamic noise abnormal coefficients and the dynamic operation abnormal coefficients under all the automobile dynamic states, a rear axle dynamic noise correlation index is obtained through calculation; and the comprehensive analysis module calculates according to the static noise abnormity coefficient, the rear axle static noise correlation index, the dynamic noise abnormity coefficient and the rear axle dynamic noise correlation index to obtain a rear axle comprehensive noise quality index, and adopts a corresponding early warning strategy. And the noise performance of the rear axle under different working conditions can be known.

Description

Technical Field

[0001] The present invention relates to the technical field of rear axle noise detection, and specifically to an on-line detection system for rear axle noise quality. Background Art

[0002] In the modern automobile manufacturing and maintenance industries, the quality detection of vehicle noise has become increasingly important. In particular, rear axle noise (also known as differential noise) has an important impact on the overall performance of the vehicle and the user experience. Rear axle noise is the sound wave generated by components such as differentials, gears, and bearings during operation. Excessive noise not only affects driving comfort but may also indicate mechanical failures and even potential safety hazards. Traditional rear axle noise detection methods mostly use manual detection or static tests in a laboratory environment. These methods usually require disassembling the vehicle or parking it on a special test bench, which is cumbersome and time-consuming. Moreover, due to environmental factors and the uncertainty of human operation, the reliability and consistency of test results are affected to a certain extent. These limitations make traditional detection methods unable to meet the high requirements for efficiency and accuracy in modern automobile production lines. With the rapid development of sensor technology, data acquisition, and processing technology, an on-line monitoring-based rear axle noise detection system has emerged. This system installs highly sensitive microphones or vibration sensors at different parts of the vehicle to collect noise data during the operation of the rear axle in real time. Combining advanced signal processing and analysis technologies, such as Fourier transform, wavelet transform, and machine learning algorithms, it can quickly and accurately identify the noise source, intensity, and spectral characteristics.

[0003] In the Chinese invention application with the publication number CN1908611A, an on-line detection system for rear axle noise quality is disclosed, including an on-line detection system for rear axle noise quality, which is characterized by consisting of a "T" type test bench, a data acquisition and processing system, and a control system; the front end of the "T" type test bench is a rigid platform designed for acoustic purposes, which can effectively prevent the leakage and absorption of axle noise; two lifting platforms are installed on the rigid plane to realize the attitude adjustment of the axle to be measured and adapt to the measurement of different types of axles; the rear power slide can be conveniently connected to the axle to be measured and provide effective power; the entire test bench has a compact structure and is easy to operate.

[0004] In the above invention application, the control system is set up with a "master-slave" structure. The system works stably and reliably, has strong anti-interference ability, and is suitable for operation in industrial field environments. By adopting an interlocking circuit, one frequency converter drives four motors, which can save costs and reduce the overall cost of the system. However, it can still only be used for detection in the production line, and cannot detect the noise quality of the rear axle during the actual operation of the rear axle. This may lead to the noise problem that occurs during the use of the vehicle cannot be discovered and solved in time, and only the noise of the rear axle itself is considered, and the influence of other factors on the noise of the rear axle is not considered. For example, bearing wear, poor gear meshing or insufficient lubrication will affect the noise of the rear axle itself. Due to the failure to monitor other key parameters related to the noise of the rear axle in real time, the system may not be able to detect potential problems in time, resulting in a lag in the early warning signal. This lag may make the problem worse, increase maintenance costs and downtime.

[0005] To this end, the present invention provides an online detection system for rear axle noise quality. Summary of the invention

[0006] 1. Technical issues to be resolved In view of the shortcomings of the prior art, the present invention provides an online detection system for rear axle noise quality. , Rear axle static noise related index , Dynamic noise anomaly coefficient , Rear axle dynamic noise related index Calculate the rear axle comprehensive noise quality index , and taking corresponding early warning strategies can more comprehensively reflect the noise performance of the rear axle system, help to more accurately understand the noise performance of the rear axle under different working conditions, provide a basis for subsequent improvements and optimizations, guide the formulation of repair and maintenance plans, help to extend the service life of the rear axle, and improve the reliability and safety of vehicle operation, thereby solving the technical problems recorded in the background technology.

[0007] (II) Technical solution To achieve the above objectives, the present invention is implemented through the following technical solutions: a rear axle noise quality online detection system, comprising: Static analysis module, including static noise analysis unit, static operation analysis unit and rear axle static quality analysis unit; static noise analysis unit is used to analyze the rear axle noise of the vehicle under static state and obtain the static noise abnormality coefficient Jz The static operation analysis unit is used to analyze the rear axle operation data of the vehicle under static conditions and obtain the static operation abnormality coefficient. Jy ; The rear axle static quality analysis unit is based on the static noise anomaly coefficient of all vehicles in static state and static operation abnormality coefficient , calculate and obtain the rear axle static noise - related index ; The dynamic analysis module includes a dynamic noise analysis unit, a dynamic operation analysis unit, and a rear axle dynamic quality analysis unit; the dynamic noise analysis unit is used to analyze the rear axle noise under the dynamic state of the vehicle to obtain the dynamic noise anomaly coefficient Dz ; the dynamic operation analysis unit is used to analyze the rear axle operation data under the dynamic state of the vehicle to obtain the dynamic operation anomaly coefficient Dy ; the rear axle dynamic quality analysis unit calculates and obtains the rear axle dynamic noise - related index based on all the dynamic noise anomaly coefficients and the dynamic operation anomaly coefficients under the dynamic state of the vehicle ; The comprehensive analysis module calculates and obtains the rear axle comprehensive noise quality index based on the static noise anomaly coefficient , the rear axle static noise - related index , the dynamic noise anomaly coefficient , and the rear axle dynamic noise - related index , and adopts corresponding warning strategies.

[0008] Furthermore, use a microphone to capture the noise signal generated by the rear axle system when the vehicle is started but not moving, record it as static noise data, use audio - processing software to split the static noise data into multiple frames, and extract the static average decibel , the static decibel variance , the static average frequency , and the static frequency variance , and calculate the static noise anomaly coefficient Jz:

[0009] where, represents the standard value of the decibel of the rear axle static noise data, represents the standard value of the frequency of the rear axle static noise data, and the standard value is the mean value obtained by statistical analysis of the rear axle static noise data of the vehicle.

[0010] Furthermore, use a load sensor to periodically detect the load of the rear axle under static conditions to obtain the maximum static load , and use a temperature sensor to periodically detect the temperature of the rear axle under static conditions to obtain the maximum static temperature , and calculate the static operation anomaly coefficient Jy:

[0011] where, represents the standard value of the rear axle static temperature, Represents the standard value of the rear axle static load, which is the mean value obtained through statistical analysis of the rear axle operation data of all vehicles in a static state.

[0012] Furthermore, obtain the static noise anomaly coefficient of all vehicles in a static state and the static operation anomaly coefficient , and calculate the rear axle static noise correlation index :

[0013] Among them, i represents the number of the static noise data of all vehicles in a static state, i = 1, 2, …, n .

[0014] Furthermore, use a microphone to capture the noise signal generated by the rear axle system of the vehicle when driving at a constant speed on the test road surface, record it as dynamic noise data, use audio processing software to divide the dynamic noise data into multiple frames, and extract the dynamic average decibel , the dynamic decibel variance , the dynamic average frequency and the dynamic frequency variance , and calculate the dynamic noise anomaly coefficient D z:

[0015] Among them, represents the standard value of the decibel of the rear axle dynamic noise data, represents the standard value of the frequency of the rear axle dynamic noise data, and the standard value is the mean value obtained through statistical analysis of the dynamic noise data of all vehicles driving at a constant speed on the same test road surface at the same speed.

[0016] Furthermore, collect the vibration data of the rear axle under dynamic conditions through a vibration sensor, including the vibration average frequency Zp and the vibration frequency variance , and calculate the dynamic operation anomaly coefficient Dy:

[0017] Among them, represents the standard value of the vibration average frequency Zp under the dynamic conditions of the rear axle, and the standard value is the mean value obtained through statistical analysis of the rear axle operation data of all vehicles under dynamic conditions.

[0018] Furthermore, obtain the dynamic noise anomaly coefficient and the dynamic operation anomaly coefficient of all vehicles under dynamic conditions, and calculate the rear axle dynamic noise correlation index :

[0019] The corresponding rear axle dynamic noise related index The calculation formula is as above.

[0020] Furthermore, obtain the static noise anomaly coefficient , the rear axle static noise related index , the dynamic noise anomaly coefficient , the rear axle dynamic noise related index , and calculate the rear axle comprehensive noise quality index :

[0021] When the rear axle comprehensive noise quality index is within the range, continue to maintain monitoring. When the rear axle comprehensive noise quality index exceeds the range, send out an early warning of abnormal rear axle noise.

[0022] (III) Beneficial effects The present invention provides an on-line detection system for the rear axle noise quality, having the following beneficial effects: 1. By analyzing the rear axle noise of the vehicle in the static state, obtain the static noise anomaly coefficient Jz, analyze the rear axle operation data of the vehicle in the static state, obtain the static operation anomaly coefficient Jy, and calculate the rear axle static noise related index based on the static noise anomaly coefficient and the static operation anomaly coefficient of all vehicles in the static state. The static noise anomaly coefficient Jz can reflect whether the noise level of the rear axle components is abnormal in the static state, while the static operation anomaly coefficient Jy can reveal whether the operation of the rear axle system is smooth in the static state. By analyzing the correlation between the static noise anomaly coefficient Jz and the static operation anomaly coefficient Jy, it is possible to help identify specific fault modes of the rear axle according to the analyzed noise data, such as bearing wear, poor gear meshing or insufficient lubrication, etc., which helps to arrange maintenance plans in advance, reduce unplanned downtime, and reduce maintenance costs.

[0023] 2. By analyzing the rear axle noise of the vehicle in the dynamic state, obtain the dynamic noise anomaly coefficient Dz , analyze the rear axle operation data of the vehicle in the dynamic state, obtain the dynamic operation anomaly coefficient Dy , and calculate the rear axle dynamic noise related index based on the dynamic noise anomaly coefficient and the dynamic operation anomaly coefficient of all vehicles in the dynamic state . The dynamic noise anomaly coefficient Dz can immediately reflect whether the noise level of the rear axle is abnormal under dynamic conditions, while the dynamic operation anomaly coefficient Dy can reveal whether the operation of the rear axle is stable during driving. By analyzing the correlation between the dynamic noise anomaly coefficient and the dynamic operation anomaly coefficient , potential faults of the rear axle system can be predicted, and preventive maintenance measures can be taken in advance. This helps to extend the service life of the rear axle, reduce maintenance costs, and improve the overall reliability of the vehicle.

[0024] 3. Based on the static noise anomaly coefficient , the rear axle static noise correlation index , the dynamic noise anomaly coefficient , and the rear axle dynamic noise correlation index , the rear axle comprehensive noise quality index is calculated, and corresponding warning strategies are adopted, which can more comprehensively reflect the noise performance of the rear axle system, help to more accurately understand the noise performance of the rear axle under different working conditions, provide a basis for subsequent improvement and optimization, guide the formulation of maintenance and repair plans, help to extend the service life of the rear axle, and improve the reliability and safety of vehicle operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 FIG. is a schematic structural diagram of an on-line detection system for the noise quality of a rear axle according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0026] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0027] Please refer to Figure 1 , the present invention provides an on-line detection system for the noise quality of a rear axle, including: A static analysis module, including a static noise analysis unit, a static operation analysis unit, and a rear axle static quality analysis unit. The static noise analysis unit is used to analyze the rear axle noise when the vehicle is static to obtain the static noise anomaly coefficient Jz . The static operation analysis unit is used to analyze the rear axle operation data when the vehicle is static to obtain the static operation anomaly coefficient Jy . The rear axle static quality analysis unit calculates the rear axle static noise correlation index based on all the static noise anomaly coefficients and static operation anomaly coefficients when the vehicle is static.

[0028] Use a microphone to capture the noise signal generated by the rear axle system when the vehicle is started but not moving, which is recorded as static noise data. Use audio processing software to split the static noise data into multiple frames and extract the static average decibel , static decibel variance , static average frequency and static frequency variance , and calculate the static noise anomaly coefficient Jz:

[0029] Among them, represents the standard value of the decibel of the rear axle static noise data, represents the standard value of the frequency of the rear axle static noise data. The standard value is the mean value obtained by statistical analysis of the static noise data of the vehicle rear axle under static conditions.

[0030] Use a load sensor to periodically detect the load of the rear axle under static conditions to obtain the maximum static load , and use a temperature sensor to periodically detect the temperature of the rear axle under static conditions to obtain the maximum static temperature , and calculate the static operation anomaly coefficient Jy:

[0031] Among them, represents the standard value of the static temperature of the rear axle, represents the standard value of the static load of the rear axle. The standard value is the mean value obtained by statistical analysis of the rear axle operation data of all vehicles under static conditions.

[0032] Obtain the static noise anomaly coefficients and static operation anomaly coefficients of all vehicles under static conditions, and calculate the rear axle static noise related index :

[0033] Among them, i represents the number of the static noise data of all vehicles under static conditions, i = 1, 2, …, n .

[0034] By analyzing the rear axle noise of the vehicle in a static state, the static noise anomaly coefficient Jz is obtained. By analyzing the rear axle operation data of the vehicle in a static state, the static operation anomaly coefficient Jy is obtained. Based on the static noise anomaly coefficient and the static operation anomaly coefficient of all vehicles in a static state, the rear axle static noise related index is calculated. The static noise anomaly coefficient Jz can reflect whether the noise level of the rear axle components is abnormal in the static state, while the static operation anomaly coefficient Jy can reveal whether the operation of the rear axle system is smooth in the static state. By analyzing the correlation between the static noise anomaly coefficient Jz and the static operation anomaly coefficient Jy, it is possible to help identify specific fault modes of the rear axle based on the analyzed noise data, such as bearing wear, poor gear meshing, or insufficient lubrication, etc., which helps to arrange maintenance plans in advance, reduce unplanned downtime, and lower maintenance costs.

[0035] The dynamic analysis module includes a dynamic noise analysis unit, a dynamic operation analysis unit, and a rear axle dynamic quality analysis unit. The dynamic noise analysis unit is used to analyze the rear axle noise of the vehicle in a dynamic state to obtain the dynamic noise anomaly coefficient Dz The dynamic operation analysis unit is used to analyze the rear axle operation data of the vehicle in a dynamic state to obtain the dynamic operation anomaly coefficient Dy The rear axle dynamic quality analysis unit is based on the dynamic noise anomaly coefficient and the dynamic operation anomaly coefficient of all vehicles in a dynamic state, and calculates the rear axle dynamic noise related index

[0036] Use a microphone to capture the noise signal generated by the rear axle system of the vehicle when driving at a constant speed on the test road surface, which is recorded as dynamic noise data. Use audio processing software to divide the dynamic noise data into multiple frames, and extract the dynamic average decibel , the dynamic decibel variance , the dynamic average frequency and the dynamic frequency variance , and calculate the dynamic noise anomaly coefficient D z:

[0037] where represents the standard value of the decibel of the rear axle dynamic noise data, represents the standard value of the frequency of the rear axle dynamic noise data, and the standard value is the mean value obtained by statistically analyzing the dynamic noise data of all vehicles driving at a constant speed on the same test road surface at the same speed.

[0038] Collect the vibration data of the rear axle in a dynamic state through a vibration sensor, including the vibration average frequency Zp and the vibration frequency variance , and calculate the dynamic operation anomaly coefficient Dy:​

[0039] Among them, represents the standard value of the average vibration frequency of the rear axle under dynamic conditions, and the standard value is the average value obtained by statistically analyzing the running data of the rear axles of all vehicles under dynamic conditions. Zp

[0040] Obtain the dynamic noise anomaly coefficient of all vehicles under dynamic conditions and the dynamic running anomaly coefficient to calculate the rear axle dynamic noise related index :

[0041] The corresponding calculation formula of the rear axle dynamic noise related index is as above.

[0042] Dz By analyzing the rear axle noise of the vehicle under dynamic conditions, the dynamic noise anomaly coefficient Dy is obtained. By analyzing the running data of the rear axle of the vehicle under dynamic conditions, the dynamic running anomaly coefficient is obtained. And based on the dynamic noise anomaly coefficient of all vehicles under dynamic conditions and the dynamic running anomaly coefficient to calculate the rear axle dynamic noise related index . The dynamic noise anomaly coefficient Dz can immediately reflect whether the noise level of the rear axle under dynamic conditions is abnormal, while the dynamic running anomaly coefficient Dy can reveal whether the running condition of the rear axle during driving is stable. By analyzing the correlation between the dynamic noise anomaly coefficient and the dynamic running anomaly coefficient , potential faults of the rear axle system can be predicted, and preventive maintenance measures can be taken in advance. This helps to extend the service life of the rear axle, reduce maintenance costs, and improve the overall reliability of the vehicle.

[0043] The comprehensive analysis module calculates the rear axle comprehensive noise quality index based on the static noise anomaly coefficient , the rear axle static noise related index , the dynamic noise anomaly coefficient , and the rear axle dynamic noise related index , and adopts the corresponding warning strategy.

[0044] Obtain the static noise anomaly coefficient , the rear axle static noise related index , the dynamic noise anomaly coefficient , the rear axle dynamic noise related index to calculate the rear axle comprehensive noise quality index :

[0045] When the comprehensive noise quality index of the rear axle is located within the range, it indicates that the noise quality of the rear axle is good, the rear axle of the vehicle is operating normally, and the monitoring continues. When the comprehensive noise quality index of the rear axle exceeds the range, it indicates that the noise quality of the rear axle is poor, there are problems with the operation of the rear axle of the vehicle, and an abnormal warning of the rear axle noise is sent outwards.

[0046] Based on the static noise abnormality coefficient , the rear axle static noise correlation index , the dynamic noise abnormality coefficient , and the rear axle dynamic noise correlation index , the comprehensive noise quality index of the rear axle is calculated, and corresponding warning strategies are adopted, which can more comprehensively reflect the noise performance of the rear axle system, help to more accurately understand the noise performance of the rear axle under different working conditions, provide a basis for subsequent improvement and optimization, guide the formulation of maintenance and repair plans, help to extend the service life of the rear axle, and improve the reliability and safety of vehicle operation.

[0047] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution.

[0048] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0049] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application.

Claims

1. A rear axle noise quality online detection system, characterized by: include: Static analysis module, including static noise analysis unit, static operation analysis unit and rear axle static quality analysis unit; The static noise analysis unit is used to analyze the rear axle noise of the vehicle in a static state and obtain the static noise abnormality coefficient. Jz The static operation analysis unit is used to analyze the rear axle operation data of the vehicle under static conditions and obtain the static operation abnormality coefficient. Jy ; The rear axle static quality analysis unit is based on the static noise anomaly coefficient of all vehicles in static state and static operation abnormality coefficient , calculate the rear axle static noise correlation index ; Dynamic analysis module, including dynamic noise analysis unit, dynamic operation analysis unit and rear axle dynamic quality analysis unit; dynamic noise analysis unit is used to analyze the rear axle noise under vehicle dynamics and obtain the dynamic noise abnormality coefficient Dz The dynamic operation analysis unit is used to analyze the rear axle operation data under vehicle dynamics and obtain the dynamic operation abnormality coefficient. Dy ; Rear axle dynamic quality analysis unit based on the dynamic noise anomaly coefficient of all vehicle dynamics and dynamic operation abnormality coefficient , calculate the rear axle dynamic noise correlation index ; Comprehensive analysis module, based on static noise anomaly coefficient , Rear axle static noise related index , Dynamic noise anomaly coefficient , Rear axle dynamic noise related index Calculate the rear axle comprehensive noise quality index , and adopt corresponding early warning strategies.

2. The rear axle noise quality online detection system according to claim 1, characterized in that: A microphone is used to capture the noise signal generated by the rear axle system when the car is started but not driving, which is recorded as static noise data. The static noise data is divided into multiple frames using audio processing software to extract the static average decibel. , static decibel variance , static average frequency and static frequency variance , calculate the static noise anomaly coefficient Jz: in, Indicates the standard value of the static noise data decibel of the rear axle, It represents the standard value of the frequency of static noise data of the rear axle. The standard value is the mean value obtained by statistical analysis of the static noise data of the rear axle of the vehicle under static conditions.

3. The rear axle noise quality online detection system according to claim 1, characterized in that: Use load cell to periodically detect the static load of rear axle to obtain the maximum static load The temperature sensor is used to periodically detect the static temperature of the rear axle to obtain the maximum static temperature. , calculate the static operation abnormality coefficient Jy: in, Indicates the standard value of the static temperature of the rear axle, It represents the standard value of the static load on the rear axle. The standard value is the mean value obtained by statistically analyzing the rear axle operating data of all vehicles under static conditions.

4. The rear axle noise quality online detection system according to claim 3, characterized in that: Get the static noise anomaly coefficient of all cars in static state and static operation abnormality coefficient , calculate the rear axle static noise correlation index : in, i Indicates the number of static noise data of all cars in static state. i=1, 2, …, n .

5. The rear axle noise quality online detection system according to claim 1, characterized in that: A microphone is used to capture the noise signal generated by the rear axle system of the car when it is driving at a constant speed on the test road, which is recorded as dynamic noise data. The dynamic noise data is divided into multiple frames using audio processing software, and the dynamic average decibel is extracted. , Dynamic decibel variance , Dynamic Average Frequency and dynamic frequency variance , calculate the dynamic noise anomaly coefficient D z: in, Indicates the standard value of the dynamic noise data of the rear axle in decibels. It represents the standard value of the frequency of dynamic noise data of the rear axle. The standard value is the mean value obtained by statistically analyzing the dynamic noise data of all vehicles driving at the same speed on the same test road.

6. The rear axle noise quality online detection system according to claim 1, characterized in that: Vibration sensors are used to collect dynamic vibration data of the rear axle, including the average vibration frequency. Z and vibration frequency variance , calculate the dynamic operation abnormality coefficient Dy: in, Indicates the average frequency of dynamic vibration of the rear axle Z The standard value is the mean value obtained by statistically analyzing the rear axle operating data under all vehicle dynamics.

7. The rear axle noise quality online detection system according to claim 6, characterized in that: Get the dynamic noise anomaly coefficient of all vehicle dynamics and dynamic operation abnormality coefficient , calculate the rear axle dynamic noise correlation index : Corresponding rear axle dynamic noise related index The calculation formula is as above.

8. The rear axle noise quality online detection system according to claim 7, characterized in that: Get the static noise anomaly coefficient , Rear axle static noise related index , Dynamic noise anomaly coefficient , Rear axle dynamic noise related index , calculate the comprehensive noise quality index of the rear axle : When the rear axle comprehensive noise quality index lie in Within the range, continue to monitor; when the rear axle comprehensive noise quality index Beyond range, and issue an abnormal rear axle noise warning.

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