Rear axle noise testing method and system

Abstract

The invention relates to the technical field of rear axle testing, and discloses a rear axle noise testing method and system, and the method comprises the steps: simulating the actual operation states of a vehicle under different working conditions; converting the loading force into a loading force simulation noise signal; synchronously acquiring noise signals of a plurality of positions; fusing the plurality of noise signals; the correlation between the loading force and the noise mode is calculated; dynamic spectrum characteristics of the rear axle total noise signals are extracted; and calculating a noise comprehensive score. Compared with the technical problems in the prior art that the real loading condition of the vehicle cannot be accurately simulated under the dynamic working condition and the correlation analysis of the noise signal and the loading force signal is lacked, the dynamic loading force model and the comprehensive scoring algorithm are introduced, so that the quantitative correlation analysis between the loading force and the noise characteristic is realized; therefore, the problems that the loading force is discontinuous and only noise signal analysis has high limitation in the existing test method are solved, and the authenticity of working condition simulation of the rear axle noise test is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of rear axle testing, and in particular relates to a rear axle noise testing method and system. Background Art

[0002] At present, the rear axle noise test plays an important role in research and development and testing, and is used to analyze the vibration and noise characteristics of the rear axle under different operating conditions. However, there are still many deficiencies in the accurate analysis of the loading force and noise signal under dynamic conditions in the prior art. For example, the traditional noise test method usually adopts a constant load or a simplified loading force model, which cannot accurately simulate the real power input of the vehicle under complex operating conditions such as acceleration, deceleration, and climbing; in addition, the existing noise signal analysis mainly relies on the spectrum analysis method, ignoring the correlation between the loading force and the noise signal, resulting in low accuracy in noise source positioning and noise characteristic analysis. The prior art cannot fully meet the demand for synchronous analysis of loading force and noise signal under dynamic loading conditions. Therefore, there is an urgent need for a method and system that can accurately simulate the dynamic loading force under complex conditions, and can still realize the synchronous acquisition, correlation analysis and dynamic spectrum characteristic extraction of loading force and noise signal, so as to improve the accuracy of rear axle noise test, the authenticity of working condition simulation and the comprehensiveness of data analysis, and provide a reliable basis for the optimization design and improvement of rear axles. Summary of the invention

[0003] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to propose a rear axle noise testing method, aiming to solve the technical problems in the prior art that the actual loading conditions of the vehicle cannot be accurately simulated under dynamic conditions, and there is a lack of correlation analysis between the noise signal and the loading force signal.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides a rear axle noise testing method,

[0005] The rear axle noise testing method comprises:

[0006] Step S10: Sending a load force F(t) application instruction to the rear axle test bench based on the dynamic load force simulator. The load force F(t) is used to simulate the actual operating condition of the vehicle at time t. The calculation formula is:

[0007]

[0008] Among them, A0 is the basic loading force, the unit is N, which is used to simulate the constant power input when the vehicle is traveling at a constant speed; is the vehicle's acceleration in m / s 2, calculated based on the time variation of vehicle speed; β1 is the weight coefficient of the load force response to vehicle acceleration; G(t) is the vehicle slope load, in N, G(t) = m g sin(θ(t)), m is the vehicle mass, g is the acceleration of gravity, θ(t) is the dynamic slope angle of the vehicle when running; β2 is the weight coefficient of the load force response to the slope load; f load (t) is the loading force frequency, which is calculated based on the engine speed and gear ratio; R(t) is the preset random disturbance term; φ is the phase offset of the loading force, in radians;

[0009] Step S20: Convert the dynamic loading force F(t) into a loading force simulation noise signal S through a physical correlation model of loading force and noise characteristics sim (t), the physical correlation model adopts the formula:

[0010]

[0011] Among them, α is the influence coefficient of the amplitude of the loading force; γ is the influence coefficient of the rate of change of the loading force; is the instantaneous rate of change of the loading force; δ is the noise offset constant, which is used to compensate for the inherent noise of the dynamic loading force simulator;

[0012] Sensors are arranged at multiple positions of the rear axle test bench to collect noise signals at multiple positions, and the noise signals at multiple positions are fused to obtain the total noise signal S of the rear axle. total (t);

[0013] Step S30: Decomposing the rear axle total noise signal S total (t) is decomposed into K modal functions u k (t), the calculation formula is:

[0014]

[0015] Among them, u k (t) is the kth mode function; r(t) is the residual signal;

[0016] Calculate the load force to simulate the noise signal S sim (t) with each mode u k The correlation coefficient R of (t) k , the formula is:

[0017]

[0018] Where ∫dt represents the integral over time t;

[0019] Correlation coefficient R k Perform weighted summation to obtain the weighted value R of the loading force correlation coefficient sim ;

[0020] Step S40: Extracting the total noise signal S of the rear axle total (t) noise dynamic spectrum characteristics, including the main frequency spectrum characteristics f peak (t), power density spectrum characteristics H(f), frequency dispersion spectrum characteristics D f ;

[0021] Step S50: Based on the extracted noise dynamic spectrum characteristics and correlation coefficient weighting value R sim A comprehensive scoring model is constructed to calculate the noise comprehensive score N, and the formula is:

[0022] N=w1·SPL avg +w2·D f +w3·F peak +w4·R sim

[0023] Among them, SPL avg is the average sound pressure level; w1, w2, w3, w4 are the weighting coefficients of the comprehensive scoring model, F peak The main frequency spectrum characteristic f peak (t) maximum peak value;

[0024] Set the low score threshold N low and the scoring threshold N medium , judge the test results:

[0025] When N≤N low , the test result is "excellent";

[0026] When N low <N≤N medium , the test result is "qualified";

[0027] When N>N medium , the test result is "failed".

[0028] Preferably, in step S10, the loading force frequency f load The calculation formula of (t) is:

[0029]

[0030] Among them, ω engine (t) is the engine speed, in rad / s; r gear is the transmission ratio of the current gear. Preferably, in step S10, the formula of the preset random disturbance term R(t) is:

[0031] R(t)=γ2·sin(2πf r t)+η(t)

[0032] Among them, γ2 is the preset disturbance amplitude; f r is the preset disturbance frequency; η(t) is the random noise of the preset normal distribution.

[0033] Preferably, in step S20, sensors are arranged at multiple positions of the rear axle stand, including:

[0034] Vibration sensors, installed at gear meshing points and bearing nodes, are used to collect structural vibration signals;

[0035] A sound pressure sensor, mounted outside the rear axle housing, records airborne noise;

[0036] The reference sensor is installed at the support point of the test bench and is used to collect the background noise of the test bench.

[0037] Preferably, in step S30, the weighted value R of the loading force correlation coefficient sim The calculation formula is:

[0038]

[0039] Among them, w k is the modal weighting coefficient, which indicates the contribution of each mode to the noise characteristics.

[0040] Preferably, step S40 includes:

[0041] Main frequency spectrum characteristics f peak The extraction formula of (t) is:

[0042] f peak (t) = argmax(S(f,t))

[0043] Among them, S(f,t) is the time-frequency spectrum density, which is obtained from the total noise signal S of the rear axle by short-time Fourier transform. total (t), argmax(S(f,t)) represents the frequency that makes the spectrum density S(f,t) reach the maximum value;

[0044] The extraction formula of power density spectrum characteristic H(f) is:

[0045]

[0046] Among them, FFT (S sim S(t)) is the frequency spectrum of the noise signal simulated by the loading force; S(f) is the frequency spectrum of the total noise signal of the rear axle;

[0047] Frequency dispersion spectrum characteristics D f The extraction formula is:

[0048]

[0049] Among them, f i is the ith frequency component in the total noise signal of the rear axle, f avg is the mean value of the frequency components in the total noise signal of the rear axle.

[0050] Preferably, in step S50, the average sound pressure level SPL avg The calculation formula is:

[0051]

[0052] Among them, p(t) is the instantaneous sound pressure of the noise signal, in Pa, which comes from the total noise signal of the rear axle; p0 is the reference sound pressure, the default value is 20μPa, and T is the preset test time length.

[0053] The present invention also provides a rear axle noise testing system comprising:

[0054] The loading force application module is used to send a loading force F(t) application instruction to the rear axle test bench based on the dynamic loading force simulator. The loading force F(t) is used to simulate the actual operating condition of the vehicle at time t. The calculation formula is:

[0055]

[0056] Among them, A0 is the basic loading force, the unit is N, which is used to simulate the constant power input when the vehicle is traveling at a constant speed; is the vehicle's acceleration in m / s 2 , calculated based on the time variation of vehicle speed; β1 is the weight coefficient of the load force response to vehicle acceleration; G(t) is the vehicle slope load, in N, G(t) = m g sin(θ(t)), m is the vehicle mass, g is the acceleration of gravity, θ(t) is the dynamic slope angle of the vehicle when running; β2 is the weight coefficient of the load force response to the slope load; f load (t) is the loading force frequency, which is calculated based on the engine speed and gear ratio; R(t) is the preset random disturbance term; φ is the phase offset of the loading force, in radians;

[0057] The simulation noise signal generation module is used to convert the dynamic loading force F(t) into the loading force simulation noise signal S through the physical correlation model of the loading force and the noise characteristics. sim (t), the physical correlation model adopts the formula:

[0058]

[0059] Among them, α is the influence coefficient of the amplitude of the loading force; γ is the influence coefficient of the rate of change of the loading force; is the instantaneous rate of change of the loading force; δ is the noise offset constant, which is used to compensate for the inherent noise of the dynamic loading force simulator;

[0060] Sensors are arranged at multiple positions of the rear axle test bench to collect noise signals at multiple positions, and the noise signals at multiple positions are fused to obtain the total noise signal S of the rear axle. total (t);

[0061] The signal decomposition and correlation calculation module is used to convert the rear axle total noise signal S total (t) is decomposed into K modal functions u k (t), the calculation formula is:

[0062]

[0063] Among them, u k (t) is the kth mode function; r(t) is the residual signal;

[0064] Calculate the load force to simulate the noise signal S sim (t) with each mode u k The correlation coefficient R of (t) k , the formula is:

[0065]

[0066] Where ∫dt represents the integral over time t;

[0067] Correlation coefficient R k Perform weighted summation to obtain the weighted value R of the loading force correlation coefficient sim ;

[0068] The state spectrum characteristic extraction module is used to extract the total noise signal S of the rear axle total (t) noise dynamic spectrum characteristics, including the main frequency spectrum characteristics f peak (t), power density spectrum characteristics H(f), frequency dispersion spectrum characteristics D f ;

[0069] Noise test comprehensive scoring module, used to extract noise dynamic spectrum characteristics and correlation coefficient weighted value R sim A comprehensive scoring model is constructed to calculate the noise comprehensive score N, and the formula is:

[0070] N=w1·SPL avg +w2·D f +w3·F peak +w4·R sim

[0071] Among them, SPL avg is the average sound pressure level; w1, w2, w3, w4 are the weighting coefficients of the comprehensive scoring model, F peak The main frequency spectrum characteristic fpeak (t) maximum peak value;

[0072] Set the low score threshold N low and the scoring threshold N medium , judge the test results:

[0073] When N≤N low , the test result is "excellent";

[0074] When N low <N≤N medium , the test result is "qualified";

[0075] When N>N medium , the test result is "failed".

[0076] The present invention also provides a computer program product, comprising a rear axle noise test program, wherein the rear axle noise test program implements the rear axle noise test method when executed by a processor.

[0077] The beneficial effect of the present invention is that compared with the technical problems in the prior art that the actual loading conditions of the vehicle cannot be accurately simulated under dynamic working conditions and the noise signal and the loading force signal lack correlation analysis, the present application realizes the quantitative correlation analysis between the loading force and the noise characteristics by introducing a dynamic loading force model and a comprehensive scoring algorithm, thereby avoiding the problems of discontinuous loading force and high limitations of only noise signal analysis in the existing test methods, and improving the authenticity of the working condition simulation of the rear axle noise test. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0079] Figure 1 The figure is a flow chart of a first embodiment of a rear axle noise testing method of the present invention.

[0080] Figure 2 The figure is a schematic diagram of equipment for a rear axle noise testing method of the present invention. DETAILED DESCRIPTION

[0081] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0082] Embodiment 1: Figure 1 , which is a flow chart of the first embodiment of the rear axle noise testing method of the present invention, provides the first embodiment of the rear axle noise testing method of the present invention.

[0083] In a first embodiment, the rear axle noise testing method includes:

[0084] Step S10: Sending a load force F(t) application instruction to the rear axle test bench based on the dynamic load force simulator. The load force F(t) is used to simulate the actual operating condition of the vehicle at time t. The calculation formula is:

[0085]

[0086] Among them, A0 is the basic loading force, the unit is N, which is used to simulate the constant power input when the vehicle is traveling at a constant speed; is the vehicle's acceleration in m / s 2 , calculated based on the time variation of vehicle speed; β1 is the weight coefficient of the load force response to vehicle acceleration; G(t) is the vehicle slope load, in N, G(t) = m g sin(θ(t)), m is the vehicle mass, g is the acceleration of gravity, θ(t) is the dynamic slope angle of the vehicle when running; β2 is the weight coefficient of the load force response to the slope load; f load (t) is the loading force frequency, which is calculated based on the engine speed and gear ratio; R(t) is the preset random disturbance term; φ is the phase offset of the loading force, in radians;

[0087] It should be noted that the dynamic loading force formula is based on vehicle dynamics theory and is designed in combination with the actual vehicle operating conditions such as acceleration, deceleration, and climbing. It can more realistically simulate the stress of the rear axle test bench under complex conditions. The influence of vehicle acceleration and slope load is added to the formula to reflect the dynamic characteristics of the loading force. The random disturbance term simulates the complex vibration environment introduced by the actual road surface and loading system, further enhancing the authenticity of the test conditions. Weight coefficient β d It is used to measure the influence of vehicle acceleration on loading force. Different vehicles, such as cars and trucks, have different dynamic responses. dThe value of needs to be adjusted according to the actual vehicle type; β2 is used to measure the influence of slope load on loading force. For example, when the slope is large, β2 will make a major contribution to the loading force; the loading force frequency is the vibration frequency of the loading force, which is directly calculated by the engine speed and the gear ratio, and can accurately reflect the periodic characteristics of the loading force; by associating the loading force frequency with the engine operating conditions, the power input characteristics of the vehicle under different gears and speeds can be more realistically simulated; the introduction of random disturbance terms can simulate nonlinear factors in complex test environments, such as random vibration caused by uneven road surfaces, noise interference from the loading system, etc.; by designing the amplitude and frequency of the preset random disturbance terms, the test complexity can be adjusted according to the test requirements.

[0088] For example, in a uniform driving scenario, the vehicle travels at a constant speed v = 30m / s with no acceleration. The road slope is 0°, the basic load force A0 = 500N, the random disturbance R(t) = 50·sin(2π·5t), and the calculation F(t) = [A0+β d ·0+β2·(1200·9.8·sin(0°))]·sin(2π·f load (t)·t+φ)+R(t)=500·sin(2π·f load (t)·t+φ)+50·sin(2π·5t); In the climbing acceleration scenario, the vehicle accelerates from v=20m / s to v=30m / s, the acceleration The road slope is 10°, the basic load force A0=500N, β1=1.5, β2=0.8, the vehicle mass m=1200kg, the gravity acceleration g=9.8m / s 2 , random disturbance R(t) = 30·sin(2π·10t), calculate G(t) = 1200·9.8·sin(10°) = 2044.3N, F(t) = [500+1.5·2+0.8·2044.3]·sin(2π·f load (t)·t+φ)+30·sin(2π·10t), F(t)=2136.3·sin(2π·f load (t)·t+φ)+30·sin(2π·10t).

[0089] Step S20: Convert the dynamic loading force F(t) into a loading force simulation noise signal S through a physical correlation model of loading force and noise characteristics sim (t), the physical correlation model adopts the formula:

[0090]

[0091] Among them, α is the influence coefficient of the amplitude of the loading force; γ is the influence coefficient of the rate of change of the loading force; is the instantaneous rate of change of the loading force; δ is the noise offset constant, which is used to compensate for the inherent noise of the dynamic loading force simulator;

[0092] Sensors are arranged at multiple positions of the rear axle test bench to collect noise signals at multiple positions, and the noise signals at multiple positions are fused to obtain the total noise signal S of the rear axle. total (t);

[0093] It should be noted that the formula is used to convert the dynamic loading force into a loading force simulation noise signal. Its purpose is to associate the dynamic mechanical characteristics with the noise characteristics and quantify the contribution of the loading force to the noise signal. α reflects the influence of the amplitude of the loading force on the noise signal. For example, a larger loading force amplitude will cause greater structural noise. γ reflects the dynamic influence of the loading force change rate on the noise signal. For example, a rapid change in the loading force will cause high-frequency vibration.

[0094] It should be noted that sensors such as vibration sensors and sound pressure sensors are arranged at multiple positions on the rear axle test bench in order to comprehensively capture the distribution of noise signals under the action of loading force; the noise signals at multiple positions are integrated to obtain the total noise signal of the rear axle, which aims to reflect the overall noise performance of the rear axle under the action of loading force; the fusion design should consider the weight distribution of different sensor signals according to the signal strength or position importance, and remove the interference of background noise and non-target noise.

[0095] For example, the dynamic loading force formula is F(t) = 500·sin(2π·10t) + 30·sin(2π·50t), where the main loading frequency is 10Hz, the random perturbation frequency is 50Hz, the parameters α = 1.2, γ = 0.8, δ = 5, and the loading force simulation noise signal is calculated.

[0096]

[0097] For example, assume that sensors are arranged at three positions of the rear axle frame, and the following noise signals are recorded respectively: Position 1 (gear meshing point): S1(t) = 60·sin(2π·10t) + 5; Position 2 (rear axle frame support point): S2(t) = 50·sin(2π·10t) + 3; Position 3 (outside the rear axle housing): S3(t) = 55·sin(2π·10t) + 4; The three signals are fused by weighted average method to obtain the total noise signal of the rear axle: Among them, w1=0.4, w2=0.3, w3=0.3.

[0098] Step S30: Decomposing the rear axle total noise signal S total (t) is decomposed into K modal functions u k(t), the calculation formula is:

[0099]

[0100] Among them, u k (t) is the kth mode function; r(t) is the residual signal;

[0101] Calculate the load force to simulate the noise signal S sim (t) with each mode u k The correlation coefficient R of (t) k , the formula is:

[0102]

[0103] Where ∫dt represents the integral over time t;

[0104] Correlation coefficient R k Perform weighted summation to obtain the weighted value R of the loading force correlation coefficient sim ;

[0105] It should be noted that the modal decomposition method is used to decompose the total noise signal of the rear axle into a modal function and a residual signal. Its core function is to extract the main modal characteristics of the rear axle noise. The modal function corresponds to different frequency components or noise sources in the signal, such as gear meshing noise, bearing vibration noise, etc. The correlation coefficient is used to quantify the correlation between the loading force simulation noise signal and each mode. k The higher the value of R is, the more the noise signal of this mode is affected by the loading force. k The lower the value, the more likely the mode is dominated by other noise sources such as background noise. The loading force correlation weighted value is a comprehensive index obtained by weighted summation of the correlation coefficients of all modes.

[0106] Step S40: Extracting the total noise signal S of the rear axle total (t) noise dynamic spectrum characteristics, including the main frequency spectrum characteristics f peak (t), power density spectrum characteristics H(f), frequency dispersion spectrum characteristics D f ;

[0107] It should be noted that the main frequency characteristics are used to quickly identify the dominant source of noise; the power density spectrum characteristics are used to deeply analyze the energy distribution of frequency components; and the frequency dispersion can help determine the complexity of the noise signal and whether there are multiple noise sources.

[0108] Step S50: Based on the extracted noise dynamic spectrum characteristics and correlation coefficient weighting value R sim A comprehensive scoring model is constructed to calculate the noise comprehensive score N, and the formula is:

[0109] N=w1·SPL avg +w2·D f +w3·F peak +w4·R sim

[0110] Among them, SPL avg is the average sound pressure level; w1, w2, w3, w4 are the weighting coefficients of the comprehensive scoring model, F peak The main frequency spectrum characteristic f peak (t) maximum peak value;

[0111] Set the low score threshold N low and the scoring threshold N medium , judge the test results:

[0112] When N≤N low , the test result is "excellent";

[0113] When N low <N≤N medium , the test result is "qualified";

[0114] When N>N medium , the test result is "failed".

[0115] It should be noted that the comprehensive score is a dimensionless value obtained by weighted calculation of noise characteristics and loading force correlation, which is used to quantify the overall performance of rear axle test bench noise; the average sound pressure level reflects the overall strength of the noise signal; the frequency dispersion reflects the distribution range and complexity of the noise frequency components; the main frequency power density reflects the energy of the main frequency of the noise; the loading force correlation weighted value reflects the strength of the relationship between the loading force and the noise signal; by weighted combination of these characteristics, the performance of the rear axle noise can be comprehensively evaluated.

[0116] For example, suppose the noise characteristic extraction result is the average sound pressure level SPL a vg=70dB, frequency dispersion D f =55Hz, main frequency power density P peak =90dB, load force correlation weighted value R sim =0.75, weighting coefficients are set to w1=0.3, w2=0.2, w3=0.3, w4=0.2, threshold N low =60,N medium =80, N = w1·SPL avg +w2·D f +w3·P peak +w4·R sim=0.3·70+0.2·55+0.3·90+0.2·0.75=21+11+27+0.15=59.15; comprehensive score N=59.15, satisfying N≤N l ow, the test result is judged to be excellent.

[0117] In addition, a rear axle noise test system provided by the present invention adopts a rear axle noise test method in the above embodiment, which can solve a technical problem of rear axle noise test. Compared with the prior art, the beneficial effects of a rear axle noise test system provided by the present invention are the same as the beneficial effects of a rear axle noise test method provided by the above embodiment, and other technical features of the rear axle noise test system are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0118] The present invention provides a rear axle noise testing device, please refer to Figure 2A rear axle noise test device includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a rear axle noise test method in the above-mentioned embodiment 1. A rear axle noise test device in the embodiment of the present invention may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. A rear axle noise test device is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention. A rear axle noise test device may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM) 1004. In RAM1004, various programs and data required for the operation of a rear axle noise test device are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow a rear axle noise test device to communicate wirelessly or wired with other devices to exchange data. Although a rear axle noise test device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0119] The present invention also provides a computer program product, including a computer program, wherein when the computer program is executed by a processor, the steps of the above-mentioned rear axle noise test method are implemented. The computer program product provided by the present invention can solve the technical problem of a rear axle noise test. Compared with the prior art, the beneficial effects of the computer program product provided by the present invention are the same as the beneficial effects of the rear axle noise test method provided by the above-mentioned embodiment, which will not be described in detail here.

[0120] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through a communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present invention are executed.

[0121] It should be understood that the various parts disclosed in the present invention can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0122] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A rear axle noise testing method, characterized in that: Methods include: Step S10: Sending a load force F(t) application instruction to the rear axle test bench based on the dynamic load force simulator. The load force F(t) is used to simulate the actual operating condition of the vehicle at time t. The calculation formula is: Among them, A0 is the basic loading force, the unit is N, which is used to simulate the constant power input when the vehicle is traveling at a constant speed; is the vehicle's acceleration in m / s 2 , calculated based on the time variation of vehicle speed; β1 is the weight coefficient of the load force response to vehicle acceleration; G(t) is the vehicle slope load, in N, G(t) = m g sin(θ(t)), m is the vehicle mass, g is the acceleration of gravity, θ(t) is the dynamic slope angle of the vehicle when running; β2 is the weight coefficient of the load force response to the slope load; f load (t) is the loading force frequency, which is calculated based on the engine speed and gear ratio; R(t) is the preset random disturbance term; φ is the phase offset of the loading force, in radians; Step S20: Convert the dynamic loading force F(t) into a loading force simulation noise signal S through a physical correlation model of loading force and noise characteristics sim (t), the physical correlation model adopts the formula: Among them, α is the influence coefficient of the amplitude of the loading force; γ is the influence coefficient of the rate of change of the loading force; is the instantaneous rate of change of the loading force; δ is the noise offset constant, which is used to compensate for the inherent noise of the dynamic loading force simulator; Sensors are arranged at multiple positions of the rear axle test bench to collect noise signals at multiple positions, and the noise signals at multiple positions are fused to obtain the total noise signal S of the rear axle. total (t); Step S30: Decomposing the rear axle total noise signal S total (t) is decomposed into K modal functions u k (t), the calculation formula is: Among them, u k (t) is the kth mode function; r(t) is the residual signal; Calculate the load force to simulate the noise signal S sim (t) with each mode u k The correlation coefficient R of (t) k , the formula is: Where ∫dt represents the integral over time t; Correlation coefficient R k Perform weighted summation to obtain the weighted value R of the loading force correlation coefficient sim ; Step S40: Extracting the total noise signal S of the rear axle total (t) noise dynamic spectrum characteristics, including the main frequency spectrum characteristics f peak (t), power density spectrum characteristics H(f), frequency dispersion spectrum characteristics D f ; Step S50: Based on the extracted noise dynamic spectrum characteristics and correlation coefficient weighting value R sim A comprehensive scoring model is constructed to calculate the noise comprehensive score N, and the formula is: N=w1·SPL avg +w2·D f +w3·F peak +w4·R sim Among them, SPL avg is the average sound pressure level; w1, w2, w3, w4 are the weighting coefficients of the comprehensive scoring model, F peak The main frequency spectrum characteristic f peak (t) maximum peak value; Set the low score threshold N low and the scoring threshold N medium , judge the test results: When N≤N low , the test result is "excellent"; When N low <N≤N medium , the test result is "qualified"; When N>N medium , the test result is "failed".

2. A rear axle noise testing method as claimed in claim 1, characterized in that: In step S10, the loading force frequency f load The calculation formula of (t) is: Among them, ω engine (t) is the engine speed, in rad / s; r gear is the transmission ratio of the current gear.

3. A rear axle noise testing method as claimed in claim 1, characterized in that: In step S10, the formula of the preset random disturbance term R(t) is: R(t)=γ2·sin(2πf r t)+η(t) Among them, γ2 is the preset disturbance amplitude; f r is the preset disturbance frequency; η(t) is the random noise of the preset normal distribution.

4. A rear axle noise testing method as claimed in claim 1, characterized in that: In step S20, sensors are arranged at multiple positions of the rear axle stand, including: Vibration sensors, installed at gear meshing points and bearing nodes, are used to collect structural vibration signals; A sound pressure sensor, mounted outside the rear axle housing, records airborne noise; The reference sensor is installed at the support point of the test bench and is used to collect the background noise of the test bench.

5. A rear axle noise testing method as claimed in claim 1, characterized in that: In step S30, the weighted value R of the load force correlation coefficient sim The calculation formula is: Among them, w k is the modal weighting coefficient, which indicates the contribution of each mode to the noise characteristics.

6. A rear axle noise testing method as claimed in claim 4, characterized in that: Step S40 includes: Main frequency spectrum characteristics f peak The extraction formula of (t) is: f peak (t)=argmax(S(f,t)) Among them, S(f, t) is the time-frequency spectrum density, which is obtained from the total noise signal S of the rear axle by short-time Fourier transform. total (t), argmax(S(f, t)) represents the frequency at which the spectrum density S(f, t) reaches its maximum value; The extraction formula of power density spectrum characteristic H(f) is: Among them, FFT (S sim S(t)) is the frequency spectrum of the noise signal simulated by the loading force; S(f) is the frequency spectrum of the total noise signal of the rear axle; Frequency dispersion spectrum characteristics D f The extraction formula is: Among them, f i is the ith frequency component in the total noise signal of the rear axle, f avg is the mean value of the frequency components in the total noise signal of the rear axle.

7. A rear axle noise testing method as claimed in claim 1, characterized in that: In step S50, the average sound pressure level SPL avg The calculation formula is: Among them, p(t) is the instantaneous sound pressure of the noise signal, in Pa, which comes from the total noise signal of the rear axle; p0 is the reference sound pressure, the default value is 20μPa, and T is the preset test time length.

8. A rear axle noise testing system, characterized in that: The rear axle noise testing system comprises: The loading force application module is used to send a loading force F(t) application instruction to the rear axle test bench based on the dynamic loading force simulator. The loading force F(t) is used to simulate the actual operating condition of the vehicle at time t. The calculation formula is: Among them, A0 is the basic loading force, the unit is N, which is used to simulate the constant power input when the vehicle is traveling at a constant speed; is the vehicle's acceleration in m / s 2 , calculated based on the time variation of vehicle speed; β1 is the weight coefficient of the load force response to vehicle acceleration; G(t) is the vehicle slope load, in N, G(t) = m g sin(θ(t)), m is the vehicle mass, g is the acceleration of gravity, θ(t) is the dynamic slope angle of the vehicle when running; β2 is the weight coefficient of the load force response to the slope load; f load (t) is the loading force frequency, which is calculated based on the engine speed and gear ratio; R(t) is the preset random disturbance term; φ is the phase offset of the loading force, in radians; The simulation noise signal generation module is used to convert the dynamic loading force F(t) into the loading force simulation noise signal S through the physical correlation model of the loading force and the noise characteristics. sim (t), the physical correlation model adopts the formula: Among them, α is the influence coefficient of the amplitude of the loading force; γ is the influence coefficient of the rate of change of the loading force; is the instantaneous rate of change of the loading force; δ is the noise offset constant, which is used to compensate for the inherent noise of the dynamic loading force simulator; Sensors are arranged at multiple positions of the rear axle test bench to collect noise signals at multiple positions, and the noise signals at multiple positions are fused to obtain the total noise signal S of the rear axle. total (t); The signal decomposition and correlation calculation module is used to convert the rear axle total noise signal S total (t) is decomposed into K modal functions u k (t), the calculation formula is: Among them, u k (t) is the kth mode function; r(t) is the residual signal; Calculate the load force to simulate the noise signal S sim (t) with each mode u k The correlation coefficient R of (t) k , the formula is: Where ∫dt represents the integral over time t; Correlation coefficient R k Perform weighted summation to obtain the weighted value R of the loading force correlation coefficient sim ; The state spectrum characteristic extraction module is used to extract the total noise signal S of the rear axle total (t) noise dynamic spectrum characteristics, including the main frequency spectrum characteristics f peak (t), power density spectrum characteristics H(f), frequency dispersion spectrum characteristics D f ; Noise test comprehensive scoring module, used to extract noise dynamic spectrum characteristics and correlation coefficient weighted value R sim A comprehensive scoring model is constructed to calculate the noise comprehensive score N, and the formula is: N=w1·SPL avg +w2·D f +w3·F peak +w4·R sim Among them, SPL avg is the average sound pressure level; w1, w2, w3, w4 are the weighting coefficients of the comprehensive scoring model, F peak The main frequency spectrum characteristic f peak (t) maximum peak value; Set the low score threshold N low and the scoring threshold N medium , judge the test results: When N≤N low , the test result is "excellent"; When N low <N≤N medium , the test result is "qualified"; When N>N medium , the test result is "failed".

9. A rear axle noise testing device, characterized in that: The rear axle noise testing device comprises: a memory, a processor and a rear axle noise testing program stored in the memory and executable on the processor. When the rear axle noise testing program is executed by the processor, the rear axle noise testing method described in any one of claims 1 to 7 is implemented.

10. A computer program product, characterized in that The computer program product comprises a rear axle noise test program, and when the rear axle noise test program is executed by a processor, the rear axle noise test method described in any one of claims 1 to 7 is implemented.

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