Method and system for predicting service life of automobile tire

Abstract

The invention relates to the technical field of tire detection, and discloses an automobile tire service life prediction method and system, the system comprises a support device, a sliding assembly, a sensor assembly and a control module; the acquisition unit acquires parameter information of the tested tire to determine a downward pressure when the tested tire is in contact with the sliding assembly, controls the driving motor to rotate to acquire vibration data of the tested tire within a preset time, and acquires temperature data of the tested tire based on the infrared thermometer; the processing unit performs normal distribution processing on the vibration data to determine vibration representative data; establishing a temperature distribution diagram according to the temperature data; determining a first life prediction value of the tested tire according to the tread wear; the judgment unit determines a wear evaluation value; and the adjusting unit adjusts the first service life prediction value to obtain a final tire service life prediction value. According to the method, the vibration and temperature anomaly evaluation values are combined with the tread wear data, so that the tire life is dynamically adjusted and predicted, and the prediction precision of the tire life is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of tire detection, and in particular to a method and system for predicting the service life of automobile tires. Background Art

[0002] Tires are important components of vehicles and are used to carry the movement of the entire vehicle. Generally, tires have a standard life when they leave the factory. However, due to different factors such as the tire's usage environment and operating habits, there is usually a large gap between the actual life of the tire and the standard life. At present, more than half of the traffic accidents on highways in my country are caused by tire wear problems, and a large number of accidents are caused by tire blowouts caused by severe tire wear. Therefore, accurate life prediction of vehicle tires can prevent most vehicle accidents before they happen.

[0003] Traditional life prediction methods are often based only on tread wear or historical tire usage, while ignoring the real-time status of the tire during operation, such as temperature distribution and vibration. This prediction method is too simplistic and cannot reflect the impact of factors such as road conditions, load conditions, and ambient temperature changes on tire life in actual use. Therefore, the prediction accuracy is low, which can easily lead to sudden failures of tires in actual use. In addition, the current tire life prediction lacks effective detection of local defects in tires, especially abnormal temperature areas and small vibration anomalies are often difficult to identify at an early stage. Local overheating or abnormal vibration of the tire may lead to local fatigue and early failure. If it is not discovered and adjusted in time, it will significantly shorten the actual life of the tire and even cause safety hazards.

[0004] Therefore, it is necessary to design a method and system for predicting the service life of automobile tires to solve the problems existing in the current technology. Summary of the invention

[0005] In view of this, the present invention proposes a method and system for predicting the service life of automobile tires, aiming to solve the problems of low data accuracy and poor data reliability caused by single detection data in current tire life prediction technology.

[0006] In one aspect, the present invention provides a vehicle tire service life prediction system, comprising:

[0007] A support device, a sliding assembly, a sensor assembly and a control module; the support device includes a support frame and a drive motor, the support frame is used to fix the tire under test, the drive motor is used to drive the tire under test to rotate on the upper surface of the sliding assembly, the sensor assembly includes a vibration sensor and an infrared thermometer, the vibration sensor assembly is arranged on the tire under test, and the infrared thermometer is arranged on the support frame to collect an infrared image of the tire under test;

[0008] The control module includes a collection unit, a processing unit, a judgment unit and an adjustment unit;

[0009] The acquisition unit is configured to acquire parameter information of the tire under test, wherein the parameter information includes specification size and tread wear, and the downforce of the tire under test when in contact with the sliding assembly is determined according to the specification size. The acquisition unit is also configured to control the rotation of the drive motor. When the rotation speed of the tire under test is stable, the acquisition unit acquires vibration data of the tire under test within a preset time, stops the drive motor after the preset time, and acquires temperature data of the tire under test based on the infrared thermometer.

[0010] The processing unit is configured to perform normal distribution processing on the vibration data to determine vibration representative data; the processing unit is also configured to establish a temperature distribution map according to the temperature data; the processing unit is also configured to determine a first life prediction value of the tested tire according to the tread wear;

[0011] The judgment unit is configured to judge whether the tested tire has a defect according to the temperature distribution map, and determine the defect assessment value according to the temperature distribution map when a defect exists; the judgment unit is also configured to judge whether the tested tire has surface wear according to the vibration representative data, and determine the wear assessment value according to the vibration representative data when surface wear exists;

[0012] The adjustment unit is configured to adjust the first life prediction value according to the defect assessment value and the wear assessment value to obtain a final tire life prediction value.

[0013] Furthermore, when the acquisition unit determines the downforce when the tested tire contacts the sliding assembly according to the specification size, it includes:

[0014] The specifications and dimensions include rated load, tire width and tire contact area coefficient;

[0015] Fd = Pr × K × L × C × Sf;

[0016] Wherein, Fd represents downforce, Pr represents rated load, K represents tire width, L represents tire ground contact length, C represents tire contact area coefficient, Sf represents safety factor, and Sf takes values ​​of [0.7, 1.0].

[0017] Furthermore, the processing unit performs normal distribution processing on the vibration data to determine the vibration representative data, including:

[0018] The processing unit integrates the vibration data to form a vibration data set, and divides the vibration data set into a plurality of sub-vibration data sets with one rotation of the tested tire as a period, wherein the number of vibration data in the sub-vibration data sets is the same;

[0019] Perform kernel density estimation on the vibration data at the same position in all sub-vibration data sets, and determine the data with the highest probability as the representative vibration data at that position;

[0020] Combining vibration representative data at different positions to form the vibration representative data;

[0021] When performing the kernel density estimation, the Gaussian kernel density function is:

[0022]

[0023] Where n is the total number of sub-vibration data sets, h represents the smoothing bandwidth, and s i represents the i-th data point, and x represents any variable in the overall data.

[0024] Furthermore, the smoothing bandwidth is calculated by the following formula:

[0025]

[0026] Wherein, h represents the smoothing bandwidth, σ represents the standard deviation of the vibration data at the same position in all sub-vibration data sets, β1 represents the data skewness, β2 represents the data kurtosis, n represents the total number of sub-vibration data sets, and k represents the adjustment coefficient;

[0027] The data skewness is calculated by the following formula:

[0028]

[0029] The data kurtosis is calculated by the following formula:

[0030]

[0031] Where n represents the total number of sub-vibration data sets, s i represents the i-th data point, represents the mean of the vibration data at the same position in all sub-vibration data sets, and σ represents the standard deviation of the vibration data at the same position in all sub-vibration data sets.

[0032] Furthermore, when the processing unit determines the first life prediction value of the tested tire according to the tread wear, it includes:

[0033] The processing unit compares the tread wear with a first preset wear and a second preset wear respectively, and determines a first life prediction value of the tested tire according to the comparison result, wherein the first preset wear is less than the second preset wear;

[0034] When the tread wear is less than or equal to the first preset wear, the first life prediction value of the tested tire is determined to be the first preset life prediction value; when the tread wear is greater than the first preset wear and less than or equal to the second preset wear, the first life prediction value of the tested tire is determined to be the second preset life prediction value; when the tread wear is greater than the second preset wear, the first life prediction value of the tested tire is determined to be the third preset life prediction value; the first preset life prediction value is greater than the second preset life prediction value, and the second preset life prediction value is greater than the third preset life prediction value.

[0035] Furthermore, when the judging unit judges whether the tested tire has defects according to the temperature distribution diagram, it includes:

[0036] In the temperature distribution diagram, a central point (x 0 ,y 0 , z 0 );

[0037] Determine the radius of the spherical area by taking the central point as the center of the circle and r as the neighborhood radius;

[0038] Calculate the average temperature of the spherical domain according to the temperature values ​​of each coordinate point within the radius of the spherical domain;

[0039] When the difference between the temperature data of the center point and the average temperature of the spherical area is greater than the temperature difference threshold, it is determined that the temperature of the center point is abnormal;

[0040] The average temperature of the spherical region is calculated by the following formula:

[0041]

[0042] Among them, T avg represents the average temperature of the sphere, r represents the radius of the sphere, (x 0 ,y 0 , z 0 ) represents the coordinates of the center point, and T(x, y, z) represents the temperature data at point (x, y, z) on the temperature distribution graph.

[0043] Furthermore, when the determination unit determines the defect assessment value according to the temperature distribution diagram when a defect exists, it includes:

[0044] The judgment unit collects the number of points where the temperature is abnormal, and determines the defect assessment value according to the number of points, and the defect assessment value is proportional to the number of points.

[0045] Further, the judging unit judges whether the tested tire has surface wear according to the vibration representative data, and when surface wear exists, determines the wear assessment value according to the vibration representative data, including:

[0046] The judging unit compares the vibration data in the vibration representative data with the vibration thresholds respectively, and when the vibration representative data includes data greater than the vibration threshold, the judging unit determines that the tested tire has surface wear;

[0047] When surface wear exists, the judgment unit determines a vibration difference value according to the vibration representative data and the vibration threshold value, wherein the vibration difference value is a difference between data in the vibration representative data that is greater than the vibration threshold value and the vibration threshold value, and when there are multiple data in the vibration representative data that are greater than the vibration threshold value, the vibration difference value is calculated according to the maximum value of the vibration data;

[0048] The wear evaluation value is determined according to the vibration difference, and the wear evaluation value is proportional to the vibration difference.

[0049] Furthermore, the adjusting unit adjusts the first life prediction value according to the defect assessment value and the wear assessment value to obtain a final tire life prediction value, including:

[0050]

[0051] Among them, Lz represents the final tire life prediction value, L represents the first life prediction value, Et represents the defect assessment value, Ev represents the wear assessment value, N represents the number of temperature abnormality points, ΔTmax represents the maximum temperature difference in the spherical domain, and ΔVmax represents the vibration difference.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows: by introducing a support device, a sliding assembly, a sensor assembly and a control module, the tire can be fully monitored in real time and data collected, overcoming the problem of the single means of traditional tire life prediction. The vibration sensor and the infrared thermometer are used to detect the vibration state and temperature distribution of the tire respectively. Combined with the control module, these data are processed into a normal distribution and the temperature distribution diagram is analyzed to identify local defects and wear conditions on the tire surface. By combining the vibration and temperature anomaly assessment values ​​with the tread wear data, the tire life can be dynamically adjusted and accurately predicted. The accuracy of tire life prediction is improved, which can effectively reduce traffic accidents caused by tire wear.

[0053] On the other hand, the present application also provides a method for predicting the service life of a vehicle tire, which is applied to the above-mentioned vehicle tire service life prediction system, comprising:

[0054] Collecting parameter information of the tested tire, the parameter information including specification size and tread wear, determining the downforce of the tested tire when it contacts the sliding assembly according to the specification size, controlling the rotation of the driving motor, collecting vibration data of the tested tire within a preset time when the rotation speed of the tested tire is stable, stopping the driving motor after the preset time, and collecting temperature data of the tested tire based on an infrared thermometer;

[0055] Performing normal distribution processing on the vibration data to determine vibration representative data; establishing a temperature distribution diagram according to the temperature data; determining a first life prediction value of the tested tire according to the tread wear;

[0056] Determine whether the tested tire has defects according to the temperature distribution map, and determine the defect assessment value according to the temperature distribution map when defects exist; determine whether the tested tire has surface wear according to the vibration representative data, and determine the wear assessment value according to the vibration representative data when surface wear exists;

[0057] The first life prediction value is adjusted according to the defect assessment value and the wear assessment value to obtain a final tire life prediction value.

[0058] It is understandable that the above-mentioned automobile tire service life prediction method and system have the same beneficial effects, which will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0060] Figure 1 A structural block diagram of a vehicle tire service life prediction system provided by an embodiment of the present invention;

[0061] Figure 2 A flowchart of a method for predicting the service life of a vehicle tire provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0062] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0063] With the rapid development of the automobile industry, tires, as key components of vehicles, bear the entire weight of the vehicle and are in direct contact with the road surface, playing a vital role in the safe driving of the vehicle. The performance and service life of tires directly affect the driving stability and ride comfort of the vehicle. However, although tires have a clear standard life when they leave the factory, due to the complex and changeable use environment during actual driving, such as road conditions, climate conditions, driving habits, load conditions and other factors, the actual life of tires is often much lower than their standard life. In addition, tires will inevitably experience wear, fatigue, aging and other problems in long-term use, which will shorten their life and even lead to dangerous situations.

[0064] According to statistics, tire failure is one of the important causes of traffic accidents, especially on highways, where tire blowouts caused by tire wear, abnormal air pressure or structural damage account for a considerable proportion. In particular, when a tire has local abnormalities such as overheating, fatigue cracks or excessive wear during driving, if it cannot be detected in time, it may cause the tire to fail suddenly, increasing the risk of traffic accidents. Therefore, how to dynamically and real-time predict the service life of tires through more accurate technical means has become a problem that needs to be solved urgently.

[0065] Traditional tire life prediction methods mainly rely on the analysis of simple historical data such as tread wear, tire usage time, and mileage. This type of method cannot effectively capture the real-time status of the tire during operation, such as important parameters such as tire temperature changes and vibration characteristics, resulting in insufficient prediction accuracy. In addition, traditional methods have limited ability to identify local defects of tires, especially potential problems such as local overheating of tires and abnormal small vibrations, which are difficult to detect early and can easily cause sudden failures of tires during actual driving. In view of the complex working environment of tires, the existing technology lacks a comprehensive, multi-parameter coordinated life prediction model, which is difficult to meet the high safety requirements of modern automobiles. Therefore, it is necessary to design a method and system for predicting the service life of automobile tires to solve the problems existing in current technology.

[0066] In some embodiments of the present application, see Figure 1As shown, a vehicle tire service life prediction system includes: a support device, a sliding assembly, a sensor assembly and a control module. The support device includes a support frame and a drive motor, the support frame is used to fix the tire under test, the drive motor is used to drive the tire under test to rotate on the upper surface of the sliding assembly, the sensor assembly includes a vibration sensor and an infrared thermometer, the vibration sensor assembly is arranged on the tire under test, and the infrared thermometer is arranged on the support frame to collect an infrared image of the tire under test.

[0067] The control module includes a collection unit, a processing unit, a judgment unit and an adjustment unit.

[0068] The acquisition unit is configured to acquire parameter information of the tire under test, including specification size and tread wear, and determine the downforce of the tire under test when it contacts the sliding assembly based on the specification size. The acquisition unit is also configured to control the rotation of the drive motor. When the rotation speed of the tire under test is stable, the acquisition unit acquires vibration data of the tire under test within a preset time, stops the drive motor after the preset time, and acquires temperature data of the tire under test based on an infrared thermometer.

[0069] The processing unit is configured to perform normal distribution processing on the vibration data to determine vibration representative data. The processing unit is also configured to establish a temperature distribution map according to the temperature data. The processing unit is also configured to determine a first life prediction value of the tested tire according to tread wear.

[0070] The judging unit is configured to judge whether the tested tire has defects according to the temperature distribution map, and determine the defect evaluation value according to the temperature distribution map when defects exist. The judging unit is also configured to judge whether the tested tire has surface wear according to the vibration representative data, and determine the wear evaluation value according to the vibration representative data when surface wear exists.

[0071] The adjustment unit is configured to adjust the first tire life prediction value according to the defect assessment value and the wear assessment value to obtain a final tire life prediction value.

[0072] Specifically, the support device includes a support frame and a drive motor. The support frame is used to fix the tire to be tested to ensure that the tire is in a stable state during the test, while the drive motor is used to simulate the rotation of the tire on the surface of the sliding component to simulate the rotation state of the tire when it is actually working. The function of the sliding component is to provide a surface for the tire to move, and to press the tire against the sliding component through the downforce to ensure that sufficient friction is provided when the tire rotates for simulation testing. The sensor component mainly consists of a vibration sensor and an infrared thermometer. The vibration sensor is installed on the tire to monitor the vibration of the tire in real time during the rotation process. The infrared thermometer is set on the support frame and can collect tire surface temperature data through infrared imaging technology. The temperature and vibration data reflect the health status of the tire under different operating conditions and can promptly detect possible abnormalities or defects on the tire surface. The control module includes an acquisition unit, a processing unit, a judgment unit and an adjustment unit, which are responsible for data acquisition, processing, analysis and life prediction. The acquisition unit collects parameter information of the tire, including specification size and tread wear. At the same time, the acquisition unit determines the downforce when the tire contacts the sliding component according to the specification size to ensure that the load during the test is consistent with the actual use scenario. The acquisition unit collects vibration data within a predetermined time under the condition of stable tire speed, and collects temperature distribution information of the tire through an infrared thermometer after the test. The processing unit performs normal distribution processing on the collected vibration data, and obtains representative vibration values ​​by summarizing and integrating the vibration data. Subsequently, it establishes a temperature distribution map of the tire through temperature data, so as to more intuitively present the temperature state of the tire surface. Finally, the processing unit preliminarily predicts the service life of the tire in combination with the tread wear data, and generates a first life prediction value. The judgment unit analyzes whether there are local defects in the tire, such as temperature abnormality areas, based on the temperature distribution map. If there are defects, the judgment unit calculates the defect assessment value based on the temperature difference of these abnormal areas. At the same time, the judgment unit detects the wear of the tire surface through vibration representative data, and calculates the wear assessment value if the vibration exceeds the threshold. The adjustment unit dynamically adjusts the first life prediction value in combination with the defect assessment value and the wear assessment value, thereby obtaining a more accurate final tire life prediction value. The adjustment process ensures that the prediction result can comprehensively consider the various states and potential problems of the tire, and avoids the prediction deviation caused by a single parameter.

[0073] It is understandable that by combining vibration data and temperature distribution, multi-angle monitoring and evaluation of tire health status is achieved, the accuracy of tire life prediction is improved, and local defects and wear of tires can be discovered at an early stage to prevent potential tire failure risks. In addition, by automatically adjusting the algorithm to integrate defect and wear data, the deviation caused by a single prediction factor in traditional methods is effectively avoided, providing higher protection for vehicle driving safety.

[0074] In some embodiments of the present application, when the acquisition unit determines the downforce when the tested tire contacts the sliding assembly according to the specification size, it includes:

[0075] Specification dimensions include load rating, tire width and tire contact area factor.

[0076] Fd = Pr × K × L × C × Sf;

[0077] Wherein, Fd represents downforce, Pr represents rated load, K represents tire width, L represents tire ground contact length, C represents tire contact area coefficient, Sf represents safety factor, and Sf takes values ​​of [0.7, 1.0].

[0078] Specifically, downforce refers to the vertical force applied when the tire contacts the sliding component, which determines the friction and load between the tire and the sliding surface during the test. The rated load refers to the maximum load that the tire can withstand under standard conditions of use. The tire contact length is the length of the part of the tire in contact with the ground, which can be obtained through simulation experiments. The tire contact area coefficient represents the contact ratio between the tire and the sliding component, which is determined according to the shape and size of the tire contact surface and takes a value of [0.8,1].

[0079] It can be understood that by introducing the tire contact area coefficient and the safety factor, the downforce is effectively adjusted to ensure that the stress state of the tire during the test is closer to the actual driving state, thereby improving the accuracy and safety of the test. At the same time, this embodiment can avoid secondary damage to the tire caused by excessive or insufficient downforce during the test, thereby extending the service life of the tire and improving the reliability of the test.

[0080] In some embodiments of the present application, the processing unit performs normal distribution processing on the vibration data to determine the vibration representative data, including:

[0081] The processing unit integrates the vibration data into a vibration data set, and divides the vibration data set into a plurality of sub-vibration data sets with one rotation of the tested tire as a cycle, and the number of vibration data in the sub-vibration data sets is the same.

[0082] Kernel density estimation is performed on the vibration data at the same position in all sub-vibration data sets to determine the data with the highest probability as the representative vibration data at that position.

[0083] The vibration representative data at different positions are combined to form the vibration representative data.

[0084] When performing kernel density estimation, the Gaussian kernel density function is:

[0085]

[0086] Where n is the total number of sub-vibration data sets, h represents the smoothing bandwidth, and s i represents the i-th data point, and x represents any variable in the overall data.

[0087] In some embodiments of the present application, the smoothing bandwidth is calculated by the following formula:

[0088]

[0089] Where h represents the smoothing bandwidth, σ represents the standard deviation of the vibration data at the same position in all sub-vibration data sets, β1 represents the data skewness, β2 represents the data kurtosis, n represents the total number of sub-vibration data sets, and k represents the adjustment coefficient.

[0090] The data skewness is calculated by the following formula:

[0091]

[0092] The kurtosis of the data is calculated by the following formula:

[0093]

[0094] Where n represents the total number of sub-vibration data sets, s i represents the i-th data point, represents the mean of the vibration data at the same position in all sub-vibration data sets, and σ represents the standard deviation of the vibration data at the same position in all sub-vibration data sets.

[0095] Specifically, when the processing unit performs normal distribution processing on the vibration data, the collected vibration data is first formed into a vibration data set, and the data set is divided into several sub-vibration data sets according to the cycle of one rotation of the tire, and the number of data points in these subsets is the same. Next, through kernel density estimation, the highest probability value of the vibration data at each position is determined as the vibration representative data of the position. Finally, the representative data of different positions are combined to obtain the vibration representative data of the entire tire.

[0096] It can be understood that by dividing the vibration data into multiple periodic subsets and performing kernel density estimation on the data of each subset, the vibration representative data representing different positions on the entire tire surface can be accurately extracted. The use of the Gaussian kernel density function makes the processing of vibration data more flexible, and by calculating the skewness and kurtosis to calibrate the symmetry and sharpness of the data, the vibration characteristics of the tire can be accurately reflected. At the same time, the smoothness of the kernel density estimation is ensured by the reasonable selection of the smoothing bandwidth, thereby avoiding the situation where the data fluctuation is too large or too small. The accuracy and reliability of vibration data processing are improved.

[0097] In some embodiments of the present application, when the processing unit determines the first life prediction value of the tested tire based on the tread wear, it includes: the processing unit compares the tread wear with the set first preset wear and second preset wear, respectively, and determines the first life prediction value of the tested tire based on the comparison result, and the first preset wear is less than the second preset wear.

[0098] Specifically, when the tread wear is less than or equal to the first preset wear, the first life prediction value of the tested tire is determined to be the first preset life prediction value. When the tread wear is greater than the first preset wear and less than or equal to the second preset wear, the first life prediction value of the tested tire is determined to be the second preset life prediction value. When the tread wear is greater than the second preset wear, the first life prediction value of the tested tire is determined to be the third preset life prediction value. The first preset life prediction value is greater than the second preset life prediction value, and the second preset life prediction value is greater than the third preset life prediction value.

[0099] It is understandable that by setting multiple preset wear thresholds, the tire life prediction is accurately divided into multiple intervals. Compared with the traditional life prediction method, the hierarchical prediction method can better reflect the gradual development process of tire wear and can provide targeted life prediction information at different wear stages. The prediction method can more clearly show the remaining service life of the tire and provide more intuitive tire status feedback.

[0100] In some embodiments of the present application, when the judging unit judges whether the tested tire has defects according to the temperature distribution diagram, it includes:

[0101] In the temperature distribution diagram, a central point (x 0 ,y 0 , z 0 ).

[0102] The radius of the sphere is determined with the center point as the center and r as the neighborhood radius.

[0103] The average temperature of the spherical domain is calculated based on the temperature values ​​of each coordinate point within the radius of the spherical domain.

[0104] When the difference between the temperature data of the center point and the average temperature of the spherical area is greater than the temperature difference threshold, it is determined that the temperature of the center point is abnormal.

[0105] The average spherical temperature is calculated by the following formula:

[0106]

[0107] Among them, T avg represents the average temperature of the sphere, r represents the radius of the sphere, (x 0 ,y 0 , z 0) represents the coordinates of the center point, and T(x, y, z) represents the temperature data at point (x, y, z) on the temperature distribution graph.

[0108] In some embodiments of the present application, when a defect exists, the judgment unit determines the defect assessment value according to the temperature distribution diagram, including: the judgment unit collects the number of points where the temperature is abnormal, and determines the defect assessment value according to the number of points, and the defect assessment value is proportional to the number of points.

[0109] Specifically, the infrared thermometer is used to monitor the tire surface in real time to obtain the temperature distribution of each coordinate point. Since local overheating of the tire is often a manifestation of early defects, the temperature distribution map can be used to identify whether the tire has local overheating problems.

[0110] It can be understood that by using the temperature distribution map and spherical analysis method, the detection of local defects of the tire is achieved. Compared with traditional detection methods, the potential local defects of the tire can be accurately identified through the changes in temperature anomalies, especially in the case of overheating of the tire, which can predict possible failures earlier. By quantifying the number of temperature anomaly points and converting them into defect assessment values, more intuitive and actionable tire health status feedback can be provided. The defect assessment value is used to quantify the degree of tire defects, mainly by counting the number of temperature anomaly points. The more temperature anomaly points, the larger the local overheating area of ​​the tire, and the higher the risk of tire defects. The defect assessment value is [0, 1].

[0111] In some embodiments of the present application, a judgment unit judges whether the tested tire has surface wear based on the vibration representative data, and when surface wear exists, determines the wear assessment value based on the vibration representative data, including: the judgment unit compares the vibration data in the vibration representative data with the vibration threshold value respectively, and when there is data in the vibration representative data that is greater than the vibration threshold value, the judgment unit determines that the tested tire has surface wear.

[0112] Specifically, when there is surface wear, the judgment unit determines the vibration difference according to the vibration representative data and the vibration threshold, the vibration difference is the difference between the data greater than the vibration threshold in the vibration representative data and the vibration threshold, and when there are multiple data greater than the vibration threshold in the vibration representative data, the vibration difference is calculated according to the maximum value of the vibration data. The wear assessment value is determined according to the vibration difference, and the wear assessment value is proportional to the vibration difference.

[0113] It is understandable that the vibration threshold is a preset standard value, which is used as a reference standard for the normal vibration state of the tire to distinguish between normal and abnormal vibrations, and can be obtained based on the initial tire rotation. By analyzing the vibration representative data of the tire, it is possible to detect whether there is wear on the tire surface. Compared with traditional naked eye detection or detection methods that rely solely on tread wear depth, vibration analysis has higher sensitivity and real-time performance, and can detect potential wear problems early. In addition, the wear assessment value determined by the vibration difference can intuitively understand the degree of tire wear. The wear assessment value is [0,1].

[0114] In some embodiments of the present application, the adjustment unit adjusts the first life prediction value according to the defect assessment value and the wear assessment value to obtain the final tire life prediction value, including:

[0115]

[0116] Among them, Lz represents the final tire life prediction value, L represents the first life prediction value, Et represents the defect assessment value, Ev represents the wear assessment value, N represents the number of temperature abnormality points, ΔTmax represents the maximum temperature difference in the spherical domain, and ΔVmax represents the vibration difference.

[0117] Specifically, for example: L is 50,000 kilometers, N is 5, ΔTmax=10°C, Et=0.3, ΔVmax is 0.2 mm, Ev=0.4, and the final calculated predicted tire life value Lz is approximately 28,000 kilometers.

[0118] It is understandable that the temperature anomaly point and the maximum temperature difference in the spherical domain reflect the thermal state of the tire, while the vibration difference reflects the mechanical state. Both evaluate the impact on tire life from the perspectives of thermal stress and mechanical wear. The impact of tread wear on tire life is taken into account, and defect assessment values ​​and wear assessment values ​​are introduced to improve the accuracy of tire life prediction. The multi-factor adjustment method can better reflect the complex operating environment of the tire in actual use, detect potential local defects or abnormal wear in a timely manner, avoid the inaccuracy of traditional methods that rely only on a single factor (such as wear), and improve the prediction accuracy of tire service life.

[0119] In the above-mentioned embodiment, by introducing a support device, a sliding assembly, a sensor assembly and a control module, the tire can be fully monitored in real time and data collected, thus overcoming the problem of the single means of traditional tire life prediction. The vibration sensor and the infrared thermometer are used to detect the vibration state and temperature distribution of the tire respectively. Combined with the control module, these data are processed into a normal distribution and the temperature distribution diagram is analyzed to identify local defects and wear conditions on the tire surface. By combining the vibration and temperature anomaly assessment values ​​with the tread wear data, the tire life can be dynamically adjusted and accurately predicted. The accuracy of tire life prediction is improved, which can effectively reduce traffic accidents caused by tire wear.

[0120] In another preferred embodiment based on the above embodiment, refer to Figure 2 As shown, this embodiment provides a method for predicting the service life of a vehicle tire, which is applied to the above-mentioned vehicle tire service life prediction system, including:

[0121] S100: collecting parameter information of the tested tire, the parameter information including specification size and tread wear, determining the downforce when the tested tire contacts the sliding assembly according to the specification size, controlling the rotation of the driving motor, collecting vibration data of the tested tire within a preset time when the rotation speed of the tested tire is stable, stopping the driving motor after the preset time, and collecting temperature data of the tested tire based on an infrared thermometer;

[0122] S200: performing normal distribution processing on the vibration data to determine vibration representative data; establishing a temperature distribution diagram according to the temperature data; and determining a first life prediction value of the tested tire according to tread wear;

[0123] S300: judging whether the tested tire has defects according to the temperature distribution diagram, and determining the defect assessment value according to the temperature distribution diagram when defects exist; judging whether the tested tire has surface wear according to the vibration representative data, and determining the wear assessment value according to the vibration representative data when surface wear exists.

[0124] S400: adjusting the first tire life prediction value according to the defect assessment value and the wear assessment value to obtain a final tire life prediction value.

[0125] It is understandable that by introducing support devices, sliding components, sensor components and control modules, the tires can be fully monitored and data collected in real time, overcoming the problem of the single means of traditional tire life prediction. The vibration sensor and infrared thermometer are used to detect the vibration state and temperature distribution of the tire respectively. Combined with the control module, these data are processed into normal distribution and analyzed into temperature distribution diagrams to identify local defects and wear conditions on the tire surface. By combining the vibration and temperature anomaly assessment values ​​with the tread wear data, the tire life can be dynamically adjusted and accurately predicted. The accuracy of tire life prediction is improved, which can effectively reduce traffic accidents caused by tire wear.

[0126] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0127] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0128] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0129] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0130] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A vehicle tire service life prediction system, characterized in that: include: A support device, a sliding assembly, a sensor assembly and a control module; the support device includes a support frame and a drive motor, the support frame is used to fix the tire under test, the drive motor is used to drive the tire under test to rotate on the upper surface of the sliding assembly, the sensor assembly includes a vibration sensor and an infrared thermometer, the vibration sensor assembly is arranged on the tire under test, and the infrared thermometer is arranged on the support frame to collect an infrared image of the tire under test; The control module includes a collection unit, a processing unit, a judgment unit and an adjustment unit; The acquisition unit is configured to acquire parameter information of the tire under test, wherein the parameter information includes specification size and tread wear, and the downforce of the tire under test when in contact with the sliding assembly is determined according to the specification size. The acquisition unit is also configured to control the rotation of the drive motor. When the rotation speed of the tire under test is stable, the acquisition unit acquires vibration data of the tire under test within a preset time, stops the drive motor after the preset time, and acquires temperature data of the tire under test based on the infrared thermometer. The processing unit is configured to perform normal distribution processing on the vibration data to determine vibration representative data; the processing unit is also configured to establish a temperature distribution map according to the temperature data; the processing unit is also configured to determine a first life prediction value of the tested tire according to the tread wear; The judgment unit is configured to judge whether the tested tire has a defect according to the temperature distribution map, and determine the defect assessment value according to the temperature distribution map when a defect exists; the judgment unit is also configured to judge whether the tested tire has surface wear according to the vibration representative data, and determine the wear assessment value according to the vibration representative data when surface wear exists; The adjustment unit is configured to adjust the first life prediction value according to the defect assessment value and the wear assessment value to obtain a final tire life prediction value.

2. The automobile tire service life prediction system according to claim 1, characterized in that: When the collecting unit determines the downforce when the tested tire contacts the sliding assembly according to the specification size, it includes: The specifications and dimensions include rated load, tire width and tire contact area coefficient; Fd = Pr × K × L × C × Sf; Wherein, Fd represents downforce, Pr represents rated load, K represents tire width, L represents tire ground contact length, C represents tire contact area coefficient, Sf represents safety factor, and Sf takes values ​​of [0.7, 1.0].

3. The automobile tire service life prediction system according to claim 1, characterized in that: The processing unit performs normal distribution processing on the vibration data to determine the vibration representative data, including: The processing unit integrates the vibration data to form a vibration data set, and divides the vibration data set into a plurality of sub-vibration data sets with one rotation of the tested tire as a period, wherein the number of vibration data in the sub-vibration data sets is the same; Perform kernel density estimation on the vibration data at the same position in all sub-vibration data sets, and determine the data with the highest probability as the representative vibration data at that position; Combining vibration representative data at different positions to form the vibration representative data; When performing the kernel density estimation, the Gaussian kernel density function is: Where n is the total number of sub-vibration data sets, h represents the smoothing bandwidth, and s i represents the i-th data point, and x represents any variable in the overall data.

4. The automobile tire service life prediction system according to claim 3, characterized in that: The smoothing bandwidth is calculated by the following formula: Wherein, h represents the smoothing bandwidth, σ represents the standard deviation of the vibration data at the same position in all sub-vibration data sets, β1 represents the data skewness, β2 represents the data kurtosis, n represents the total number of sub-vibration data sets, and k represents the adjustment coefficient; The data skewness is calculated by the following formula: The data kurtosis is calculated by the following formula: Where n represents the total number of sub-vibration data sets, s i represents the i-th data point, represents the mean of the vibration data at the same position in all sub-vibration data sets, and σ represents the standard deviation of the vibration data at the same position in all sub-vibration data sets.

5. The automobile tire service life prediction system according to claim 4, characterized in that: When the processing unit determines the first life prediction value of the tested tire according to the tread wear, it includes: The processing unit compares the tread wear with a first preset wear and a second preset wear respectively, and determines a first life prediction value of the tested tire according to the comparison result, wherein the first preset wear is less than the second preset wear; When the tread wear is less than or equal to the first preset wear, the first life prediction value of the tested tire is determined to be the first preset life prediction value; when the tread wear is greater than the first preset wear and less than or equal to the second preset wear, the first life prediction value of the tested tire is determined to be the second preset life prediction value; when the tread wear is greater than the second preset wear, the first life prediction value of the tested tire is determined to be the third preset life prediction value; the first preset life prediction value is greater than the second preset life prediction value, and the second preset life prediction value is greater than the third preset life prediction value.

6. The automobile tire service life prediction system according to claim 5, characterized in that: When the judging unit judges whether the tested tire has defects according to the temperature distribution diagram, it includes: Arbitrarily determine a center point (x0, y0, z0) in the temperature distribution diagram; Determine the radius of the spherical area by taking the central point as the center of the circle and r as the neighborhood radius; Calculate the average temperature of the spherical domain according to the temperature values ​​of each coordinate point within the radius of the spherical domain; When the difference between the temperature data of the center point and the average temperature of the spherical area is greater than the temperature difference threshold, it is determined that the temperature of the center point is abnormal; The average temperature of the spherical region is calculated by the following formula: Among them, T avg represents the average temperature of the sphere, r represents the radius of the sphere, (x0, y0, z0) represents the coordinates of the center point, and T(x, y, z) represents the temperature data at point (x, y, z) on the temperature distribution graph.

7. The automobile tire service life prediction system according to claim 6, characterized in that: When the determination unit determines the defect assessment value according to the temperature distribution diagram when a defect exists, it includes: The judgment unit collects the number of points where the temperature is abnormal, and determines the defect assessment value according to the number of points, and the defect assessment value is proportional to the number of points.

8. The automobile tire service life prediction system according to claim 7, characterized in that: The judging unit judges whether the tested tire has surface wear according to the vibration representative data, and when surface wear exists, determines the wear assessment value according to the vibration representative data, including: The judging unit compares the vibration data in the vibration representative data with the vibration thresholds respectively, and when the vibration representative data includes data greater than the vibration threshold, the judging unit determines that the tested tire has surface wear; When surface wear exists, the judgment unit determines a vibration difference value according to the vibration representative data and the vibration threshold value, wherein the vibration difference value is a difference between data in the vibration representative data that is greater than the vibration threshold value and the vibration threshold value, and when there are multiple data in the vibration representative data that are greater than the vibration threshold value, the vibration difference value is calculated according to the maximum value of the vibration data; The wear evaluation value is determined according to the vibration difference, and the wear evaluation value is proportional to the vibration difference.

9. The automobile tire service life prediction system according to claim 8, characterized in that: The adjusting unit adjusts the first life prediction value according to the defect assessment value and the wear assessment value to obtain a final tire life prediction value, including: Among them, Lz represents the final tire life prediction value, L represents the first life prediction value, Et represents the defect assessment value, Ev represents the wear assessment value, N represents the number of temperature abnormality points, ΔTmax represents the maximum temperature difference in the spherical domain, and ΔVmax represents the vibration difference.

10. A method for predicting the service life of automobile tires, applied to the automobile tire service life prediction system according to any one of claims 1 to 9, characterized in that: include: Collecting parameter information of the tested tire, the parameter information including specification size and tread wear, determining the downforce of the tested tire when it contacts the sliding assembly according to the specification size, controlling the rotation of the driving motor, collecting vibration data of the tested tire within a preset time when the rotation speed of the tested tire is stable, stopping the driving motor after the preset time, and collecting temperature data of the tested tire based on an infrared thermometer; Performing normal distribution processing on the vibration data to determine vibration representative data; Establishing a temperature distribution diagram according to the temperature data; determining a first life prediction value of the tested tire according to the tread wear; Determine whether the tested tire has defects according to the temperature distribution map, and determine the defect assessment value according to the temperature distribution map when defects exist; determine whether the tested tire has surface wear according to the vibration representative data, and determine the wear assessment value according to the vibration representative data when surface wear exists; The first life prediction value is adjusted according to the defect assessment value and the wear assessment value to obtain a final tire life prediction value.

Images