A multi-dimensional testing method and system for high-speed uniformity and noise of a commercial vehicle tire

Abstract

This invention provides a multi-dimensional testing method for the high-speed uniformity and noise of commercial vehicle tires, comprising the following steps: acquiring the process parameters of the tire under test and grouping the samples; using a high-rigidity test bench to collect multi-physics field signals of the tire during high-speed rotation, processing the mechanical signals to extract HSU parameters, and performing time-frequency joint analysis on the non-stationary mechanical signals; performing wavelet packet decomposition on the acoustic signals to extract the energy distribution of the acoustic signals and the correlation coefficient with the HSU parameters; establishing a cross-domain coupling model of HSU parameters and acoustic response based on the partial least squares regression algorithm, and using variance analysis to quantify the sensitivity of process parameters to noise. This invention achieves, for the first time, a synchronous correlation mapping between HSU parameters and noise on commercial vehicle tires, establishing a quantitative relationship model between mechanical and acoustic performance, and correlating noise with process parameters, enabling enterprises to effectively predict and control the noise level induced by specific process parameters.

Description

Technical Field

[0001] This invention relates to the field of tire dynamics testing technology, and in particular to a method and system for multi-dimensional testing of high-speed uniformity and noise of commercial vehicle tires. Background Technology

[0002] With the accelerating trend of electrification and intelligentization in commercial vehicles, OEMs have significantly reduced their tolerance thresholds for tire noise, vibration, and harshness (NVH) performance (cabin noise requirement ≤65dB(A)@70km / h). However, low-frequency structural noise (20-300Hz) and tread noise (500-2000Hz) caused by uniformity defects in commercial vehicle tires under high-speed conditions have become the main cause of excessive vehicle noise.

[0003] Traditional high-speed uniformity (HSU) testing is primarily geared towards passenger car tires (PCR). Limited by load-bearing capacity (typically ≤2.5 tons), its testing conditions cannot meet the testing requirements of commercial vehicle tires (single tire load ≥3.5 tons). Furthermore, traditional HSU testing for PCR can only obtain mechanical parameters such as radial force (RFV), lateral force (LFV), and tangential force (TFV) at the wheel axle, lacking the ability to simultaneously correlate these parameters with noise. This makes it impossible to establish a quantitative model of the relationship between mechanical and acoustic performance.

[0004] The mechanism by which manufacturing process fluctuations affect HSU (Hardware Suspension Unit) of commercial vehicle tires is not yet clear, and there is currently no testing method that can cover the mapping analysis between process parameters and performance. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the above-mentioned technologies and provide a multi-dimensional testing method and system for the high-speed uniformity and noise of commercial vehicle tires.

[0006] Therefore, this invention provides a multi-dimensional testing method for the high-speed uniformity and noise of commercial vehicle tires, comprising the following steps:

[0007] Obtain the process parameters of the tire to be tested, and group the samples based on the process parameters;

[0008] Using a high-rigidity test bench, multi-physics field signals of the tire during high-speed rotation are collected synchronously. These multi-physics field signals include mechanical signals and acoustic signals.

[0009] The mechanical signal is processed to extract HSU parameters, and time-frequency joint analysis is performed on the non-stationary mechanical signal.

[0010] The acoustic signal is subjected to wavelet packet decomposition to extract the energy distribution of the acoustic signal and its correlation coefficient with the HSU parameters;

[0011] A cross-domain coupling model of the HSU parameters and acoustic-vibration response is established based on the partial least squares regression algorithm, and the sensitivity of the process parameters to noise is quantified by analysis of variance.

[0012] Preferably, the cross-domain coupling model between the HSU parameters and the acoustic spectral response is as follows:

[0013]

[0014] in, The sound pressure level spectrum, This refers to the acceleration due to tread vibration. , Here, t is the coupling coefficient, and t is time. For the residual term, Represents angular frequency. Represents the effective value of tangential force. Represents the nth harmonic component of the radial force. Represents the peak value of the lateral force. This represents the first harmonic component of the tangential force.

[0015] Preferably, the HSU parameters include the peak-to-peak values ​​of radial force, lateral force, and tangential force, as well as their 1st to 24th order harmonic components.

[0016] Preferably, the specific steps of the wavelet packet decomposition method include:

[0017] Performing J-level iterative decomposition on the discrete acoustic signal yields 2 J A number of equal-width sub-band signals, where J=5, are used to evenly divide the signal into 32 sub-bands;

[0018] Calculate the energy percentage of each sub-band Quantify the contribution of different frequency components to the total energy.

[0019] Preferably, the formula for calculating the sensitivity of the process parameters to noise is as follows:

[0020]

[0021] in, For the first Sensitivity index of process parameters For the range of process fluctuations, This represents the standard deviation of noise.

[0022] Preferably, the process parameters include the tire carcass inversion height and the rubber core height; the deviation of the process parameters is quantified by industrial CT scanning, and when the tire carcass inversion height difference is ≥3mm, it is defined as an abnormal group.

[0023] Preferably, the method further includes a noise prediction model obtained by regression fitting based on a cross-domain coupling model, the prediction model comprising:

[0024] Prediction models based on mechanical parameters:

[0025]

[0026] Wherein, RFV represents radial force fluctuation, and TFV represents tangential force fluctuation;

[0027] Prediction models based on process parameters:

[0028] .

[0029] A multi-dimensional testing system for high-speed uniformity and noise of commercial vehicle tires, comprising:

[0030] High stiffness test bench: Equipped with a floating vibration isolation loading system with dynamic stiffness ≥1×10^9N / m and integrated with a hydraulic servo loading system to apply vertical load to the tires, with a maximum load-bearing capacity of not less than 7 tons.

[0031] Rotary drum: The surface of the rotary drum is covered with a road surface assembled from multiple trapezoidal road panels, and the tire surface is in contact with the road surface;

[0032] Sensor array: including a triaxial high-precision force sensor mounted at the center of the axle head, a four-channel acoustic sensor array arranged in a ring around the tire circumference, and a temperature sensor array for acquiring four-dimensional data distribution;

[0033] Synchronous acquisition and processing module: used to realize microsecond-level synchronous acquisition of multi-physics field signals and cross-domain data modeling and analysis;

[0034] The environment simulation module is used to simulate the actual operating environment of tires.

[0035] Preferably, the acoustic sensor has a dynamic range of 137dB and an inherent noise of ≤15.5dB(A); the force sensor has a measurement range of ±50gpk, a broadband resolution of 0.0001grms, and a frequency range covering 0.5 to 5000Hz.

[0036] Preferably, the environmental simulation module operates under constant temperature and humidity conditions, gradually increasing the speed from 40 km / h to 100 km / h in steps of 20 km / h, with each speed point running for at least 180 seconds, while simultaneously acquiring mechanical and acoustic signals.

[0037] The beneficial effects of the present invention are as follows: The present invention provides a multi-dimensional testing method and system for the high-speed uniformity and noise of commercial vehicle tires, which has the following beneficial effects.

[0038] Using a testing system capable of bearing more than 7 tons, integrating multiple standard road surfaces, and simultaneously collecting four-dimensional data on mechanics, vibration, acoustics, and temperature, it overcomes the limitations of traditional passenger car testing machines that are insufficient in load and unable to simulate real road surfaces. At the same time, through joint time-frequency analysis of sound / vibration signals and wavelet packet decomposition, it achieves for the first time a synchronous correlation mapping between HSU parameters and noise on commercial vehicle tires, and establishes a quantitative relationship model between mechanical and acoustic performance.

[0039] The constructed cross-domain coupling model of HSU parameters and acoustic-vibration response, along with the process parameter sensitivity index, correlates noise with process parameters, enabling enterprises to effectively predict and control the noise level represented by specific process parameters.

[0040] This invention has been successfully applied to the commercial vehicle product line of a tire company, resulting in a 11.7% reduction in new product development cycle; a 15.3% decrease in NVH complaint rate for high-end commercial vehicle tires; a 3.1% reduction in process defect rate; and annual cost savings of over 12 million yuan. Attached Figure Description

[0041] Figure 1 These are the surface response plots of HSU and PNL;

[0042] Figure 2 It is a contour plot of PNL and process parameters;

[0043] Figure 3 This is a diagram showing the interaction between PNL and process parameters;

[0044] Figure 4 This is an RFV interaction diagram;

[0045] Figure 5 This is a TFV interaction diagram. Detailed Implementation

[0046] The present invention will be further described below with reference to the accompanying drawings and specific embodiments to aid in understanding its content. Unless otherwise specified, the methods used in this invention are conventional methods; the raw materials and apparatus used, unless otherwise specified, are conventional commercially available products.

[0047] like Figures 1-5 As shown, this invention provides a multi-dimensional testing method for the high-speed uniformity and noise of commercial vehicle tires, including the following steps:

[0048] S1. Select N groups (N≥20) of commercial vehicle tires of the same specification and record key process parameters such as tire carcass reverse wrapping height and rubber core height.

[0049] S2. Construct a tire body structure model using industrial CT scanning, quantify the deviation of the process parameters, and define the abnormal group when the tire body reverse wrapping height difference is ≥3mm. Group the samples based on the deviation of the process parameters.

[0050] S3. Using a high-rigidity test bench, synchronously acquire multi-physics field signals of the tire during high-speed rotation. The multi-physics field signals include mechanical signals and acoustic signals.

[0051] S4. Post-process the mechanical / vibration signal to extract HSU parameters, and perform time-frequency joint analysis on the non-stationary mechanical signal; the HSU parameters include the peak-to-peak values ​​of radial force, lateral force, and tangential force and their 1st-24th order harmonic components, rotational speed / rotational frequency (n, fr), order (O=f / fr), peak acceleration / effective value (n, fr). ).

[0052] S5. Perform wavelet packet decomposition on the acoustic signal to extract the energy distribution of the acoustic signal and its correlation coefficient with the HSU parameters.

[0053] S6. Based on the partial least squares regression algorithm, establish a cross-domain coupling model between the HSU parameters and the acoustic vibration response, and use variance analysis to quantify the sensitivity of the process parameters to noise.

[0054] Furthermore, in step S4, the amplitude and phase of each harmonic of the radial force, lateral force, and tangential force are calculated based on the following formula, typically through Fast Fourier Transform.

[0055]

[0056] In the formula:

[0057] Fz(i) is the radial force at each sampling point in one revolution of the SIA coordinate system, measured as an average of 8 revolutions.

[0058] i is the variable exponent of the sampling point, i = 1, 2, ..., m

[0059] m is the number of sampling points per revolution, for example, 4096.

[0060] j is the variable exponent of the harmonics, j = 1, 2, ..., n, where n is the final harmonic number, such as 16, 32.

[0061] RjH is the decomposed amplitude (N) of the radial force at the j-th harmonic or 1 / (j*T) frequency.

[0062] RjP is the decomposed phase angle (°) at the j-th harmonic of the radial force or at a frequency of 1 / (j*T).

[0063] T is the time it takes for the tire to rotate one revolution at a specified speed.

[0064] Fx(i), Fy(i), MX(i), and MZ(i) are all calculated in the same way as Fz(i).

[0065] Furthermore, in step S4, the time-frequency joint analysis of the non-stationary mechanical signal is performed using a combined analysis method of short-time Fourier transform (STFT) and Wigner-Ville distribution (WVD) to analyze the time-frequency characteristics of HSU vibration excitation:

[0066]

[0067] in, For window functions, * denotes complex conjugate, and N is a general symbol (which can be R, L, or T).

[0068] Furthermore, in step S5, the specific steps of the wavelet packet decomposition method include:

[0069] If we set the scaling function and wavelet function to form an orthogonal wavelet basis, then the corresponding family of wavelet packet functions is defined as follows:

[0070]

[0071] Where j is the scale (number of decomposition layers), k is the translation position, and n is the sub-band index. The decomposition of signal f(t) is its projection onto this family of functions.

[0072] For example, for a discrete signal x[n] of length N, the decomposition process is as follows:

[0073] First, initialization: Secondly, there is iterative decomposition. For the j-th layer (j=1,2,…,J), for the signal of each node in the previous layer… Perform the following operations:

[0074] Low-pass filter:

[0075] High-pass filter:

[0076] Downsampling: for and Binary sampling is performed (retaining even-indexed samples), where h[n] and g[n] are the low-pass and high-pass filters, respectively, determined by the selected wavelet basis. Finally, after J-level decomposition, the desired result is obtained. Each of the following sub-band signals has an equal width and a length of [missing information]. These subband coefficients together constitute the time-frequency representation of the signal.

[0077] If the sampling frequency is f_s, then after J-level decomposition, the bandwidth of each sub-band is:

[0078] =

[0079] When J=5 / 32

[0080] This means that the signal can be evenly divided into 32 segments in the frequency domain, with each segment being only 1 / 32 the width of the original Nyquist bandwidth, which can effectively cover the main frequency bands of tire noise and ensure frequency domain resolution.

[0081] In this embodiment, the acoustic signal is uniformly divided into 32 sub-bands.

[0082] Wavelet packet decomposition (WPD) is an extension of the traditional wavelet transform. It can recursively decompose both the high and low frequency components of a signal, thus providing more refined analysis in the time-frequency plane. Its core algorithm can be summarized as a filter bank combined with complete binary tree decomposition.

[0083] HSU vibration excitation typically refers to the non-stationary, time-varying excitation experienced by a structure under specific operating conditions (such as variable speed). Traditional Fourier transform struggles to capture its local features, while wavelet packet decomposition excels at handling such signals.

[0084] For HSU (variable speed operation) vibration signals, the excitation frequency may vary with the rotational speed, and multiple harmonic components exist. Narrower sub-bands are helpful in:

[0085] Separate closely spaced frequency components (e.g., frequency shifters and their harmonics);

[0086] To more accurately pinpoint the exact moment a frequency appears on the timeline.

[0087] Furthermore, in step S5, the energy distribution of the acoustic signal is extracted based on Parseval's theorem, which states that the total energy of the acoustic signal in the time domain is equal to the sum of the energies of its orthogonal components in the frequency domain. For wavelet packet decomposition, the energy of the k-th sub-band at the j-th layer... It can be approximated as the sum of squares of the corresponding wavelet packet coefficients.

[0088] The specific calculation process is as follows:

[0089] Energy of the k-th sub-band in the j-th layer:

[0090]

[0091] in: It is the coefficient sequence of the k-th node in the j-th layer.

[0092] Total signal energy

[0093]

[0094] Energy percentage of the k-th sub-band

[0095]

[0096] Furthermore, in step S5, the correlation coefficient between the acoustic signal and the HSU parameters quantifies the strength of the linear relationship between the acoustic signal and the HSU parameter variables, and its value is between -1 and 1.

[0097] The commonly used Pearson correlation coefficient r is defined as follows:

[0098]

[0099] in: and Let be two variables of the i-th sample. and These are their means.

[0100] Furthermore, in step S6, the cross-domain coupling model of the HSU parameters and the acoustic spectral response is as follows:

[0101]

[0102] in, The sound pressure level spectrum, This refers to the acceleration due to tread vibration. , Here, is the coupling coefficient, and t is time. For the residual term, Represents angular frequency. Represents the effective value of tangential force. Represents the nth harmonic component of the radial force. Represents the peak value of the lateral force. This represents the first harmonic component of the tangential force.

[0103] The coupling coefficients and residual terms were obtained through least squares regression.

[0104] Furthermore, the formula for calculating the sensitivity of the process parameters to noise is as follows:

[0105]

[0106] in, For the first Sensitivity index of process parameters For the range of process fluctuations, This represents the standard deviation of noise. It can be obtained by taking the partial derivative of the following process parameter prediction model.

[0107] Furthermore, the method also includes extracting the main contributing factors RFV and TFV based on the mechanical-acoustic mapping relationship revealed by the cross-domain coupling model, and obtaining an engineering-applicable linear noise prediction model through regression fitting. The prediction model includes:

[0108] Prediction models based on mechanical parameters:

[0109]

[0110] Wherein, RFV represents radial force fluctuation, and TFV represents tangential force fluctuation;

[0111] Extensive testing revealed a weak correlation between LFV and PNL, so they were not considered.

[0112] Prediction models based on process parameters:

[0113] .

[0114] The frequency range of the above PNL is 20-100Hz.

[0115] The impact of process fluctuations: For every 2mm increase in the deviation of the tire carcass wrapping height, the low-frequency noise of the PNL increases by 1-2 dB(A), while the impact of the rubber core height is less than 1 dB(A). There is an interaction between the two. See [link to relevant documentation]. Figures 2-5 .

[0116] A multi-dimensional testing system for high-speed uniformity and noise of commercial vehicle tires, comprising:

[0117] High-rigidity test bench: Maximum load capacity of 7 tons, equipped with a floating vibration isolation upper and lower loading system with dynamic stiffness ≥1×10^9N / m to suppress structural resonance during the test (natural frequency ≥200Hz), and integrated with a hydraulic servo loading system (accuracy ±0.5%FS) to apply vertical load (0-50kN) to the tire to simulate the actual wheel end load.

[0118] Drum: The surface of the drum is equipped with a replica of the standard ISO10844 road surface of the actual road test site. The replica road surface is divided into 9 sections, each of which is designed in a trapezoidal shape to simulate the actual road surface on which the vehicle is driving. The tire surface is in contact with the replica road surface on the drum. During the actual test, the tire position remains relatively unchanged, and the drum drives the tire to rotate.

[0119] Sensor array: including a triaxial high-precision force sensor mounted at the center of the axle head, a four-channel acoustic sensor array arranged in a ring around the tire circumference, and a temperature sensor array for acquiring four-dimensional data distribution; the temperature sensor array is used to detect the thermal stability of the test process, ensure that data is collected under constant temperature conditions, and eliminate the interference of rubber thermal effect on HSU parameters.

[0120] Synchronous acquisition and processing module: used to realize microsecond-level synchronous acquisition of multi-physics field signals and cross-domain data modeling and analysis;

[0121] The environment simulation module is used to simulate the actual operating environment of tires.

[0122] Furthermore, the road surface is designed in a trapezoidal shape, which can avoid the influence of the order of road surface excitation on tire tread noise and vibration.

[0123] Furthermore, the acoustic sensor has a dynamic range of 137dB, an inherent noise of ≤15.5dB(A), a maximum pressure of 20μPa, a frequency range of (±2dB) 3.75 to 20000Hz, a sound pressure measurement range of 30-140dB, and simultaneously acquires the near-field noise spectrum, i.e., simultaneously acquires the 1 / 3 octave band sound pressure level (20Hz-2kHz) and cavity resonance noise (180-220Hz), with a focus on extracting noise in the 20-100Hz and 100-2000Hz ranges.

[0124] The 137dB dynamic range ensures that the acoustic sensor can still clearly distinguish tire noise even in the presence of background noise from components such as the drum drive motor.

[0125] Furthermore, the force sensor has a sensitivity of (±10%) 100mV / g (10.2mV / (m / s²)), a measurement range of ±50gpk (±490m / s²pk), a broadband resolution of 0.0001grms (0.001m / s²rms), a frequency range of (±5%) 0.5 to 5000Hz, and real-time acquisition of acceleration in the X / Y / Z directions.

[0126] Furthermore, the environmental simulation module operates under constant temperature (23±1℃) and constant humidity (50±5%RH) conditions, gradually increasing the speed from 40km / h to 100km / h in steps of 20km / h, with each speed point running for 180 seconds, while simultaneously acquiring mechanical and acoustic signals.

[0127] Constant temperature and humidity conditions eliminate the nonlinear interference introduced by changes in the stiffness and damping of tire rubber materials with temperature and humidity, ensuring the stability of the causal relationship between HSU parameters and noise response.

[0128] This invention is the first to achieve simultaneous acquisition of four-dimensional data—vibration (RFV / LFV / TFV), acoustic (20Hz-2kHz), and temperature fields—in commercial vehicle tire testing, with a time synchronization error ≤1μs. This ensures that the energy distribution and correlation coefficients obtained from subsequent time-frequency joint analysis and wavelet packet decomposition are established under a strictly consistent time coordinate system, avoiding spurious correlations or phase confusion caused by time mismatch. Through time-frequency joint analysis of acoustic / vibration signals (STFT+WVD), the time-varying coupling mechanism of tire-noise under HSU excitation is revealed (e.g., TFV focuses on 20-60Hz noise, LFV harmonics induce 100-200Hz modal resonance, and RFV focuses on 60-400Hz noise).

[0129] It should be noted that four-dimensional temperature field data typically refers to temperature distribution data with a time dimension (t) superimposed on a three-dimensional spatial dimension (x, y, z). That is, each data point contains four coordinate information: spatial location (x, y, z) + time (t) + the temperature value (T) at that location at the corresponding time. Temperatures at different spatial points and times are collected synchronously through a sensor array.

[0130] Example:

[0131] Twenty 12R22.5 TBR tires were selected and divided into two groups:

[0132] Control group (N=10): Standard process (carcass reverse wrapping height tolerance ±0.5mm, rubber core height tolerance ±2mm);

[0133] Experimental group (N=10): Introduced process deviations (carcass reverse wrapping height +2mm, rubber core height +5mm).

[0134] Data was collected under steady-state conditions at 70 km / h:

[0135] parameter Control group (mean ± standard deviation) Experimental group (mean ± standard deviation) rate of change <![CDATA[RFV(m / s 2 )]]> 9.70±0.50 13.30±0.40 37.1% <![CDATA[TFV(m / s 2 )]]> 5.30±0.40 11.60±0.40 118.9% 20-100Hz noise (dB) 48.38±0.30 50.40±0.25 4.2%

[0136] It should be noted that the 20-100Hz noise in the table is calculated based on the process parameter prediction model.

[0137] The data above shows that the RFV of the experimental group increased by 37.1% compared to the control group. This indicates that the manufacturing deviation severely disrupted the radial stiffness uniformity of the tire, leading to a significant increase in the periodic fluctuations of radial force during rotation. The TFV of the experimental group increased by 118.9% compared to the control group. This indicates that the manufacturing deviation (especially the increased tire carcass overlay height) had a significant impact on the tire's tangential dynamic characteristics (force fluctuations related to driving / braking).

[0138] While the HSU parameters deteriorated significantly, the low-frequency noise in the 20-100Hz range of the experimental group increased by 2.02dB compared with the control group, with a change rate of 4.2%. This change can be perceived by drivers and passengers and affects their driving experience.

[0139] This invention breaks through the load and adaptation limitations of traditional HSUs, constructs a high-speed uniformity testing system suitable for commercial vehicle tires, establishes a cross-domain correlation model between HSU mechanical parameters (RFV / LFV / TFV) and wideband noise (20Hz-2kHz), and quantifies the sensitivity coefficients of manufacturing process fluctuations to HSUs and noise, guiding process tolerance design.

[0140] This invention has been successfully applied to the commercial vehicle product line of a tire company, resulting in a 11.7% reduction in the new product development cycle (by replacing repeated trial production with model prediction); a 15.3% decrease in NVH complaints about high-end commercial vehicle tires; a 3.1% reduction in the defective product rate; and annual cost savings of over 12 million yuan.

[0141] In the description of this invention, it should be understood that the terms "left", "right", "up", "down", "top", "bottom", "front", "back", "inner", "outer", "back", "middle", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0142] However, the above description is merely a specific embodiment of the present invention and should not be construed as limiting the scope of the present invention. Therefore, any substitution of equivalent components or equivalent changes and modifications made in accordance with the scope of protection of the present invention should still fall within the scope of the claims of the present invention.

Claims

1. A multi-dimensional testing method for the high-speed uniformity and noise of commercial vehicle tires, characterized in that, Includes the following steps: Obtain the process parameters of the tire to be tested, and group the samples based on the process parameters; Using a high-rigidity test bench, multi-physics field signals of the tire during high-speed rotation are collected synchronously. These multi-physics field signals include mechanical signals and acoustic signals. The mechanical signal is processed to extract HSU parameters, and time-frequency joint analysis is performed on the non-stationary mechanical signal. The acoustic signal is subjected to wavelet packet decomposition to extract the energy distribution of the acoustic signal and its correlation coefficient with the HSU parameters; A cross-domain coupling model of the HSU parameters and acoustic-vibration response is established based on the partial least squares regression algorithm, and the sensitivity of the process parameters to noise is quantified by analysis of variance.

2. The method for multi-dimensional testing of high-speed uniformity and noise of commercial vehicle tires according to claim 1, characterized in that, The cross-domain coupling model between the HSU parameters and the acoustic vibration response is as follows: ； in, The sound pressure level spectrum, This refers to the acceleration due to tread vibration. , Here, is the coupling coefficient, and t is time. For the residual term, Represents angular frequency. Represents the effective value of tangential force. Represents the nth harmonic component of the radial force. Represents the peak value of the lateral force. This represents the first harmonic component of the tangential force.

3. The method for multi-dimensional testing of high-speed uniformity and noise of commercial vehicle tires according to claim 1, characterized in that, The HSU parameters include the peak-to-peak values ​​of radial force, lateral force, and tangential force, as well as their 1st to 24th order harmonic components.

4. The method for multi-dimensional testing of high-speed uniformity and noise of commercial vehicle tires according to claim 1, characterized in that, The specific steps of the wavelet packet decomposition method include: Performing J-level iterative decomposition on the discrete acoustic signal yields 2 J A number of equal-width sub-band signals, where J=5, are used to evenly divide the signal into 32 sub-bands; Calculate the energy percentage of each sub-band Quantify the contribution of different frequency components to the total energy.

5. The method for multi-dimensional testing of high-speed uniformity and noise of commercial vehicle tires according to claim 1, characterized in that, The formula for calculating the sensitivity of the process parameters to noise is as follows: ； in, For the first Sensitivity index of process parameters For the range of process fluctuations, This represents the standard deviation of noise.

6. The method for multi-dimensional testing of high-speed uniformity and noise of commercial vehicle tires according to claim 1, characterized in that, The process parameters include the tire carcass inversion height and the rubber core height; the deviation of the process parameters is quantified by industrial CT scanning, and when the tire carcass inversion height difference is ≥3mm, it is defined as an abnormal group.

7. The method for multi-dimensional testing of high-speed uniformity and noise of commercial vehicle tires according to claim 1, characterized in that, The method also includes a noise prediction model obtained by regression fitting based on a cross-domain coupling model, the prediction model comprising: Prediction models based on mechanical parameters: ； Wherein, RFV represents radial force fluctuation, and TFV represents tangential force fluctuation; Prediction models based on process parameters: 。 8. A testing system for implementing the method of claim 1, characterized in that, include: High stiffness test bench: Equipped with a floating vibration isolation loading system with dynamic stiffness ≥1×10^9N / m and integrated with a hydraulic servo loading system to apply vertical load to the tires, with a maximum load-bearing capacity of not less than 7 tons. Rotary drum: The surface of the rotary drum is covered with a road surface assembled from multiple trapezoidal road panels, and the tire surface is in contact with the road surface; Sensor array: including a triaxial high-precision force sensor mounted at the center of the axle head, a four-channel acoustic sensor array arranged in a ring around the tire circumference, and a temperature sensor array for acquiring four-dimensional data distribution; Synchronous acquisition and processing module: used to realize microsecond-level synchronous acquisition of multi-physics field signals and cross-domain data modeling and analysis; The environment simulation module is used to simulate the actual operating environment of tires.

9. The multi-dimensional testing system for high-speed uniformity and noise of commercial vehicle tires according to claim 8, characterized in that, The acoustic sensor has a dynamic range of 137dB and an inherent noise of ≤15.5dB(A); the force sensor has a measurement range of ±50gpk, a broadband resolution of 0.0001grms, and a frequency range covering 0.5 to 5000Hz.

10. A multi-dimensional testing system for high-speed uniformity and noise of commercial vehicle tires according to claim 8, characterized in that, The environmental simulation module operates under constant temperature and humidity conditions, gradually increasing the speed from 40 km / h to 100 km / h in steps of 20 km / h, with each speed point running for at least 180 seconds, while simultaneously acquiring mechanical and acoustic signals.

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