A tire test analysis and reliability evaluation method based on damage evolution model

Abstract

The present invention provides a tire test analysis and reliability evaluation method based on a damage evolution model, which acquires data, including: acquiring 12 mechanical channel data, including force and displacement in three directions of the tire and torque and angle on three axes; identifying the periodic tread jitter value and sidewall movement value of the tire as deformation channel data; acquiring the tire temperature field as temperature channel data; data analysis, including: analyzing the tire reliability based on mechanical signals, visual deformation signals, and temperature field signals; reliability prediction, including: judging the tire damage probability based on the reliability analysis results of the mechanical signals, visual deformation signals, and temperature field signals.

Description

Technical Field

[0001] The present invention belongs to the technical field of tires, and in particular relates to a tire test analysis and reliability evaluation method based on a damage evolution model. Background Art

[0002] Aircraft and automobile tires face the risk of blowouts during use, which can easily cause loss of life and property. Therefore, accurate test prediction of tire reliability is of great significance. Existing research shows that tire blowouts during tests are mainly caused by damage to the internal cords or interfaces of the tire body, and these damages are difficult to produce visible changes on the outer surface of the tire, which makes it difficult to accurately predict the risk of tire blowouts. When conducting traditional tire tests according to current standards, testers are required to closely inspect the tire status and complete operations such as replenishing tire pressure at regular intervals. If a sudden blowout occurs at this time, it will cause a major safety accident. On the other hand, even if there are no testers around when the tire blows, the huge impact force generated by the blowout can easily damage the test bench, sensor system and other devices, which will significantly reduce the service life of the test equipment in the long run.

[0003] Therefore, a prediction method is needed that can accurately predict the reliability of tires in tests, determine whether the tires are abnormal, and predict the possibility of tire blowout in the future. Summary of the Invention

[0004] To address the above-mentioned issues, the present invention provides a tire test analysis and reliability evaluation method based on a damage evolution model. This method can predict tire blowouts based on the principles of tire internal structure and mechanical damage inversion. Based on the six-channel mechanical and deformation information in the tire test data, a mechanical model is used to establish the tire damage evolution law, determine whether the tire reliability is abnormal, and predict the possibility of tire blowout in the future.

[0005] The specific solution of the present invention is: a tire test analysis and reliability evaluation method based on a damage evolution model, the method comprising:

[0006] Acquire data, including: acquiring 12 mechanical channel data, including force and displacement in three directions of the tire, torque and angle on three axes; identifying the periodic tread vibration and sidewall movement of the tire as deformation channel data; and acquiring the tire temperature field as temperature channel data.

[0007] Data analysis, including tire reliability analysis based on mechanical signals, visual deformation signals, and temperature field signals;

[0008] Tire reliability analysis based on mechanical signals includes: using force and displacement in three directions and torque and angle information on three axes to monitor changes in tire mechanical stability in real time, and obtaining the tire's comprehensive stress state through mechanical model inversion or analyzing the tire's overall load spectrum using fast Fourier transform to instantly determine the tire's mechanical state;

[0009] Tire reliability analysis based on visual deformation signals includes: using the tire's periodic tread vibration and sidewall movement values ​​to detect tire deformation during movement, and judging the probability of tire failure based on real-time detection of tire deformation;

[0010] Tire reliability analysis based on temperature field signals includes: judging tire status based on local tire temperature;

[0011] Reliability prediction includes: judging the probability of tire damage based on reliability analysis results of mechanical signals, visual deformation signals, and temperature field signals.

[0012] Beneficial effects of the present invention:

[0013] 1. The tire test analysis and reliability evaluation method based on the damage evolution model provided by this invention utilizes the three-directional forces and three-axis torque information in tire test data to monitor changes in the tire's mechanical stability in real time during the test. The method also inverts the mechanical model to obtain the tire's comprehensive stress state, allowing for immediate determination of any abnormalities in the tire's mechanical state.

[0014] 2. The tire test analysis and reliability evaluation method based on the damage evolution model provided by this invention utilizes six-channel information from tire test data, combined with fast Fourier transform to analyze the tire's overall load spectrum. The tire's spectral response is used as one of the indicators for predicting tire blowouts, making tire reliability prediction more accurate.

[0015] 3. The tire test analysis and reliability evaluation method based on the damage evolution model provided by this invention utilizes computer vision recognition technology to quickly and intuitively detect tire deformation during movement by locking onto features such as the tire surface and carcass contour in multiple directions. This real-time detection of tire deformation can then be used to determine the likelihood of a tire blowout.

[0016] 4. The tire test analysis and reliability evaluation method based on the damage evolution model provided by the present invention, combined with the tire test big data generated by the above three points and utilizing the principles of probability, can accurately calculate the probability of a tire blowout within a certain period of time in the future, thereby minimizing the tire testing workload and shortening the tire R&D cycle while ensuring safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The tire test data acquisition system provided by the present invention;

[0018] Figure 2 A schematic diagram of the six directional components of the tire provided by the present invention;

[0019] Figure 3 This is a schematic diagram of the test data and nonlinear curve fitting results provided by the present invention;

[0020] Figure 4 This is a schematic diagram of the tread visual recognition preprocessing provided by the present invention;

[0021] Figure 5 A schematic diagram of the tire sidewall visual recognition preprocessing provided by the present invention;

[0022] Figure 6 This is a schematic diagram of the test data analysis and tire reliability prediction process provided by the present invention.

[0023] Reference numerals: 1-six-channel sensor; 2-tire; 21-longitudinal groove on tread; 22-tread marking line; 23-sidewall marking line; 24-tread profile; 3-image acquisition device; 4-test loading device; 5-rigid contact end; 7-thermal imaging system; 6-area; 61-raw data analysis curve A; 62-nonlinear fitting curve; 63-raw data analysis curve B. DETAILED DESCRIPTION

[0024] In order to make the objectives, technical solutions, and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other. To achieve the above-mentioned objectives, the present invention adopts the following technical solutions.

[0025] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the various components in a certain specific posture. If the specific posture changes, the directional indications will also change accordingly.

[0026] In a first aspect, the present invention provides a tire test reliability prediction method based on mechanical signals. The method utilizes a six-channel sensor mounted on a tire shaft or lever arm, which outputs signals from six channels at a specific frequency. The force signals include: Fx, Fy, Fz, Mx, My, and Mz, and the displacement signals include: dx, dy, dz, θx, θy, and θz. The x-axis indicates the tire's heading, the y-axis indicates the tire's lateral direction, and the z-axis indicates the tire's radial direction. F represents the force along the three axes, M represents the torque around the three axes, d represents the displacement along the three axes, and θ represents the angle around the three axes. Because tire damage accumulates and evolves during testing, the data from the six channels changes accordingly. The data from these six channels is fully recorded and analyzed in real time based on a theoretical model of tire damage evolution. Analysis methods include, but are not limited to, data discreteness analysis and Fourier transform. Combined with tire test data, the method determines whether the tire has experienced anomalies and predicts its reliability.

[0027] Secondly, the present invention provides a tire test analysis and reliability evaluation method based on a damage evolution model based on visual deformation signals. It uses optical measurement and computer vision recognition technology to track characteristic information such as the tire surface and tire body contour in multiple directions, and quickly and automatically calculates the external deformation and motion morphology parameters of the tire during movement. Also based on the tire damage evolution model, it judges the tire reliability and predicts whether it will burst.

[0028] In a third aspect, the present invention provides a tire test analysis and reliability evaluation method based on a damage evolution model based on thermal imaging signals. It uses an infrared thermal imaging system to track and monitor the temperature field information on the tire surface, quickly identify local heat accumulation on the tire surface, and combine it with the tire structure to estimate and predict the possibility of tire blowout.

[0029] The specific technical solutions are:

[0030] Figure 1 A schematic diagram of a tire test data acquisition system provided by the present invention includes:

[0031] The six-channel sensor 1 is rigidly connected to the test loading device 4 and is capable of outputting the magnitudes of the six forces or moments sensed by the sensor: Fx, Fy, Fz, Mx, My, and Mz; and the magnitudes of the displacements or rotations in the six directions: dx, dy, dz, θx, θy, and θz. These output signals, as data from 12 mechanical channels, are transmitted in real time to an external storage device (i.e., a computer) via wired or wireless means. The sensor's accuracy and range are selected based on the required test load; the output frequency should be no less than 20 Hz.

[0032] Tire 2 is the tire to be tested, and its axis is connected to the test loading device 4 and can rotate around the central axis.

[0033] Image acquisition devices 3 can be placed in multiple locations facing the tire's heading, lateral, and radial directions. Their image resolution should be as high as possible, no less than 2K, and their frame rate should be no less than 60fps. If conditions permit, high-speed cameras equipped with light sources can be used, and infrared cameras can also be added to capture the tire temperature field.

[0034] The test loading device 4 is used to apply radial force, deflection angle and other working conditions to the tire 2.

[0035] The rigid contact end 5 is used to cooperate with the test loading device 4 to apply a corresponding test load to the tire 2 .

[0036] The thermal imaging system 7 is used to collect the surface temperature field information of the tire 2; and obtain the tire temperature field as temperature channel data.

[0037] In one embodiment of the present invention, the tire generates significant heat during the test due to the self-heating properties of rubber. For a perfectly healthy tire, the temperature field during a normal rolling test should be uniform around the circumference. However, at some point during the test, the tire may develop uneven internal damage, leading to localized heat accumulation. When the temperature in this area reaches a temperature high enough to cause tire material failure (such as the rubber melting point), a tire blowout is inevitable. Therefore, a thermal imaging system can be used to issue a warning before the local tire temperature reaches the material failure temperature, allowing the test to be stopped and the tire to cool down.

[0038] Figure 2 The schematic diagram of the six-channel mechanical components of the tire provided by the present invention is mainly intended to standardize the description of the six-directional components in the present invention, wherein:

[0039] Fx represents the tire's yaw force; Fy represents the tire's lateral force; Fz represents the tire's radial force; Mx represents the tire's moment about the yaw axis; My represents the tire's moment about the lateral axis; and Mz represents the tire's moment about the radial axis. Furthermore, the components of the tire's displacement in the six channels correspond to the mechanical components: dx corresponds to Fx, dy corresponds to Fy, dz corresponds to Fz, θx corresponds to Mx, θy corresponds to My, and θz corresponds to Mz.

[0040] Figure 3This is a schematic diagram of the experimental data and nonlinear curve fitting results provided by the present invention. Region 6 includes raw data analysis curve A 61 and nonlinear fitting curve 62. Raw data analysis curve A 61 is obtained by processing the six-channel data output by the six-channel sensor 1 through certain mathematical methods and mechanical inversion. Nonlinear fitting curve 62 is obtained by performing nonlinear fitting of raw data analysis curve A 61 using a classic iterative regression algorithm such as Levenberg-Marquardt. Its forms include but are not limited to trigonometric functions, power polynomials, and Boltzmann functions.

[0041] In one embodiment of the present invention, a tire 2 is subjected to a long-term continuous rolling test, during which the six-channel sensor 1 continuously outputs data collected by the six channels. Since the mechanical properties of the tire 2 itself will decline to a certain extent after a long period of rolling, the data of the six channels will also change to a certain extent as time goes by. In one possible case, when the tire 2 produces the same radial deformation dz, Fz gradually decreases over time, that is, the radial stiffness Kz=Fz / dz of the tire decreases. When the radial stiffness Kz decreases to a certain extent, the tire 2 is damaged. At this time, if the time from the start of the test to the damage of the tire 2 is divided into several sections, and the Kz data in each period are averaged, a result similar to the following will be obtained. Figure 3 If the original data analysis curve B63 shows a trend, then for tires of the same type, the same curve law can be used to predict the reliability of the tires. When the Kz value of the tested tire 2 drops to the dangerous level obtained by big data experience accumulation, an alarm is issued and the test is stopped.

[0042] In another embodiment of the present invention, a tire 2 undergoes an intermittent high-load rolling test. Over time, the tire 2 develops internal damage that cannot be directly observed, causing fluctuations in multiple mechanical performance indicators of the tire 2. In one possible scenario, the internal cord on one side of the tire 2 breaks, causing the tire to wobble in the y direction. At this time, the values ​​of the six-channel sensor fluctuate to a certain extent. Therefore, the variance of the six component force signals is calculated within each time period of t seconds to evaluate their stability. The formula for calculating the variance σ is:

[0043] (1)

[0044] Where N is the number of samples, Xi is the sample value, and μ is the sample average. The data curve after the above processing is as follows Figure 3 The original data analysis curve A61 is shown in FIG. 61. The original data analysis curve A61 is analyzed using the correlation function , preferably the Boltzmann function The nonlinear fitting curve 62 is obtained by fitting, where a, b, and c are constants. The nonlinear fitting curve 62 is the damage evolution model of the tire under the working condition. For the same type of damage form of the tire under the same working condition, the value of the nonlinear fitting curve 62 is used. Make a pre-judgment to determine the value of the nonlinear fitting curve 62 Whether the critical value has been reached. These critical values ​​are obtained through multiple tests. Each operating condition and each type of tire corresponds to a set of critical values. During the test, the parameters and the values ​​reached by the curve when the tire blows out are recorded. The distribution pattern of the values ​​obtained from multiple tests conforms to the normal distribution. The normal distribution pattern can then be used to calculate the probability of a blowout.

[0045] In another embodiment of the present invention, when a tire 2 is undergoing an intermittent high-load rolling test, the data from the six-channel sensor can be Fourier transformed to obtain its spectrum curve. The characteristic values ​​in the spectrum curve are then taken to obtain a new raw data analysis curve A 61. The above process is repeated to achieve accurate early warning. The Fourier transform expression is:

[0046] (2)

[0047] Where ω represents frequency, t represents time, θ(t) is the spectral density function, and function Θ(ω) is the Fourier transform of θ(t).

[0048] Figure 4 、 Figure 5 The schematic diagrams of the tread visual recognition preprocessing and the sidewall visual recognition preprocessing provided by the present invention respectively include:

[0049] longitudinal grooves 21 on the tread;

[0050] The tread marking line 22 is a smooth marking line drawn on the tire tread with a certain width around the circumference of the tire. The marking line can be a solid line or a dotted line.

[0051] The sidewall marking line 23 is a circumferential curve drawn on the sidewall of the tire. It should be as round as possible and concentric with the wheel axle. The width of the marking line 23 should also be kept constant. It can be a solid line or a dashed line.

[0052] The number of the above-mentioned marking lines is not unique, and they can also be replaced by marking points, speckles, etc. with a certain degree of recognition;

[0053] Tread profile 24.

[0054] In a possible embodiment, the present invention arranges an image acquisition device 3 on the heading direction and the side of the tire 2 respectively. When the tire 2 is subjected to a rolling test, the viewing angles seen by the two image acquisition devices 3 are the same as those of the tire 2. Figure 4 、 Figure 5In the initial state, the tread marking line 22 and the sidewall marking line 23 on the tire 2 have regular shapes and appear as a stable curve or straight line in the field of view of the image acquisition device 3. As the performance of the tire 2 gradually deteriorates, the dynamic stability of the tire 2 also changes. In the field of view of the image acquisition device 3, the tread marking line 22 begins to periodically vibrate left and right, and the shape of the sidewall marking line 23 also moves periodically. These two periodic vibration values ​​are recorded as and , and The size can be converted according to the image pixels and shooting distance. Identify the periodic tread vibration value and sidewall movement value of the tire as deformation channel data; when and When the value reaches a certain value, it indicates that the internal damage of tire 2 has accumulated to a certain extent and a tire blowout will occur soon. and The critical value of the destruction theory is modified, where and The calculation formula is: , x0, y0 are the coordinates of the feature point before deformation, and x1, y1 are the coordinates of the feature point after deformation. The tire damage probability W can be calculated using the following formula:

[0055] (3)

[0056] Where, It is obtained through fitting and The feature prediction function, M is The total number of tires that reach the theoretical critical value; m is The total number of tires that burst after reaching the theoretical critical value. The probability calculation method here uses the normal distribution function Calculation, the probability calculation method here is also applicable to the six-channel data processing method described above.

[0057] Figure 6 This is a schematic diagram of the test data analysis and tire reliability prediction process provided by the present invention. The left frame contains the original test data of the tire 2 collected by the six-channel sensor 1 or the image acquisition device 3 during the test. This data is then analyzed and processed in real time and converted into parameters that can represent the state of the tire 2 using the various processing methods described above. Finally, based on the large amount of test data, a probabilistic relationship between these parameters and tire reliability is established to achieve accurate prediction of tire reliability.

[0058] In a possible embodiment, for a certain type of tire 2, a certain amount of test data has been accumulated according to the above method, including the variance, prediction function, and the like of the 12 channels Fx, Fy, Fz, Mx, My, Mz, dx, dy, dz, θx, θy, and θz. , and the prediction function of the tire surface marking jitter value , each prediction function 、 Each channel corresponds to a critical value or critical range. However, for any tire, not every channel's prediction function will reach the critical value or critical range before a tire blows out. Therefore, it is necessary to weight the signal of each channel by multiplying it by a weighting constant E for different types of tires and working conditions. The weighting constant E is determined as follows:

[0059] For the kth channel, the critical value of its prediction function in a certain experiment is (x) or , the quotient of the critical value of the prediction function of the channel and the sum of the critical values ​​of the prediction functions of all other channels is the weighted constant corresponding to the channel :

[0060] or (4)

[0061] Then, the data signal evolution characteristics under different working conditions can be integrated to summarize the multi-channel tire test analysis and reliability evaluation method based on the damage evolution model applicable to various test conditions, which can be summarized as the following formula (5). The meaning of formula (5) is that based on the mechanical signals and visual signals mentioned in the previous embodiments, the probability relationship between these channel signals and tire reliability is obtained respectively. For different types of tires, such as bias tires or radial tires, the relationship between tire reliability and these channels is different, so it is necessary to weight the signal of each channel; at the same time, the corresponding reliability changes of each tire when facing different working conditions are different. Different working conditions are functions of the test loading time and load size, so different working conditions also need to be weighted here, and then the tire blowout possibility prediction function W is obtained:

[0062] (5)

[0063] Where M is the total number of tires that reach the theoretical critical value; m is the total number of tires that burst after reaching the theoretical critical value; q represents the total number of operating conditions the tire experiences during the test; and p represents the total number of data signal outputs, including 24 nonlinear fitting curves obtained by fitting 12 force / displacement signal channels (Fx, Fy, Fz, Mx, My, Mz, dx, dy, dz, θx, θy, θz). , several marker line jitter values ​​Δ obtained from several visual signal channels n , the abnormal temperature value T obtained by the temperature field signal channel, F represents the load on the tire in each working condition, S represents the time the tire experiences in each working condition in the test, is the weighted constant of each working condition's contribution to tire blowout, It is the weighted constant of the prediction capability of each channel under each working condition, which can be continuously revised based on the test big data. Refers to the characteristic prediction function used in each working condition, including the prediction function mentioned above 、 The rest of the parameters have the same meanings as in formula (3).

[0064] In another embodiment of the present invention, a method for evaluating tire reliability through temperature field is provided: an infrared camera 7 is added to collect the temperature field of tire 2. When there is no damage inside tire 2, the temperature field of tire 2 should be uniformly distributed 360° along its circumference. As the tire test progresses, local damage occurs inside tire 2, which is manifested as abnormal local heat accumulation in the area corresponding to the damage location on the outer surface of tire 2. The highest abnormal temperature collected is recorded as temperature T. Before this, the lowest melting point t of the rubber used in the tire needs to be determined in advance. When T=0.9t, an early warning needs to be issued. This temperature field data is used in parallel with the damage probability W calculated by formula (5) to judge the tire reliability. When the obtained temperature field reaches 90% of the material melting point, that is, T=0.9t, an early warning is issued. At this time, even if the damage probability W does not reach the warning value, it is necessary to stop loading the tire; if the temperature field T<0.9t, but the damage probability W reaches 0.9, it is still necessary to stop loading the tire. It should be noted that the damage probability of 0.9 and the temperature reaching 90% of the melting point described here can be appropriately adjusted according to the test needs to ensure test safety.

Claims

1. A tire test analysis and reliability evaluation method based on a damage evolution model, characterized in that: The method comprises: Acquire data, including: acquiring 12 mechanical channel data, including force and displacement in three directions of the tire, torque and angle on three axes; identifying the periodic tread vibration and sidewall movement of the tire as deformation channel data; and acquiring the tire temperature field as temperature channel data. Data analysis, including tire reliability analysis based on mechanical signals, visual deformation signals, and temperature field signals; Tire reliability analysis based on mechanical signals includes: using force and displacement in three directions and torque and angle information on three axes to monitor changes in tire mechanical stability in real time, and obtaining the tire's comprehensive stress state through mechanical model inversion or analyzing the tire's overall load spectrum using fast Fourier transform to instantly determine the tire's mechanical state; Tire reliability analysis based on visual deformation signals includes: using the tire's periodic tread vibration and sidewall movement values ​​to detect tire deformation during movement, and judging the probability of tire failure based on real-time detection of tire deformation; Tire reliability analysis based on temperature field signals includes: judging tire status based on local tire temperature; Reliability prediction includes: judging the probability of tire damage based on reliability analysis results of mechanical signals, visual deformation signals, and temperature field signals.

2. The method according to claim 1, characterized in that The forces in the three directions and the torque information of the three axes are Fx, Fy, Fz, Mx, My, and Mz respectively; the corresponding displacements and rotation angles in each direction are dx, dy, dz, θx, θy, and θz, where Fx represents the directional force of the tire; Fy represents the lateral force of the tire; Fz represents the radial force of the tire; Mx represents the moment of the tire around the directional axis; My represents the moment of the tire around the lateral axis; and Mz represents the moment of the tire around the radial axis. The displacement components in the six channels correspond to the mechanical components, the displacement dx corresponds to the directional force Fx, dy corresponds to the lateral force Fy, dz corresponds to the radial force Fz, the rotation angle θx corresponds to the directional axis moment Mx, the rotation angle θy corresponds to the lateral axis moment My, and the rotation angle θz corresponds to the radial axis moment Mz; several periodic tread jitter values ​​or sidewall movement values ​​are recorded as Δ n ; Local abnormal temperature signal T on the tire surface.

3. The method according to claim 2, characterized in that The comprehensive stress state of the tire is obtained by inversion of the mechanical model, including: calculating the variance of the force and displacement in the three directions and the torque and angle of the three axes in each time period of t seconds. The variance σ is calculated as follows: （1） Where N is the number of samples, is the sample value, μ is the sample average, and the original data analysis curve is obtained respectively, and the nonlinear fitting curve is obtained by fitting with the correlation function , the nonlinear fitting curve is the damage evolution model of the tire under this working condition; It is a characteristic prediction function based on mechanical signal analysis.

4. The method according to claim 3, characterized in that The related function forms include but are not limited to the Boltzmann function. The specific formula of the Boltzmann function is: , where a, b, and c are constants.

5. The method according to claim 3, characterized in that The overall tire load spectrum is analyzed by fast Fourier transform, which includes: performing Fourier transform on the force and displacement in three directions and the torque and angle of rotation on three axes to obtain their spectrum curves respectively, taking the characteristic values ​​in the spectrum curves to obtain new raw data analysis curves, and repeating the nonlinear curve fitting process. The expression of the Fourier transform used is: （2） Where ω represents frequency, t represents time, θ(t) is the spectral density function, and function Θ(ω) is the Fourier transform of θ(t).

6. The method according to claim 3, characterized in that Several periodic tread vibration values ​​or sidewall movement values ​​are recorded as Δ n , and the way to obtain it is: Marking tread marks are marks drawn on the tire tread with a uniform width around the circumference of the tire; Marking sidewall marks are marks drawn on the sidewall of the tire, around the circumference of the tire side, concentric with the wheel axle, and with a uniform width; Several image acquisition devices are arranged on the heading direction and side of the tire. When the tire rolls, each image acquisition device collects images. As the tire performance gradually degrades, the marking line on the tread will periodically shake in the field of view of the image acquisition device, and the shape of the sidewall marking line will also periodically move. The periodic shaking value or movement value is recorded as n ; The obtained information after fitting n Feature prediction function .

7. The method according to claim 6, characterized in that The fitted n Feature prediction function The fitting methods include but are not limited to the Boltzmann function. The specific formula of the Boltzmann function is: , where a, b, and c are constants.

8. The method according to claim 6, characterized in that The number of marks is 1 or more, in the form of marking lines, marking points, or speckles.

9. The method according to claim 6, characterized in that The signal of each channel is multiplied by a weighting constant E for weighted processing according to different types of tires and working conditions. The weighting constant E is determined as follows: For the kth channel, the critical value of the prediction function is (x) or , the weight constant corresponding to the channel for: or (4).

10. The method according to claim 2, characterized in that The temperature field monitoring method is as follows: an infrared camera is added to the image acquisition device to collect the tire temperature field, and the highest abnormal temperature collected is recorded as temperature T.

11. The method according to claim 9, characterized in that Judgment based on the reliability analysis results of mechanical signals, visual deformation signals, and temperature field signals, including: the probability of failure of multi-channel tires under various test conditions for: （5） Where M is the total number of tires that reach the theoretical critical value; m is the total number of tires that have blown out after reaching the theoretical critical value; q represents the total number of working conditions the tire has experienced; and p represents the total number of data signal outputs, including the nonlinear fitting curve obtained by fitting the 12 force / displacement signal channels, namely Fx, Fy, Fz, Mx, My, Mz, dx, dy, dz, θx, θy, and θz. , several marker line jitter values ​​Δ obtained from several visual signal channels n ; F represents the load on the tire in each working condition, S represents the time the tire experiences in each working condition during the test, is the weighted function of each working condition’s contribution to tire blowout, is the weighted constant of the prediction capability of each channel under each working condition, Refers to the feature prediction function of each channel data, including 、 .

12. The method according to claim 11, characterized in that Determining the tire damage probability based on the reliability analysis results of mechanical signals, visual deformation signals, and temperature field signals also includes: calculating the temperature field data damage probability W based on the abnormal temperature value T obtained from the temperature field signal channel and concurrently determining the tire reliability. When the resulting temperature field reaches 90% of the material melting point, that is, T=0.9t, an early warning is issued. At this time, even if the damage probability W has not reached the warning value, loading the tire is stopped. If the temperature field T is less than 0.9t, but the damage probability W has reached 0.9, loading the tire is stopped.

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