CN116579932A - Method and system for predicting and optimizing tire pattern noise - Google Patents
Method and system for predicting and optimizing tire pattern noise Download PDFInfo
- Publication number
- CN116579932A CN116579932A CN202310340197.2A CN202310340197A CN116579932A CN 116579932 A CN116579932 A CN 116579932A CN 202310340197 A CN202310340197 A CN 202310340197A CN 116579932 A CN116579932 A CN 116579932A
- Authority
- CN
- China
- Prior art keywords
- tire
- noise
- pattern
- pixel
- predicted
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 40
- 239000011159 matrix material Substances 0.000 claims abstract description 69
- 230000000737 periodic effect Effects 0.000 claims abstract description 35
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 9
- 238000009826 distribution Methods 0.000 claims abstract description 9
- 238000012360 testing method Methods 0.000 claims description 28
- 238000005457 optimization Methods 0.000 claims description 21
- 230000005236 sound signal Effects 0.000 claims description 11
- 239000002245 particle Substances 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 8
- 238000004422 calculation algorithm Methods 0.000 claims description 7
- 238000003860 storage Methods 0.000 claims description 5
- 125000004122 cyclic group Chemical group 0.000 claims description 3
- 238000013461 design Methods 0.000 abstract description 6
- 238000004364 calculation method Methods 0.000 description 4
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000005086 pumping Methods 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
- 229910000831 Steel Inorganic materials 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 239000010959 steel Substances 0.000 description 2
- 238000012952 Resampling Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60C—VEHICLE TYRES; TYRE INFLATION; TYRE CHANGING; CONNECTING VALVES TO INFLATABLE ELASTIC BODIES IN GENERAL; DEVICES OR ARRANGEMENTS RELATED TO TYRES
- B60C11/00—Tyre tread bands; Tread patterns; Anti-skid inserts
- B60C11/03—Tread patterns
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/40—Analysis of texture
- G06T7/41—Analysis of texture based on statistical description of texture
- G06T7/42—Analysis of texture based on statistical description of texture using transform domain methods
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Probability & Statistics with Applications (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Mechanical Engineering (AREA)
- Tires In General (AREA)
Abstract
The invention discloses a method and a system for predicting and optimizing tire pattern noise, wherein the method comprises the following steps: acquiring each single-pitch image of the tire pattern, and describing pattern distribution conditions of the single-pitch images by using pixel values to obtain a pixel matrix of the single-pitch images; based on the weighting matrix, weighting is carried out on the pixel matrix of the single-pitch image to obtain a pixel weighting matrix of the single-pitch image, and then the pixel weighting matrix of the tire circumferential direction is obtained; acquiring a predicted noise periodic signal of one rotation of the tire based on a pixel weighting matrix of the tire circumference; and carrying out Fourier series decomposition on the predicted noise periodic signal, calculating a root mean square value, and obtaining predicted tire pattern noise. The invention fully considers the size and shape of the pattern design based on the tire pattern image, considers the influence difference of the grooves at different positions in the transverse direction and the longitudinal direction, realizes the accurate prediction of tire pattern noise, and optimizes the tire pattern according to the predicted tire pattern noise.
Description
Technical Field
The invention relates to the technical field of tire pattern design, in particular to a method and a system for predicting and optimizing tire pattern noise.
Background
With the rapid development of high-speed traffic and automotive fields, traffic noise due to vehicles has become a serious environmental problem and is considered as a major source of environmental noise. Vehicles are the main sources of traffic noise including engine noise, tire noise, and vehicle structural noise. Sources of tire noise include carcass structure vibrations, air pumping noise, and noise from interactions between tires/roads. Tire pattern noise dominates tire/road interaction noise. The method can predict and qualitatively evaluate the pattern noise in the early stage of the design of the tire pattern, and has important practical value and engineering significance.
The pattern noise is mainly generated by vibration and pumping: vibration is mainly generated by the impact of the tread with the ground, propagates through adjacent structures in the form of elastic waves, causes vibration of surrounding air and thus generates noise, which falls in a relatively low frequency range; the tread grooves continuously suck and exhaust air in a pulse-like fashion when in contact with the ground, producing significant pumping noise. The inclined grooves have longer contact time with the road surface than the lateral grooves, so that the air compression and release speeds are slower and the noise is less; for longitudinal grooves, the air pressure in the grooves does not change greatly with time, and the air flow generated by encountering resistance when the air in the grooves is released can be ignored. When the tread pattern blocks are contacted with the road surface, air in the pattern grooves is compressed, and the air pressure is increased to form positive pressure; and as the grooves leave the road surface, the air pressure decreases, creating negative pressure. Based on these two processes, a sound pressure waveform having a shape with a large top and a small bottom similar to "N" is formed, and as shown in fig. 1, the shape and pressure magnitude of the sound pressure waveform depend on the time for which the tire groove is in contact with the road surface.
At present, analytical models for tire pattern noise can be divided into two types, namely a semi-empirical formula model and a simple sound source model. The semi-empirical formula model is simple, the parameters are few, and only a limited number of parameters are used for representing the tread pattern of the tire, but the model is too much simplified, the practicability is poor, and the noise prediction result is poor; the simple sound source model is also simpler, all types of patterns are treated as a simple sound source, the simplicity is excessive, the design parameter difference of different patterns cannot be truly reflected, and the noise prediction result is poor.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a method and a system for predicting and optimizing tire pattern noise, which are based on tire pattern images, fully consider the size and shape of pattern design, consider the influence difference of grooves at different positions in the transverse direction and the longitudinal direction, and realize accurate prediction of tire pattern noise.
In a first aspect, the present disclosure provides a method of predicting and optimizing tire tread noise.
A method for predicting and optimizing tire tread noise, comprising:
acquiring each single-pitch image of the tire pattern, and describing pattern distribution conditions of the single-pitch images by using pixel values to obtain a pixel matrix of the single-pitch images;
based on the weighting matrix, weighting is carried out on the pixel matrix of the single-pitch image to obtain a pixel weighting matrix of the single-pitch image, and then the pixel weighting matrix of the tire circumferential direction is obtained;
acquiring a predicted noise periodic signal of one rotation of the tire based on a pixel weighting matrix of the tire circumference;
and carrying out Fourier series decomposition on the predicted noise periodic signal, calculating a root mean square value, and obtaining predicted tire pattern noise.
According to a further technical scheme, the method for acquiring the weight matrix comprises the following steps:
obtaining a tire circumferential pattern image according to each single-pitch image of the tire pattern;
performing an indoor single tire noise drum test on the pattern tire based on the tire circumferential pattern image to obtain a tested actual tire noise signal;
processing the tested actual tire noise signals, separating to obtain tested periodic pattern sound signals, and calculating the root mean square value of the test signals;
and calculating to obtain the weight matrix by using a particle swarm optimization algorithm based on the minimized root mean square value of the test signal and the root mean square value of the predicted noise periodic signal.
Further technical solution, the actual tire noise signal includes a periodic pattern sound signal and a background noise signal.
Further technical solution, the method further includes:
dislocation is carried out on each row of patterns of the tire, so that a plurality of tire pattern dislocation combination modes are formed;
and outputting the current optimal tire pattern dislocation combination mode based on the predicted tire pattern noise of the multiple tire pattern dislocation combination modes.
Further technical solution, optimizing tire tread noise, comprising:
fixing the first row of tire patterns, and adjusting the other rows of tire patterns according to the dislocation value and the dislocation step length to form a tire pattern dislocation combination mode;
predicting tire pattern noise according to the formed tire pattern dislocation combination mode;
and (3) performing cyclic iteration adjustment operation, namely taking the tire pattern dislocation combination mode with the lowest predicted tire pattern noise as the current optimal dislocation combination mode, and outputting the current optimal tire pattern dislocation combination mode until iteration is finished.
In a second aspect, the present disclosure provides a tire tread noise prediction and optimization system comprising:
the tire pattern image acquisition module is used for acquiring each single-pitch image of the tire pattern;
the tire pattern image processing module is used for describing pattern distribution conditions of the single-pitch image by using pixel values to obtain a pixel matrix of the single-pitch image; based on the weighting matrix, weighting is carried out on the pixel matrix of the single-pitch image to obtain a pixel weighting matrix of the single-pitch image, and then the pixel weighting matrix of the tire circumferential direction is obtained;
the tire pattern noise prediction module is used for acquiring a prediction noise periodic signal of one rotation of the tire based on a pixel weighting matrix of the tire circumference; and carrying out Fourier series decomposition on the predicted noise periodic signal, calculating a root mean square value, and obtaining predicted tire pattern noise.
According to a further technical scheme, the method for acquiring the weight matrix comprises the following steps:
obtaining a tire circumferential pattern image according to each single-pitch image of the tire pattern;
performing an indoor single tire noise drum test on the pattern tire based on the tire circumferential pattern image to obtain a tested actual tire noise signal;
processing the tested actual tire noise signals, separating to obtain tested periodic pattern sound signals, and calculating the root mean square value of the test signals;
and calculating to obtain the weight matrix by using a particle swarm optimization algorithm based on the minimized root mean square value of the test signal and the root mean square value of the predicted noise periodic signal.
According to a further technical scheme, the system further comprises a tire pattern dislocation optimization module, wherein the tire pattern dislocation optimization module is used for dislocation of each row of tire patterns to form various tire pattern dislocation combination modes, and the current optimal tire pattern dislocation combination mode is output based on predicted tire pattern noise of the various tire pattern dislocation combination modes.
In a third aspect, the present disclosure also provides an electronic device comprising a memory and a processor, and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the steps of the method of the first aspect.
In a fourth aspect, the present disclosure also provides a computer readable storage medium storing computer instructions which, when executed by a processor, perform the steps of the method of the first aspect.
The one or more of the above technical solutions have the following beneficial effects:
1. the invention provides a method and a system for predicting and optimizing tire pattern noise, which are used for fully considering the size and shape of pattern design based on tire pattern images, realizing accurate prediction of the tire pattern noise, optimizing tire patterns according to the predicted tire pattern noise and reducing the tire pattern noise.
2. The method fully considers the influence difference of the grooves at different positions in the transverse direction and the longitudinal direction, and records different weights, thereby ensuring the accuracy of predicting the tire pattern noise.
3. The invention realizes the separation of the pattern sound of the tire noise and other background noise, and combines the test with the theory, so that the prediction method has higher robustness.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
FIG. 1 is a schematic illustration of a tire pattern noise sound pressure waveform;
FIG. 2 is a flowchart of a method for predicting and optimizing tire tread noise according to an embodiment of the present invention;
FIG. 3 is a single pitch image of a tire tread according to one embodiment of the present invention;
FIG. 4 is a schematic diagram of a single tire noise drum microphone arrangement in a room in accordance with an embodiment of the invention;
FIG. 5 is a comparison of predicted tire pattern noise with experimental values in accordance with an embodiment of the present invention;
FIG. 6 is a schematic view of a multi-row tire pattern according to a first embodiment of the present invention;
FIG. 7 is a graph showing the result of tire pattern misalignment optimization in accordance with the first embodiment of the present invention.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
Example 1
The embodiment provides a method for predicting and optimizing tire pattern noise, as shown in fig. 2, comprising the following steps:
acquiring each single-pitch image of the tire pattern, and describing pattern distribution conditions of the single-pitch images by using pixel values to obtain a pixel matrix of the single-pitch images;
based on the weighting matrix, weighting is carried out on the pixel matrix of the single-pitch image to obtain a pixel weighting matrix of the single-pitch image, and then the pixel weighting matrix of the tire circumferential direction is obtained;
acquiring a predicted noise periodic signal of one rotation of the tire based on a pixel weighting matrix of the tire circumference;
and carrying out Fourier series decomposition on the predicted noise periodic signal, calculating a root mean square value, and obtaining predicted tire pattern noise.
In this embodiment, first, each single-pitch image of a tire pattern is acquired, the pattern distribution condition of the single-pitch image is described using pixel values, and a pixel matrix of the single-pitch image is obtained. Wherein, the pixel value of the pattern groove is set to be 1, the pixel value of the tread is set to be 0, as shown in FIG. 3, a pixel matrix P of a single-pitch image is obtained i ：
Wherein J represents the total pixel number in a wide range of the driving surface, N i The number of pixels in the single-pitch longitudinal direction is represented.
Then, the pixel distribution of the tire circumferential pattern at this time can pass through the tire circumferential pixel matrix P tire The expression is:
wherein N is b Representing the total pitch number.
Considering that the weights of the grooves are different when the grooves are located at different longitudinal and transverse positions, the weight matrix of the grooves along the longitudinal direction is set as X, the weight matrix along the transverse direction is set as Y, and the pixels of the single-pitch image are based on the weight matrixThe matrix is weighted to obtain a pixel weighting matrix P of each single-pitch image inew The method comprises the following steps:
respectively calculating corresponding weight matrix for the pixel matrix of each single-pitch image to obtain a pixel weight matrix P in the circumferential direction of the tire tirenew ，P tirenew ＝P 1new ∪P 2new ∪…∪P Nbnew . Wherein P is tirenew With a number of rows of 1 and a number of columns of N i ×N b Is a matrix of (a) in the matrix.
Then, a prediction noise periodic signal for one rotation of the tire is acquired based on the pixel weighting matrix in the tire circumferential direction. Let the tire rolling speed be V (km/h), in this embodiment, let v=80 km/h for a semi-steel tire, let v=70 km/h for an all-steel tire, and let the length of each pixel be d, a predicted noise periodic signal f (t) =p (i) for one tire rotation is obtained, where i=vt/d.
The signal f (t) obtained by one rotation of the tire is a periodic signal, and fourier series decomposition is performed on f (t) to obtain:
wherein a is 0 、A n Is the Fourier coefficient, a 0 The mean value is represented as such,indicating the phase angle.
And calculating a root mean square RMS value according to the Fourier series decomposition result obtained by the predicted noise periodic signal f (t), marking the root mean square RMS value as RMS_sim, and obtaining the predicted tire pattern noise by taking the root mean square value as the tire pattern noise value.
The key point of the scheme is that the acquisition and the determination of the weight matrix are that the positions of the transverse grooves are different, so that the positions of tire patterns are different, and finally predicted tire pattern noise is affected. The method for acquiring and determining the weight matrix comprises the following steps:
obtaining a tire circumferential pattern image according to each single-pitch image of the tire pattern;
performing an indoor single tire noise drum test on the pattern tire based on the tire circumferential pattern image to obtain a tested actual tire noise signal; the actual tire noise signal comprises a periodic pattern sound signal and a background noise signal;
processing the tested actual tire noise signals, separating to obtain tested periodic pattern sound signals, and calculating the root mean square value of the test signals;
and calculating to obtain the weight matrix X and the weight matrix Y by using a particle swarm optimization algorithm based on the minimized root mean square value of the test signal and the root mean square value of the predicted noise periodic signal.
Specifically, an indoor single tire noise drum test was performed on the basis of the pattern tire of the tire circumferential pattern image, wherein the microphone arrangement position was determined according to the ECER117 tire rule, the microphone arrangement position was 7.5m from the test center line, and the microphone height was 1.2m. As shown in fig. 4, according to the comparison between the indoor test result and the outdoor noise passing test result, the microphone measurement result at the point 7 is closest to the noise passing result, so that the equivalent position-point 7 is selected, and the 1/3 frequency multiplication spectrum measured by the microphone at the point 7 is selected as the identification of the calculation coefficient.
By the above test method, the actual tire noise signal is obtained, and the indoor test result contains periodic pattern sound signals and other background noise signals, so that the pattern sound signals of the test result need to be separated, and the specific operations include:
recording the time point t_tacho of each rotation of the tire using a rotation speed sensor (i.e., tachometer sensor);
since the steady-state speed V is selected for testing during the actual test, the instantaneous frequency of rotation is considered to be unchanged, and the instantaneous frequency of rotation rpmt=60/diff (t_tacho) corresponding to the time point t_tacho, wherein diff represents the difference;
in order to ensure the calculated instantaneous frequency conversion smoothness, let instantaneous frequency conversion rpmt=0.5 (rpmt (1: end-1) +rpmt (2: end)), which is equivalent to using 3 rotation speed pulses (i.e. tacho pulses), calculating the rotation speed rpm corresponding to the intermediate pulse; wherein 1:end-1 represents the previous point from the first point to the last point, and 2:end represents the second point to the last point;
because one difference is performed, time alignment is required, so that t_tacho=t_tacho (2:end-1);
to ensure consistency with the initial time, linear interpolation is performed on (t_tacho, rpmt), i.e. one rotational speed rpm is corresponding to each time point;
resampling is carried out on each circle of signal, wherein the sampling point number is consistent with the sampling frequency;
each piece of data obtained by sampling is subjected to discrete fourier transform DFT. If the tire rotates for N circles in the testing process, the discrete Fourier transform DFT obtained by calculation of the N circles is subjected to linear average to obtain a periodic pattern sound signal.
On the basis, a frequency range of 800-2500Hz is selected, the root mean square value of the obtained periodic pattern noise is calculated and recorded as RMS_test, based on the minimized root mean square value RMS_test of the test signal and the root mean square value RMS_sim of the predicted noise periodic signal, a matlab optimization tool box is used, and a particle swarm optimization algorithm is utilized to obtain the weighting matrices X and Y. Wherein the number of particle groups is selected to be 50. In the process of solving by using the particle swarm optimization algorithm, taking the least square of the difference between the RMS_test and the RMS_sim as an optimization target, continuously and iteratively solving, and finally solving to obtain the weighting matrixes X and Y.
And by using the obtained weighting matrixes X and Y, new patterns are selected for noise performance simulation and prediction, and compared with an actual detection result, the comparison result is shown in fig. 5, the error between the detection result and the prediction result is 0.5dB, and the accuracy is high.
In addition, the embodiment also provides an optimization method of tire pattern noise, which optimizes the patterns in a staggered manner, and comprises the following steps: and misplacing each row of patterns of the tire to form a plurality of tire pattern misplacement combination modes, and outputting the current optimal tire pattern misplacement combination mode based on the predicted tire pattern noise of the plurality of tire pattern misplacement combination modes. Specifically, optimizing tire tread noise includes:
fixing the first row of tire patterns, and adjusting the other rows of tire patterns according to the dislocation value and the dislocation step length to form a tire pattern dislocation combination mode;
predicting tire pattern noise according to the formed tire pattern dislocation combination mode; specifically, aiming at different columns of the tire pattern, adopting a weight matrix corresponding to each column to respectively acquire corresponding prediction noise, and then calculating and acquiring the prediction value of the overall noise of the tire pattern; in this embodiment, the foregoing calculation is direct addition calculation, which is actually the matrix multiplication method disclosed in this embodiment;
and (3) performing cyclic iteration adjustment operation, namely taking the tire pattern dislocation combination mode with the lowest predicted tire pattern noise as the current optimal dislocation combination mode, and outputting the current optimal tire pattern dislocation combination mode until iteration is finished.
Specifically, the tire pattern is optimized for misalignment, as shown in fig. 6, by fixing the 1 st row and shifting the 2 nd, 3 rd and 4 th rows, the pattern is optimized for misalignment, and the maximum shift value is selected to be ±l, and the shift step length is m, and the 2 nd, 3 rd and 4 th rows are combined in common (2×l /) 3 A kind of module is assembled in the module and the module is assembled in the module. And calculating estimated noise based on the prediction method according to each dislocation combination mode. When lower predicted tire tread noise (i.e., RMS value) occurs, then the current misalignment combining mode is replaced until the iteration is over, and the final misalignment combining mode is preserved. Through the optimization scheme, the optimization result is shown in fig. 7, and after dislocation, the noise is reduced by 1.1dB.
Example two
The present embodiment provides a prediction and optimization system for tire pattern noise, including:
the tire pattern image acquisition module is used for acquiring each single-pitch image of the tire pattern;
the tire pattern image processing module is used for describing pattern distribution conditions of the single-pitch image by using pixel values to obtain a pixel matrix of the single-pitch image; based on the weighting matrix, weighting is carried out on the pixel matrix of the single-pitch image to obtain a pixel weighting matrix of the single-pitch image, and then the pixel weighting matrix of the tire circumferential direction is obtained;
the tire pattern noise prediction module is used for acquiring a prediction noise periodic signal of one rotation of the tire based on a pixel weighting matrix of the tire circumference; and carrying out Fourier series decomposition on the predicted noise periodic signal, calculating a root mean square value, and obtaining predicted tire pattern noise.
Further, the system also comprises a tire pattern dislocation optimizing module which is used for dislocation each row of patterns of the tire to form a plurality of tire pattern dislocation combination modes, and outputting the current optimal tire pattern dislocation combination mode based on the predicted tire pattern noise of the plurality of tire pattern dislocation combination modes.
Example III
The present embodiment provides an electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the steps in the tire tread noise prediction and optimization method as described above.
Example IV
The present embodiment also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, perform the steps in the tire pattern noise prediction and optimization method as described above.
The steps involved in the second to fourth embodiments correspond to the first embodiment of the method, and the detailed description of the second embodiment refers to the relevant description of the first embodiment. The term "computer-readable storage medium" should be taken to include a single medium or multiple media including one or more sets of instructions; it should also be understood to include any medium capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any one of the methods of the present invention.
It will be appreciated by those skilled in the art that the modules or steps of the invention described above may be implemented by general-purpose computer means, alternatively they may be implemented by program code executable by computing means, whereby they may be stored in storage means for execution by computing means, or they may be made into individual integrated circuit modules separately, or a plurality of modules or steps in them may be made into a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
While the foregoing description of the embodiments of the present invention has been presented in conjunction with the drawings, it should be understood that it is not intended to limit the scope of the invention, but rather, it is intended to cover all modifications or variations within the scope of the invention as defined by the claims of the present invention.
Claims (10)
1. A method for predicting and optimizing tire pattern noise is characterized by comprising the following steps:
acquiring each single-pitch image of the tire pattern, and describing pattern distribution conditions of the single-pitch images by using pixel values to obtain a pixel matrix of the single-pitch images;
based on the weighting matrix, weighting is carried out on the pixel matrix of the single-pitch image to obtain a pixel weighting matrix of the single-pitch image, and then the pixel weighting matrix of the tire circumferential direction is obtained;
acquiring a predicted noise periodic signal of one rotation of the tire based on a pixel weighting matrix of the tire circumference;
and carrying out Fourier series decomposition on the predicted noise periodic signal, calculating a root mean square value, and obtaining predicted tire pattern noise.
2. A method for predicting and optimizing tire tread noise as in claim 1, wherein said method for obtaining a weight matrix comprises:
obtaining a tire circumferential pattern image according to each single-pitch image of the tire pattern;
performing an indoor single tire noise drum test on the pattern tire based on the tire circumferential pattern image to obtain a tested actual tire noise signal;
processing the tested actual tire noise signals, separating to obtain tested periodic pattern sound signals, and calculating the root mean square value of the test signals;
and calculating to obtain the weight matrix by using a particle swarm optimization algorithm based on the minimized root mean square value of the test signal and the root mean square value of the predicted noise periodic signal.
3. A method of predicting and optimizing tire tread noise as in claim 2, wherein the actual tire noise signal comprises a periodic tread sound signal and a background noise signal.
4. A method of predicting and optimizing tire tread noise as in claim 1, further comprising:
dislocation is carried out on each row of patterns of the tire, so that a plurality of tire pattern dislocation combination modes are formed;
and outputting the current optimal tire pattern dislocation combination mode based on the predicted tire pattern noise of the multiple tire pattern dislocation combination modes.
5. A method of predicting and optimizing tire tread noise as in claim 4, wherein optimizing tire tread noise comprises:
fixing the first row of tire patterns, and adjusting the other rows of tire patterns according to the dislocation value and the dislocation step length to form a tire pattern dislocation combination mode;
predicting tire pattern noise according to the formed tire pattern dislocation combination mode;
and (3) performing cyclic iteration adjustment operation, namely taking the tire pattern dislocation combination mode with the lowest predicted tire pattern noise as the current optimal dislocation combination mode, and outputting the current optimal tire pattern dislocation combination mode until iteration is finished.
6. A system for predicting and optimizing tire tread noise, comprising:
the tire pattern image acquisition module is used for acquiring each single-pitch image of the tire pattern;
the tire pattern image processing module is used for describing pattern distribution conditions of the single-pitch image by using pixel values to obtain a pixel matrix of the single-pitch image; based on the weighting matrix, weighting is carried out on the pixel matrix of the single-pitch image to obtain a pixel weighting matrix of the single-pitch image, and then the pixel weighting matrix of the tire circumferential direction is obtained;
the tire pattern noise prediction module is used for acquiring a prediction noise periodic signal of one rotation of the tire based on a pixel weighting matrix of the tire circumference; and carrying out Fourier series decomposition on the predicted noise periodic signal, calculating a root mean square value, and obtaining predicted tire pattern noise.
7. A tire tread noise prediction and optimization system as in claim 6, wherein said weight matrix acquisition method comprises:
obtaining a tire circumferential pattern image according to each single-pitch image of the tire pattern;
performing an indoor single tire noise drum test on the pattern tire based on the tire circumferential pattern image to obtain a tested actual tire noise signal;
processing the tested actual tire noise signals, separating to obtain tested periodic pattern sound signals, and calculating the root mean square value of the test signals;
and calculating to obtain the weight matrix by using a particle swarm optimization algorithm based on the minimized root mean square value of the test signal and the root mean square value of the predicted noise periodic signal.
8. The system for predicting and optimizing tire pattern noise as recited in claim 6, further comprising a tire pattern misalignment optimization module for misalignment of each row of tire patterns to form a plurality of tire pattern misalignment combinations, wherein the current optimal tire pattern misalignment combination is output based on the predicted tire pattern noise of the plurality of tire pattern misalignment combinations.
9. An electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the steps of a method of predicting and optimizing tire tread noise as claimed in any one of claims 1 to 5.
10. A computer readable storage medium storing computer instructions which, when executed by a processor, perform the steps of a method of predicting and optimizing tire tread noise as claimed in any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310340197.2A CN116579932A (en) | 2023-03-29 | 2023-03-29 | Method and system for predicting and optimizing tire pattern noise |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310340197.2A CN116579932A (en) | 2023-03-29 | 2023-03-29 | Method and system for predicting and optimizing tire pattern noise |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116579932A true CN116579932A (en) | 2023-08-11 |
Family
ID=87540265
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310340197.2A Pending CN116579932A (en) | 2023-03-29 | 2023-03-29 | Method and system for predicting and optimizing tire pattern noise |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116579932A (en) |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005029076A (en) * | 2003-07-09 | 2005-02-03 | Toyo Tire & Rubber Co Ltd | Tire for automobile excellent in groove wandering property |
| TW201400326A (en) * | 2012-06-18 | 2014-01-01 | Univ Nat Taiwan Ocean | Optimum ranking method of tire tread pitches for effectively reducing noise |
| CN104102820A (en) * | 2014-06-27 | 2014-10-15 | 清华大学 | Method and system for analyzing and forecasting tread pattern noise |
| CN107379899A (en) * | 2017-07-07 | 2017-11-24 | 淮阴工学院 | A kind of tire condition intelligent monitor system based on wireless sensor network |
| CN107796639A (en) * | 2016-08-30 | 2018-03-13 | 上海保隆汽车科技股份有限公司 | Tyre noise monitoring system and monitoring method |
| CN107848344A (en) * | 2015-08-25 | 2018-03-27 | 大陆轮胎德国有限公司 | For the method and its control device of the tread depth for determining tire tread |
| CN108614935A (en) * | 2018-04-24 | 2018-10-02 | 哈尔滨工大泰铭科技有限公司 | A method of fast implementing the prediction of tyre tread pitch noise |
| US20200094503A1 (en) * | 2016-12-23 | 2020-03-26 | Pirelli Tyre S.P.A. | Process and apparatus for applying noise-reducing elements to a tyre for vehicle wheels |
| US20200184278A1 (en) * | 2014-03-18 | 2020-06-11 | Z Advanced Computing, Inc. | System and Method for Extremely Efficient Image and Pattern Recognition and Artificial Intelligence Platform |
| CN112084682A (en) * | 2020-03-10 | 2020-12-15 | 中策橡胶集团有限公司 | TBR tire noise testing method and low-noise tire preparation |
| CN113886991A (en) * | 2021-10-18 | 2022-01-04 | 哈尔滨工业大学 | Method for predicting tire pattern impact noise |
| CN114643818A (en) * | 2022-03-07 | 2022-06-21 | 中策橡胶集团股份有限公司 | Tire pattern noise simulation prediction method, device and program product based on image recognition |
| US20220284470A1 (en) * | 2015-05-20 | 2022-09-08 | Continental Automotive Systems, Inc. | System and method for enhancing vehicle performance using machine learning |
| CN115240059A (en) * | 2022-07-13 | 2022-10-25 | 江苏海洋大学 | Improved PSO-SVM-based forward-looking sonar target recognition method |
| CN115839696A (en) * | 2023-02-27 | 2023-03-24 | 清华大学苏州汽车研究院（相城） | Tire pattern depth measuring method, device, storage medium and system |
-
2023
- 2023-03-29 CN CN202310340197.2A patent/CN116579932A/en active Pending
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005029076A (en) * | 2003-07-09 | 2005-02-03 | Toyo Tire & Rubber Co Ltd | Tire for automobile excellent in groove wandering property |
| TW201400326A (en) * | 2012-06-18 | 2014-01-01 | Univ Nat Taiwan Ocean | Optimum ranking method of tire tread pitches for effectively reducing noise |
| US20200184278A1 (en) * | 2014-03-18 | 2020-06-11 | Z Advanced Computing, Inc. | System and Method for Extremely Efficient Image and Pattern Recognition and Artificial Intelligence Platform |
| CN104102820A (en) * | 2014-06-27 | 2014-10-15 | 清华大学 | Method and system for analyzing and forecasting tread pattern noise |
| US20220284470A1 (en) * | 2015-05-20 | 2022-09-08 | Continental Automotive Systems, Inc. | System and method for enhancing vehicle performance using machine learning |
| CN107848344A (en) * | 2015-08-25 | 2018-03-27 | 大陆轮胎德国有限公司 | For the method and its control device of the tread depth for determining tire tread |
| CN107796639A (en) * | 2016-08-30 | 2018-03-13 | 上海保隆汽车科技股份有限公司 | Tyre noise monitoring system and monitoring method |
| US20200094503A1 (en) * | 2016-12-23 | 2020-03-26 | Pirelli Tyre S.P.A. | Process and apparatus for applying noise-reducing elements to a tyre for vehicle wheels |
| CN107379899A (en) * | 2017-07-07 | 2017-11-24 | 淮阴工学院 | A kind of tire condition intelligent monitor system based on wireless sensor network |
| CN108614935A (en) * | 2018-04-24 | 2018-10-02 | 哈尔滨工大泰铭科技有限公司 | A method of fast implementing the prediction of tyre tread pitch noise |
| CN112084682A (en) * | 2020-03-10 | 2020-12-15 | 中策橡胶集团有限公司 | TBR tire noise testing method and low-noise tire preparation |
| CN113886991A (en) * | 2021-10-18 | 2022-01-04 | 哈尔滨工业大学 | Method for predicting tire pattern impact noise |
| CN114643818A (en) * | 2022-03-07 | 2022-06-21 | 中策橡胶集团股份有限公司 | Tire pattern noise simulation prediction method, device and program product based on image recognition |
| CN115240059A (en) * | 2022-07-13 | 2022-10-25 | 江苏海洋大学 | Improved PSO-SVM-based forward-looking sonar target recognition method |
| CN115839696A (en) * | 2023-02-27 | 2023-03-24 | 清华大学苏州汽车研究院（相城） | Tire pattern depth measuring method, device, storage medium and system |
Non-Patent Citations (4)
| Title |
|---|
| JINN-TONG CHIU 等: "Application of a pattern recognition technique to the prediction of tire noise", 《JOURNAL OF SOUND AND VIBRATION》, vol. 350, pages 30 - 40, XP029173556, DOI: 10.1016/j.jsv.2015.04.013 * |
| 冯希金 等: "轮胎噪声研究进展", 《轮胎工业》, vol. 35, no. 09, pages 515 - 523 * |
| 邓兰祥: "基于轿车轮胎花纹噪声模型的优化设计研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅰ辑》, no. 02, pages 016 - 378 * |
| 陈理君 等: "轮胎花纹噪声仿真与优化系统软件设计", 《轮胎工业》, vol. 04, pages 199 - 203 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109858156B (en) | Vehicle and structure information simultaneous identification method based on axle coupling vibration | |
| WO2018005996A1 (en) | System, device, and method for feature generation, selection, and classification for audio detection of anomalous engine operation | |
| CN105527110B (en) | The appraisal procedure and device of automobile fuel ecomomy | |
| US11458784B2 (en) | Methods and apparatus to determine tire tread depth | |
| CN105973593A (en) | Rolling bearing health evaluation method based on local characteristic scale decomposition-approximate entropy and manifold distance | |
| US6711508B2 (en) | Method and apparatus for estimating tire air pressure | |
| CN108897905A (en) | Analysing Methods of Engine Noise | |
| CN112282952B (en) | Engine combustion fault determination method and device | |
| CN113918890B (en) | Low-load working condition construction method based on mobile window library | |
| CN112948965A (en) | Method for constructing automobile driving condition based on machine learning and statistical verification | |
| CN118070105A (en) | Intelligent self-adaptive pavement detection and maintenance method and system | |
| CN116579932A (en) | Method and system for predicting and optimizing tire pattern noise | |
| CN114136656A (en) | Method for constructing motor bench test working condition of pure electric operation automobile | |
| CN112668419B (en) | Engine emission prediction method based on vibration signal | |
| CN117390510A (en) | Engine running state prediction method, system and electronic equipment | |
| CN114383716B (en) | In-vehicle noise identification method based on conditional power spectrum analysis | |
| CN112343712B (en) | Sensitivity analysis method for engine suspension vibration | |
| KR102300965B1 (en) | Misfire diagnosis method and device of Multi cylinder four-stroke engine | |
| CN113155486A (en) | Durability simulation test method and system for power assembly suspension system | |
| CN115638900B (en) | Exhaust pipe temperature determination method and system, storage medium and electronic equipment | |
| CN106055857B (en) | The appraisal procedure and device of automobile fuel ecomomy | |
| CN118520253A (en) | Method, device and equipment for identifying engine fire fault under data security combination | |
| CN118760959A (en) | Small sample bearing fault diagnosis method based on time-frequency diagram double-flow CNN and SVM | |
| CN118072759A (en) | Noise separation method and device, electronic equipment and vehicle | |
| CN116227228A (en) | Transmission system part reliability verification method and verification device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20230811 |