Tire wear state prediction system, tire wear state prediction program, and tire wear state prediction method

Abstract

The present application has: a sensor unit (SU) provided on the inner side surface or inside of a tire (10) and having a strain sensor (SN) for detecting a strain of the tire; a strain signal acquisition section (201) that acquires a strain signal; an index calculation section (202) that calculates an index of a deformation speed based on a time series waveform of the strain signal; a relationship value calculation section (203) that calculates in advance a relationship value between the index of the deformation speed and a degree of wear of the tire; a wear state estimation section (204) that compares the relationship value with the index of the deformation speed to estimate the degree of wear of the tire; and a relationship value prediction section (205) that predicts a relationship value of another size of tire based on the relationship value between the index of the deformation speed of the tire and the degree of wear of the tire, wherein the relationship value prediction section uses data related to at least the thickness of the tire tread portion, which is saved in advance in a data saving section (206), when predicting the relationship value of the other size of tire.

Description

Technical Field

[0001] This invention relates to a tire wear condition prediction system, a tire wear condition prediction program, and a tire wear condition prediction method. Background Technology

[0002] Previously, a technique was proposed to place an acceleration sensor on a pneumatic tire (hereinafter referred to as a tire) and estimate (predict) tire wear based on the detected acceleration (Patent Document 1).

[0003] Wear estimation techniques that use acceleration sensors, such as the deformation rate index (also known as wear measure) obtained by differentiating the detected radial acceleration and obtaining the peak, estimate the tire groove allowance through regression equations based on the principle that the deformation rate index value increases as wear progresses.

[0004] Existing technical documents

[0005] Patent documents

[0006] Patent Document 1: WO2009 / 008502 Publication Summary of the Invention

[0007] In addition, in wear estimation techniques that use accelerometers, the magnitude of the acceleration signal depends on the tire's rotational speed, which makes it difficult to obtain data in low-speed areas.

[0008] Therefore, a wear estimation technique using strain sensors instead of accelerometers was proposed.

[0009] However, when predicting tire wear, both technologies using acceleration sensors and those using strain sensors share a common problem: "performing experimental parameter learning for each type of tire requires very high costs."

[0010] On the other hand, one advantage of using strain sensors is that there is almost no velocity dependence.

[0011] Furthermore, a disadvantage of using strain sensors is the difficulty in correcting for the influence of tire inner surface thickness. Therefore, it is difficult to directly adapt techniques for generalizing tire size in tire wear prediction using accelerometers to tire wear prediction using strain sensors.

[0012] Therefore, the present invention was made in view of the above-mentioned problems, and its object is to provide a tire wear condition prediction system, tire wear condition prediction program, and tire wear condition prediction method that use strain sensors and can make high-precision predictions of wear condition even for tires without learning data at low cost.

[0013] The essence of a tire wear condition prediction system according to one aspect of the present invention is that it comprises: a sensor unit disposed on the inner surface or inside of a tire, having a strain sensor for detecting the strain of the tire; a strain signal acquisition unit that acquires a strain signal output from the strain sensor; an index calculation unit that calculates an index of deformation rate based on the time-series waveform of the acquired strain signal; a relationship value calculation unit that pre-calculates a relationship value between the deformation rate index and the wear degree of the tire; a wear condition estimation unit that compares the relationship value with the deformation rate index to estimate the wear degree of the tire; and a relationship value prediction unit that predicts relationship values ​​for tires of other sizes based on the relationship value between the deformation rate index and the wear degree of the tire, wherein the relationship value prediction unit uses data pre-stored in a data storage unit that is at least related to the thickness of the tire tread portion when predicting the relationship values ​​for tires of other sizes.

[0014] Based on this structure, high-precision prediction of wear conditions can be made for tires of other sizes at low cost.

[0015] Alternatively, the data relating to the thickness of the tire tread may include data on the radial thickness from the inner surface of the tire to the belt layer disposed within the tire.

[0016] Therefore, even as the strain increases with the increase of the inner surface thickness of the tire, it is possible to predict the wear condition with high accuracy.

[0017] Other embodiments of the present invention involve a tire wear condition prediction program executed by a computer within a tire wear condition prediction system. The tire wear condition prediction program includes the following steps: a strain signal acquisition step, acquiring a strain signal output from a strain sensor disposed on or inside the inner surface of the tire; an index calculation step, calculating an index of deformation rate based on the time-series waveform of the acquired strain signal; a relationship value calculation step, pre-calculating a relationship value between the deformation rate index and the tire wear degree; a wear condition estimation step, comparing the relationship value with the deformation rate index to estimate the tire wear degree; and a relationship value prediction step, predicting relationship values ​​for tires of other sizes based on the relationship value between the deformation rate index and the tire wear degree, wherein, in the relationship value prediction step, data at least related to the thickness of the tire tread is used when predicting relationship values ​​for tires of other sizes.

[0018] Based on this structure, high-precision prediction of wear conditions can be made for tires of other sizes at low cost.

[0019] Alternatively, the data relating to the thickness of the tire tread may include data on the radial thickness from the inner surface of the tire to the belt layer disposed within the tire.

[0020] Therefore, even as the strain increases with the increase of the inner surface thickness of the tire, it is possible to predict the wear condition with high accuracy.

[0021] Other aspects of the present invention involve a tire wear condition prediction method comprising the following processes: a strain signal acquisition process, acquiring a strain signal output from a strain sensor disposed on or inside the inner surface of the tire; an index calculation process, calculating an index of deformation rate based on the time-series waveform of the acquired strain signal; a relationship value calculation process, pre-calculating a relationship value between the index of deformation rate and the wear degree of the tire; a wear condition estimation process, comparing the relationship value with the index of deformation rate to estimate the wear degree of the tire; and a relationship value prediction process, predicting relationship values ​​for tires of other sizes based on the relationship value between the index of deformation rate of the tire and the wear degree of the tire, wherein, in the relationship value prediction process, data at least related to the thickness of the tire tread is used when predicting the relationship values ​​for tires of other sizes.

[0022] Based on this structure, high-precision prediction of wear conditions can be made for tires of other sizes at low cost.

[0023] Alternatively, the data relating to the thickness of the tire tread may include data on the radial thickness from the inner surface of the tire to the belt layer disposed within the tire.

[0024] Therefore, even as the strain increases with the increase of the inner surface thickness of the tire, it is possible to predict the wear condition with high accuracy.

[0025] According to this embodiment, a tire wear condition prediction system, a tire wear condition prediction program, and a tire wear condition prediction method are provided that can use strain sensors and make high-precision predictions of wear conditions even for tires without learning data at low cost. Attached Figure Description

[0026] Figure 1 This is a schematic diagram showing the general structure of the tire wear condition prediction system according to the embodiment.

[0027] Figure 2 This is a function block diagram illustrating the functional structure of the tire wear condition prediction system according to the embodiment.

[0028] Figure 3 This is a flowchart illustrating the processing procedure for predicting tire wear status according to the embodiment.

[0029] Figure 4 It is a graph showing the relationship between tire strain and time.

[0030] Figure 5 It is a graph showing the relationship between the tire's strain rate and time.

[0031] Figure 6 It is a graph showing the relationship between an indicator of tire deformation rate (wear metric) and contact time ratio (CTR).

[0032] Figure 7 It is a chart showing the specifications of tires in multiple sizes.

[0033] Figure 8 This is a schematic diagram (a) showing the effect of the inner surface thickness of the tire (in the case of a thinner surface) on the strain, and a schematic diagram (b) showing the strain distribution.

[0034] Figure 9 This is a schematic diagram (a) showing the effect of the inner surface thickness of the tire (in the case of a thicker tire) on the strain, and a schematic diagram (b) showing the strain distribution.

[0035] Figure 10 It is a graph showing the relationship between tire strain indicators (strain measure) and contact time ratio (CTR). Detailed Implementation

[0036] Reference Figure 1 and Figure 2 The tire wear condition prediction system S1 according to the embodiments of the present invention will be explained.

[0037] Furthermore, in the accompanying drawings below, the same or similar reference numerals are used to label the same or similar parts. However, it should be noted that the drawings are schematic, and the scale of the dimensions, etc., differs from reality.

[0038] Therefore, specific dimensions should be determined by referring to the following instructions. Additionally, the accompanying drawings naturally include the dimensional relationships and proportions between the different parts.

[0039] (Overview of the tire wear condition prediction system)

[0040] Reference Figure 1 The outline structure of the tire wear condition prediction system S1 according to the embodiment is illustrated by the following structural diagram.

[0041] The tire wear condition prediction system S1 consists of a sensor unit SU installed on the side of the pneumatic tire (hereinafter referred to as the tire) 10 and a processing device (such as an ECU) 200 that processes the information obtained from the sensor unit SU via a wireless line N1.

[0042] exist Figure 1 The cross-sectional shape of the tire 10 assembled on the rim 90 is shown in the tire width direction.

[0043] Additionally, the tread portion 20 is the part of the tire 10 (not shown) that comes into contact with the road surface when it rolls on the road. The tread portion 20 has a tread pattern corresponding to the type of vehicle and the required performance.

[0044] Furthermore, a sensor unit SU is provided on the inner surface 10a of the tire 10, to which the tire wear condition prediction system S1 can be applied. This sensor unit SU includes a strain sensor SN for detecting the strain of the tire 10. Additionally, although not directly related to this embodiment, it is also possible that the sensor unit SU can acquire temperature information, etc., in addition to strain.

[0045] exist Figure 1 In the structural example shown, the sensor unit SU is disposed on the inner surface 10a facing the tread 20. More specifically, the sensor unit SU is mounted on the surface of the inner liner (not shown) used to prevent leakage of gases such as air into the internal space of the pneumatic tire 10 assembled on the rim 90.

[0046] The sensor unit SU is preferably located on each of the tires 10 installed on the vehicle. This is because it is desirable to monitor the wear condition of each tire 10, etc., to ensure vehicle safety.

[0047] In addition, the sensor unit SU does not necessarily have to be attached to the inner surface of the tire 10. For example, it can be configured such that part or all of the sensor unit SU is embedded inside the tire 10.

[0048] (Functional structure of a tire wear condition prediction system)

[0049] like Figure 2 As shown in the functional block diagram, the sensor unit SU includes: a strain sensor SN for detecting the strain of the tire 10; a transmitter 150 for sending detection data to the processing device 200; and a battery 151 for supplying power to the strain sensor SN and the transmitter 150.

[0050] On the other hand, the processing device 200, which is composed of an ECU or the like, includes a strain signal acquisition unit 201, which acquires strain signals from the sensor unit SU at fixed detection cycles. Furthermore, communication between the sensor unit SU and the processing device 200 is conducted via a wireless line N1 between the transmitter 150 on the sensor unit SU side and the communication unit 210 on the processing device 200 side.

[0051] In addition, an index calculation unit 202 is provided, which calculates an index (wear measure) of deformation rate based on the time series waveform of the acquired strain signal.

[0052] In addition, a relationship value calculation unit 203 is provided, which pre-calculates the relationship value between the deformation speed index and the wear degree of the tire 10.

[0053] In addition, a wear condition estimation unit 204 is provided, which compares the calculated relationship value with the deformation rate index to estimate the wear degree of the tire 10.

[0054] In addition, a relationship value prediction unit 205 is provided, which predicts the relationship value of other tires with different sizes from the tire 10 based on the relationship value between the index of the deformation speed of the tire 10 and the wear degree of the tire 10.

[0055] Furthermore, it includes a data storage unit 206 composed of non-volatile memory or the like, which pre-stores data related to the thickness of the tire tread for multiple tires of different sizes.

[0056] Furthermore, the relationship value prediction unit 205 uses data related to the thickness of the tire tread that is pre-stored in the data storage unit 206 to predict the relationship values ​​of tires of other sizes.

[0057] Furthermore, the specific methods for predicting the relationship values ​​of tires of other sizes will be described later.

[0058] Based on this structure, in addition to tire 10, high-precision prediction of wear conditions can be achieved at low cost for tires of other sizes. That is, unlike in the past, which required experimental parameter learning for multiple types of tires with different inner surface thicknesses, it is possible to predict the wear conditions of multiple types of tires.

[0059] In addition, data related to the thickness of the tire tread can include data on the radial thickness from the inner surface of the tire 10 to the belt layer disposed within the tire.

[0060] Therefore, even as the strain increases with the increase of the inner surface thickness of the tire, it is possible to predict the wear condition with high accuracy.

[0061] (Regarding tire wear condition prediction processing)

[0062] Reference Figure 3 The flowchart shown illustrates the processing procedure of tire wear condition prediction in the tire wear condition prediction system S1.

[0063] Furthermore, this process can be implemented through the cooperation of the OS (operating system) provided by the processing device 200 and other such devices with the specified application programs. However, some or all of the processes can also be implemented in hardware.

[0064] When the process begins, in step S10, the strain signal obtained by the strain signal acquisition unit 201 is acquired from the strain sensor SN of the sensor unit SU, and the process is transferred to step S11.

[0065] In step S11, the deformation rate index (wear metric) is calculated based on the time series waveform of the acquired strain signal, and the process is transferred to step S12.

[0066] In step S12, the relationship between the deformation rate index (wear metric) and the wear degree of tire 10 is calculated, and the process is transferred to step S13.

[0067] In step S13, the calculated relationship value is compared with the deformation rate index (wear metric) to estimate the wear degree of tire 10, and then the process proceeds to step S14.

[0068] In step S14, the relationship values ​​of tires of other sizes are predicted based on the relationship between the index (wear metric) of the deformation rate of tire 10 and the wear degree of tire 10, and the process ends.

[0069] Here, when predicting the relationship values ​​of tires of other sizes, data related to the thickness of the tire tread is used, which is stored in advance in the data storage unit 206.

[0070] In addition, data related to the thickness of the tire tread can include data on the radial thickness from the inner surface of the tire 10 to the belt layer disposed within the tire 10.

[0071] (Algorithm for predicting tire wear condition)

[0072] Here, an algorithm for predicting tire wear condition as described in this invention is explained.

[0073] In this invention, the basic algorithm for predicting tire wear condition using strain sensors is an algorithm equivalent to that disclosed in the applicant's patent applications (Japanese Patent Application Publication No. 2009-018667, WO2009 / 008502, etc.).

[0074] Furthermore, the algorithm disclosed in the aforementioned patent application pertains to wear prediction using an acceleration sensor installed on the tire. It is a machine learning model that uses the peak value (wear metric) detected after differentiating the radial acceleration and the contact time ratio (CTR) as the primary feature quantities. For more details, please refer to the description in the aforementioned patent application.

[0075] On the other hand, the “strain” used in this invention is defined as one axis in the tire circumferential (tangential) direction.

[0076] Here, in Figure 4 The graph shows an example of circumferential strain. Figure 5 The graph shows an example of its differential waveform.

[0077] also, Figure 4 , Figure 5 The waveform was obtained through FEM (Finite Element Method).

[0078] Furthermore, in the FEM analysis of this embodiment, a tire with a size of "11R22.5 M801" was used as a sample, and the detected "strain" was the strain of the tire inner liner.

[0079] Here, the wear metric (WM) is defined by the following formula.

[0080] WM = DP × ORT × CL

[0081] In addition, DP (Derivative Peak) is the peak value of the differential waveform, or it can be the absolute value of the step-in / step-out side, or the value obtained by averaging them.

[0082] Additionally, ORT (One Rotational Time) indicates the time it takes for the tire to rotate one revolution, and CL (Circumferential Length) indicates the tire's circumference.

[0083] Furthermore, regarding CL, a value representing dimensions such as diameter can be used instead of tire radius, and CL is determined according to design specifications, etc.

[0084] ORT is used to eliminate speed dependence, and CL is used to eliminate tire radius dependence.

[0085] In addition, CTR is obtained by dividing the elapsed time from the step-in peak to the step-out peak of the differential waveform by ORT, and it is a representative indicator of the load.

[0086] exist Figure 6 The graph shows the relationship between CTR and WM (wear metric: an indicator of the rate of deformation) in a brand new tire TR1a and a fully worn tire TR1b.

[0087] Reference Figure 6 The curves show that a brand new tire TR1a and a fully worn tire TR1b can be clearly distinguished.

[0088] In addition, information from TPMS (Tire Pressure Monitoring System) can be added to estimate groove allowance (RTD) using regression equations such as those described below.

[0089] RTD = f(WM, temperature, air pressure, velocity, etc.)

[0090] (On the calibration methods and problems of accelerometers)

[0091] In wear prediction using an accelerometer, the following equation (Equation 1) is used to estimate tire wear.

[0092] [Number 1]

[0093]

[0094] Here, D is the tire diameter, W is the tire width, and H is the section height.

[0095] The calibration based on tire diameter D is for cases where averaging is performed based on the sensor's own size. Therefore, the data used as sample data consists of the coordinates and deformation behavior of the tire's inner surface. Since the physical size of the sensor is not considered, the influence of D can be ignored.

[0096] Moreover, with CTR fixed at 0.04, the correlation coefficient for acceleration can be obtained as R2 = 0.967, and the correlation coefficient for strain can be obtained as R2 = 0.677.

[0097] Furthermore, as sample data, parameters such as diameter and flatness were used to cover a certain range to some extent. Figure 7 The chart shows the data for the six tire types from TR10 to TR15.

[0098] Moreover, judging from the correlation coefficients mentioned above, the correlation coefficients are very high under acceleration conditions, and this correction model can accurately predict wear metrics.

[0099] On the other hand, the correlation coefficient is low under strain conditions, suggesting that the accuracy would deteriorate if the same size correction algorithm as that used for the accelerometer were employed.

[0100] Therefore, a new correction method is needed in predicting tire wear condition using the strain sensor SN described in this embodiment.

[0101] (Regarding correction based on tire inner surface thickness)

[0102] In the case of strain sensor SN, the thickness of the rubber on the inner surface of the tire has a significant impact, therefore calibration is required.

[0103] Here, Figure 8 (a) is a schematic diagram showing the effect of the inner surface thickness of the tire (in the case of a thinner surface) on the strain. Figure 8 (b) is a schematic diagram showing the strain distribution. Figure 9 (a) is a schematic diagram showing the effect of the inner surface thickness of the tire (in the case of a thicker inner surface) on the strain. Figure 9 (b) is a schematic diagram showing the strain distribution.

[0104] First, most of the tire's tension is supported by the belt layer, and the overall mechanical behavior of the tire is largely determined by the forces and deformations generated in the belt layer.

[0105] Here, it is assumed that a certain fixed bending deformation is generated in the belt layer.

[0106] Reference Figure 8 It can be seen that when the inner surface thickness is thin (d) in = small), the compressive strain generated on the surface of the inner lining layer becomes smaller.

[0107] On the other hand, refer to Figure 9 It can be seen that when the inner surface thickness is large (d) in = Large), which suggests that the resulting compressive strain is large.

[0108] Therefore, it can be inferred that the strain increases with the increase of the inner surface thickness.

[0109] Here, the effect of variations in inner surface thickness on wear measurement was investigated using FEM. Specifically, in the fully worn model of the FEM, analysis was performed on three types of tires for the inner liner thickness: tires with the design value (TR20b), tires 1 mm thicker than the design value (TR20c), and tires 2 mm thicker than the design value (TR20d). (Refer to...) Figure 10 ).

[0110] In addition, Figure 10 In the graph, TR20a presents the analytical results of an unused (brand new) tire according to the design values.

[0111] according to Figure 10 As can be seen from the curve, when the thickness of the inner surface increases as theoretically required, the strain is amplified and the wear measurement value increases.

[0112] On the other hand, when using an acceleration sensor, the shape of the profile of the inner surface of the tire (which is roughly the same as the profile of the belt layer) determines the effect. Therefore, it is believed that although the profile change caused by the slight increase in the thickness of the inner surface is affected, no significant difference is produced.

[0113] As can be seen from the above verification, when using strain sensor SN, a correction based on the inner surface thickness of the tire is required.

[0114] (Estimation formula for wear measurement values)

[0115] Based on the research results above, the following equation (Equation 2) is used to estimate the wear metric at full wear.

[0116] [Number 2]

[0117]

[0118] Here, d in The thickness of the inner surface.

[0119] In addition to the correction performed by Equation (Equation 2), correction can also be performed using TPMS data.

[0120] In addition, the function in equation (2) can be a simple linear multiple regression or a nonlinear function.

[0121] (The process of wear prediction)

[0122] The actual process for predicting tire wear can be set up as follows.

[0123] 1) Model learning stage

[0124] First, refer to the above six types (refer to Figure 7 Completely worn tires of varying degrees were used as test tires for experiments, and WM, CTR, D, and d were studied. in Relationships such as...

[0125] 2) Parameter acquisition stage for new tires

[0126] In the absence of training data for tires, during the initial stage of driving when the tires are not yet worn, determine the relationship between WM, CTR, and TPMS data.

[0127] 3) Estimation of trench allowance

[0128] Measure WM, CTR, TPMS data, etc.

[0129] Here, when predicting relationships among other tire sizes, in addition to using d in In addition to the internal structural thickness of the tire, design information such as D: tire diameter, W: tire width, and H: tire section height can also be used.

[0130] Furthermore, wear measurements are calculated based on CTR, TPMS data, and design information data when the product is brand new and when it is fully worn.

[0131] Determine the level of the measured WM relative to the wear measurement at brand new and fully worn conditions, and calculate the groove allowance.

[0132] As described above, according to the tire wear condition prediction system S1 of this embodiment, in the wear prediction using strain sensor SN, it is no longer necessary to actually measure the "relationship between the deformation rate index and the tire wear degree" for tires of multiple specifications (sizes, etc.), and the difference between tire sizes can be predicted accurately at low cost.

[0133] The tire wear estimation system, tire wear estimation program, and tire wear estimation method of the present invention have been described above based on the illustrated embodiments. However, the present invention is not limited thereto, and the structure of each part can be replaced with any structure that has the same function.

[0134] For example, as long as the operating conditions of the power supply (battery) 151 are met, a portion of the processing function of the processing device 200 in this embodiment can also be mounted in the sensor unit SU.

[0135] Explanation of reference numerals in the attached figures

[0136] S1: Tire wear condition prediction system; SU: Sensor unit; SN: Strain sensor; 200: Processing device; 201: Strain signal acquisition unit; 202: Index calculation unit; 203: Relationship value calculation unit; 204: Wear condition estimation unit; 205: Relationship value prediction unit.

Claims

1. A tire wear condition prediction system, comprising: A sensor unit, disposed on the inner surface or inside of the tire, has a strain sensor for detecting the strain of the tire; The strain signal acquisition unit acquires the strain signal output from the strain sensor; The index calculation unit calculates the index of deformation rate based on the time series waveform of the acquired strain signal; The relationship value calculation unit uses a sample tire to pre-calculate the relationship value between the deformation rate index of the sample tire and the wear degree of the sample tire; The wear condition estimation unit compares the pre-calculated relationship value with the deformation rate index calculated by the index calculation unit to estimate the wear degree of the tire; as well as The relationship value prediction unit predicts the relationship values ​​for tires of other sizes based on the relationship between the deformation rate of the tire and the degree of tire wear. When predicting the relationship values ​​of tires of other sizes, the relationship value prediction unit uses data pre-stored in the data storage unit that is at least related to the thickness of the tire tread.

2. The tire wear condition prediction system according to claim 1, wherein, Data relating to the thickness of the tire tread includes data on the radial thickness from the inner surface of the tire to the belt layer disposed within the tire.

3. A tire wear condition prediction program, executed by a computer within a tire wear condition prediction system, the tire wear condition prediction program comprising the following steps: The strain signal acquisition step involves acquiring the strain signal output from a strain sensor located on or inside the inner surface of the tire. The index calculation step involves calculating the deformation rate index based on the time series waveform of the obtained strain signal. The relationship value calculation step involves using a sample tire to pre-calculate the relationship value between the deformation rate index of the sample tire and the wear degree of the sample tire. The wear condition estimation step compares the pre-calculated relationship value with the deformation rate index calculated in the index calculation step to estimate the wear degree of the tire. as well as The relationship value prediction step predicts the relationship values ​​for tires of other sizes based on the relationship between the deformation rate of the tire and the wear degree of the tire. In the relationship value prediction step, when predicting the relationship values ​​of tires of other sizes, data related to at least the thickness of the tire tread is used.

4. The tire wear condition prediction program according to claim 3, wherein, Data relating to the thickness of the tire tread includes data on the radial thickness from the inner surface of the tire to the belt layer disposed within the tire.

5. A method for predicting tire wear condition, comprising the following steps: The strain signal acquisition process involves acquiring strain signals output from strain sensors located on or inside the inner surface of the tire. The index calculation process calculates the deformation rate index based on the time series waveform of the obtained strain signal; The relationship value calculation process uses sample tires to pre-calculate the relationship value between the deformation rate index of the sample tires and the wear degree of the sample tires; The wear condition estimation process compares the pre-calculated relationship value with the deformation rate index calculated during the index calculation process to estimate the wear degree of the tire; as well as The relationship value prediction process predicts the relationship values ​​for tires of other sizes based on the relationship between the deformation rate of the tire and the degree of tire wear. In the process of predicting the relationship value, when predicting the relationship value of tires of other sizes, data that is at least related to the thickness of the tire tread is used.

6. The tire wear condition prediction method according to claim 5, wherein, Data relating to the thickness of the tire tread includes data on the radial thickness from the inner surface of the tire to the belt layer disposed within the tire.

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