JP2022104266A - Wear amount estimation system, arithmetic model creation system, and wear amount estimation method - Google Patents

Abstract

To provide a wear amount estimation system, an arithmetic model creation system, and a wear amount estimation method that can improve accuracy of estimating an amount of wear of a tire.SOLUTION: A wear amount estimation system 100 comprises: a vehicle information acquisition unit 12; a tire severity degree calculation unit 13; and a wear amount calculation unit 14. The vehicle information acquisition unit 12 acquires information including a temperature of a tire 7 installed on a vehicle and a travel distance and an altitude of the vehicle. The tire severity degree calculation unit 13 calculates a degree of severity for wear of the tire on the basis of the temperature of the tire 7 and the altitude of the vehicle acquired by the vehicle information acquisition unit 12. The wear amount calculation unit 14 has an arithmetic model 14a that calculates an amount of wear of the tire on the basis of input information, and inputs the travel distance acquired by the vehicle information acquisition unit 12 and the degree of severity calculated by the tire severity degree calculation unit 13 to the arithmetic model 14a to calculate the amount of wear of the tire.SELECTED DRAWING: Figure 1

Description

The present invention relates to a wear amount estimation system for estimating the wear amount of a tire mounted on a vehicle, an arithmetic model generation system, and a wear amount estimation method.

Generally, tires are worn according to the traveling condition, the traveling distance, and the like. Recently, a device that attaches a sensor for measuring the pressure and temperature of a tire to the tire and displays the measured pressure and temperature has been commercialized.

Patent Document 1 describes a conventional tire wear estimation system. The tire wear estimation system includes at least one sensor attached to the tire to generate a first predictor, at least one of a lookup table and database that stores data about the second predictor, and at least one. It has one of the predictors including the effects of one vehicle and a model that receives the predictors and produces an estimated wear factor for at least one tire.

Japanese Unexamined Patent Publication No. 2018-158722

The tire wear estimation system described in Patent Document 1 estimates the amount of tire wear by using, for example, the wheel position and the drive train as vehicle influences. The present inventor estimates the amount of tire wear because the amount of tire wear varies depending on the temperature of the tire and the amount of altitude change of the vehicle even if the influence of the vehicle described in Patent Document 1 is the same. The present inventor has noticed that there is room for improvement. The present inventor also considered that the change in the depth of each groove of the tire can be estimated as the amount of wear, and the uneven wear of the tire caused by the uphill running and the downhill running based on the amount of altitude change of the vehicle can also be estimated.

The present invention has been made in view of such circumstances, and an object thereof is a wear amount estimation system, an arithmetic model generation system, and a wear amount estimation method capable of improving the estimation accuracy of the tire wear amount. Is to provide.

The wear amount estimation system of an aspect of the present invention includes a vehicle information acquisition unit that acquires information including the temperature of a tire mounted on the vehicle, and the mileage and altitude of the vehicle, and a tire acquired by the vehicle information acquisition unit. It has a tire severity calculation unit that calculates the severity of tire wear based on the temperature and altitude of the vehicle, and a calculation model that calculates the amount of tire wear based on the input information, and is acquired by the vehicle information acquisition unit. It is provided with a wear amount calculation unit for calculating the tire wear amount by inputting the mileage and the severity calculated by the tire severity calculation unit into the calculation model.

Another aspect of the present invention is a computational model generation system. The arithmetic model generation system has a vehicle information acquisition unit that acquires information including the temperature of the tire mounted on the vehicle, the mileage and altitude of the vehicle, and the tire temperature and the altitude of the vehicle acquired by the vehicle information acquisition unit. It has a tire severity calculation unit that calculates the severity of tire wear based on it, and a calculation model that calculates the amount of tire wear based on the input information, and the mileage acquired by the vehicle information acquisition unit and the tire. The harshness calculated by the harshness calculation unit is input to the calculation model to calculate the tire wear amount, and the wear amount measured by the tire and the wear amount calculated by the wear amount calculation unit. It is provided with a learning processing unit for learning the arithmetic model by comparing with.

Another aspect of the present invention is a wear amount estimation method. The wear amount estimation method includes a vehicle information acquisition step for acquiring information including the temperature of the tire mounted on the vehicle, the mileage and altitude of the vehicle, and the tire temperature and the vehicle altitude acquired by the vehicle information acquisition step. The mileage acquired by the vehicle information acquisition step and the tire harshness are added to the tire harshness calculation step for calculating the harshness for tire wear based on the calculation model for calculating the tire wear amount based on the input information. It is provided with a wear amount calculation step for calculating the tire wear amount by inputting the severity calculated by the calculation step.

According to the present invention, the accuracy of estimating the amount of tire wear can be improved.

It is a block diagram which shows the functional structure of the wear amount estimation system which concerns on embodiment. It is a block diagram which shows the functional structure of an in-vehicle measuring apparatus. It is a schematic diagram for demonstrating the calculation of the tire harshness. It is a schematic diagram for demonstrating the wear amount estimation and learning of the arithmetic model. It is a block diagram which shows the functional structure of the arithmetic model generation system. It is a flowchart which shows the procedure of the calculation model generation by the calculation model generation system. It is a chart which shows the result of the wear amount estimation by Example 1. FIG. It is a chart which shows the result of the wear amount estimation by Example 2.

Hereinafter, the present invention will be described with reference to FIGS. 1 to 8 based on a preferred embodiment. The same or equivalent components and members shown in the drawings shall be designated by the same reference numerals, and duplicate description thereof will be omitted as appropriate. Further, the dimensions of the members in each drawing are shown in an appropriately enlarged or reduced size for easy understanding. In addition, some of the members that are not important for explaining the embodiment in each drawing are omitted and displayed.

(Embodiment)
FIG. 1 is a block diagram showing a functional configuration of the wear amount estimation system 100 according to the embodiment. The wear amount estimation system 100 includes an in-vehicle measuring device 70 mounted on the vehicle, a weather information server device 80, and a wear amount estimation device 10 for estimating the wear amount of each tire 7 mounted on the vehicle.

The wear amount estimation device 10 obtains vehicle measurement information such as vehicle speed and position information from an in-vehicle measuring device 70 mounted on the vehicle via a communication network 9 such as the Internet, and tire measurement information measured by the tire 7. get. Further, the wear amount estimation device 10 acquires weather information from the weather information server device 80. The wear amount estimation device 10 estimates the wear amount of each tire 7 by performing a calculation by a learning type calculation model based on the acquired information.

FIG. 2 is a block diagram showing a functional configuration of the in-vehicle measuring device 70. The in-vehicle measuring device 70 includes a vehicle measuring unit 71, a tire measuring unit 72, an information acquisition unit 73, and a communication unit 74. Each part of the in-vehicle measuring device 70 can be realized by an electronic element such as a computer CPU or a mechanical component in terms of hardware, and can be realized by a computer program or the like in terms of software. It depicts a functional block realized by. Therefore, it is understood by those skilled in the art that these functional blocks can be realized in various forms by combining hardware and software.

The vehicle measurement unit 71 has a speed meter 71a and a GPS receiver 71b mounted on the vehicle. The speed meter 71a measures the speed of the vehicle. The GPS receiver 71b measures the current position information (latitude, longitude and altitude) of the vehicle. Further, the vehicle measuring unit 71 may have, for example, an acceleration sensor and may measure the acceleration of the vehicle in the three axial directions.

The tire measuring unit 72 has a temperature sensor 72a and a pressure sensor 72b. The temperature sensor 72a and the pressure sensor 72b are arranged on the air valve or the like of the tire 7 mounted on the vehicle, or are firmly wound and fixed to the wheel by a belt or the like, and measure the temperature and the air pressure of the tire 7, respectively. .. The temperature sensor 72a may be arranged on the inner liner or the like of the tire 7.

The information acquisition unit 73 acquires vehicle measurement information (speed, position information, acceleration, etc.) measured by the vehicle measurement unit 71 and tire measurement information (tire temperature, air pressure, etc.) measured by the tire measurement unit 72. The information acquisition unit 73 associates the measured time information or the acquired time information with each measurement data included in the vehicle measurement information and the tire measurement information. The information acquisition unit 73 transmits the vehicle measurement information and the tire measurement information from the communication unit 74 to the wear amount estimation device 10 together with the time information associated with each measurement data.

When the vehicle is equipped with a device such as a digital tachometer, the information acquisition unit 73 may acquire the speed, acceleration, position information, and the like of the vehicle collected by the device. The communication unit 74 communicates with the communication network 9 by wireless communication such as WiFi (registered trademark), and the vehicle measurement information, the tire measurement information, and the time information acquired by the information acquisition unit 73 are subjected to the wear amount via the communication network 9. It is transmitted to the estimation device 10.

Returning to FIG. 1, the weather information server device 80 provides weather information in each place. The meteorological information provided by the meteorological information server device 80 is information including the amount of precipitation, the amount of snowfall, the amount of snowfall, the temperature, the sunshine duration, and the like in each place. The wear amount estimation device 10 acquires weather information at a place where the vehicle is traveling from the weather information server device 80.

The wear amount estimation device 10 includes a communication unit 11, a vehicle information acquisition unit 12, a tire severity calculation unit 13, a wear amount calculation unit 14, and a storage unit 15. Each part of the wear amount estimation device 10 can be realized by an electronic element such as a computer CPU or a mechanical component in terms of hardware, and can be realized by a computer program or the like in terms of software. It depicts a functional block realized by cooperation. Therefore, it is understood by those skilled in the art that these functional blocks can be realized in various forms by combining hardware and software.

The communication unit 11 communicates with the communication network 9 by wireless or wired communication, and communicates with the communication unit 74 of the in-vehicle measuring device 70. Further, the communication unit 11 communicates with the weather information server device 80 via the communication network 9.

The vehicle information acquisition unit 12 acquires vehicle measurement information (speed, position information, acceleration, etc.) and tire measurement information (tire temperature, air pressure, etc.) transmitted from the vehicle-mounted measuring device 70 mounted on the vehicle. The vehicle information acquisition unit 12 calculates and acquires the mileage of the vehicle based on the vehicle measurement information.

The vehicle information acquisition unit 12 can calculate and acquire the mileage based on the position information of the vehicle measurement information. Further, the mileage of the vehicle may be calculated based on the speed data in the vehicle measurement information and the time data associated with the data. That is, the mileage of the vehicle can be calculated by multiplying the speed data arranged in chronological order by the time difference until the next time point.

If the information on the mileage of the vehicle is provided from the vehicle or an external device for vehicle management, the vehicle information acquisition unit 12 does not need to calculate the mileage by itself, and the information on the mileage from the vehicle or the external device. May be obtained.

The vehicle information acquisition unit 12 outputs the acquired mileage to the wear amount calculation unit 14. The vehicle information acquisition unit 12 outputs the acquired tire measurement information (tire temperature, air pressure, etc.) to the wear amount calculation unit 14. The vehicle information acquisition unit 12 outputs the position information in the vehicle measurement information and the temperature of the tire 7 in the tire measurement information to the tire severity calculation unit 13.

When the vehicle information acquisition unit 12 estimates the tire wear amount based on the calculation model using the vehicle acceleration as an input element in the wear amount calculation unit 14, the vehicle information acquisition unit 12 outputs the acceleration data in the vehicle measurement information to the wear amount calculation unit 14. ..

Further, the vehicle information acquisition unit 12 acquires data used for estimating the wear amount of the tire 7 from the vehicle specification data 15a and the tire specification data 15b from the storage unit 15 and outputs the data to the wear amount calculation unit 14. The storage unit 15 is a storage device composed of, for example, an SSD (Solid State Drive), a hard disk, a CD-ROM, a DVD, etc., and stores data provided in advance regarding specifications of various vehicles and tires 7. ..

The vehicle specification data 15a includes, for example, information on vehicle performance such as manufacturer, vehicle name, vehicle model, vehicle body weight, drive train, total length, vehicle width, vehicle height, and maximum load capacity. In addition, the tire specification data 15b includes, for example, manufacturer, product name, tire size, tire width, flatness, wear resistance, tire strength, static rigidity, dynamic rigidity, tire outer diameter, road index, and date of manufacture. Information on the performance of the tire 7, such as, is included.

The tire harshness calculation unit 13 calculates the harshness against tire wear (hereinafter referred to as tire harshness) based on the temperature of the tire and the altitude of the vehicle. The tire harshness calculation unit 13 calculates the amount of altitude change from the altitude included in the position information of the vehicle.

FIG. 3 is a schematic diagram for explaining the calculation of the tire harshness. In FIG. 3, the horizontal axis represents the tire temperature and the vertical axis represents the altitude change amount, and the tire temperature measured over a predetermined measurement period (for example, January) and the calculated altitude change amount are plotted. .. The amount of altitude change represents a state in which the plus side is traveling uphill and a state in which the minus side is traveling downhill. Further, the tire temperature and the calculated altitude change amount are, for example, data every 60 seconds.

The tire harshness calculation unit 13 divides the temperature on the horizontal axis into three and the altitude change on the vertical axis into three, and divides the tire into nine sections SC1 to SC9 in total. The tire harshness calculation unit 13 calculates the frequency data in each section as the tire harshness. For example, assuming that the total number of plots is 10,000 and there are 50 data in the region of SC1, the tire severity calculation unit 13 calculates the frequency in SC1 as 0.005 (= 50/10000). The number of divisions in each axis is not limited to 3, and may be 2 or more.

In FIG. 3, in the sections of SC1, SC4, and SC7, the temperature of the tire 7 is low, and the tire 7 does not generate heat due to slippage or friction, so that the amount of wear is considered to be small. In the sections of SC3, SC6 and SC9, the temperature of the tire 7 is high, and the tire 7 generates heat due to slippage and friction, so that it is considered that the amount of wear is large.

In the sections of SC7, SC8 and SC9, the amount of altitude change is positive, and it is considered that the tire 7 slips and the amount of wear increases because the vehicle is traveling uphill. In the sections of SC1, SC2 and SC3, the amount of altitude change is negative, and it is considered that the tire 7 slips and the amount of wear increases because the vehicle is traveling downhill.

It is believed that the higher the frequency in the sections of SC3 and SC9, the more the amount of wear tends to increase. It is considered that the sections of SC4, SC5 and SC6 are traveling on a flat road, and the amount of wear is considered to be less than that of an uphill or a downhill.

In the sections of SC7 and SC8, and SC1 and SC2, the vehicle is running on a slope, but it is considered that there is no continuous running so that the temperature of the tire 7 rises.

The distribution pattern of the temperature-altitude change amount that affects the wear amount of the tire 7 is considered to be various patterns depending on the area where the vehicle is traveling and the time zone, but as big data, the distribution pattern and the wear amount in each section are considered. By looking at the correlation between the two, the effect on the amount of wear of the tire 7 can be inferred.

The wear amount calculation unit 14 has a calculation model 14a and estimates the wear amount of the tire 7. The calculation model 14a is a learning model that calculates the amount of wear of the tire 7 based on the input information. FIG. 4 is a schematic diagram for explaining wear amount estimation and learning of the calculation model 14a. The input data to the calculation model 14a is generally classified into each system of vehicle measurement information, tire measurement information, tire severity information, and other information.

Input data related to vehicle measurement information includes vehicle acceleration and mileage. The mileage is acquired by the vehicle information acquisition unit 12 as described above. The input data related to the tire measurement information includes the temperature and air pressure of the tire 7. The acceleration of the vehicle shall be used as input data to the calculation model as appropriate.

The input data related to the tire severity information is the tire severity calculated by the tire severity calculation unit 13. The tire harshness is calculated based on the temperature of the tire 7 and the amount of change in altitude as described above.

The input data based on other information includes the road surface condition estimated based on the weather information, the maximum load capacity of the vehicle included in the vehicle specification data 15a, the wear resistance performance of the tire 7 included in the tire specification data 15b, and the like. For the wear resistance performance of the tire 7, for example, a tire wear index value or the like, which is an index of the wear resistance performance of various tread formulations, is used with the standard formulation as 100 based on the Rambone wear test.

As the calculation model 14a, a learning type model such as a neural network is used. The calculation model 14a is constructed by using a method such as DNN (Deep Neural Network) or a decision tree. Further, the arithmetic model 14a may be, for example, a multiple linear regression model for input information, and may be model-generated by learning.

FIG. 5 is a block diagram showing a functional configuration of the arithmetic model generation system 110. In addition to the configuration of the wear amount estimation system 100, the calculation model generation system 110 includes a tire wear amount measurement device 60 and a calculation model generation device 20 having a learning processing unit 21.

The tire wear amount measuring device 60 directly measures the depth of the groove provided in the tread of the tire 7 and acquires the wear amount of the tire 7. The operator may measure the depth of each groove by a measuring instrument, a camera, visual inspection, or the like, and the tire wear amount measuring device 60 may store the measurement data input by the operator. Further, the tire wear amount measuring device 60 may be a dedicated device that measures the depth of the groove by a mechanical or optical method and stores the wear amount.

Specifically, the tire wear amount measuring device 60 measures, for example, when there are four tire grooves, at four locations in the width direction, and further, in the circumferential direction of the same groove, for example, at intervals of 120 °, 3 Measure the location. As a result, uneven wear data in the width direction or the circumferential direction of the tire is also stored in the tire wear amount measuring device 60. Since the diameter of the tire wear amount measuring device 60 changes depending on the tire wear, the groove depth may be indirectly measured by calculation from the information on the mileage and the tire rotation speed / speed. In addition, the one that directly measures the groove depth may be used in combination with the one that is predicted by calculation from the mileage and the rotation speed / speed of the tire.

The arithmetic model generation device 20 has a learning processing unit 21 in addition to each configuration of the wear amount estimation device 10. The portion corresponding to each configuration of the wear amount estimation device 10 in the calculation model generation device 20 has the same function as that of the wear amount estimation device 10, but the calculation model 14a is before or during learning.

The learning processing unit 21 acquires the wear amount of the tire 7 from the tire wear amount measuring device 60 via the communication unit 11. With reference to FIG. 4, in the learning process of the calculation model 14a, the wear amount of the tire 7 as output data is estimated by the calculation model 14a based on the input information and compared with the teacher data. As the teacher data, the wear amount of the tire 7 measured by the tire wear amount measuring device 60 is used.

The learning processing unit 21 compares the wear amount of the tire 7 estimated by the calculation model 14a with the teacher data, newly sets various coefficients in the calculation process such as weighting in the calculation model 14a, and repeats the model update. Perform learning with. The wear amount estimation system 100 estimates the wear amount of the tire 7 using the calculation model 14a learned by the calculation model generation system 110. The learning processing unit 21 can use a known learning method such as gradient boosting. Further, for the verification of the calculation model 14a, a known verification method such as random data sampling or cross-validation can be used.

Next, the operation of the wear amount estimation system 100 and the arithmetic model generation system 110 will be described. FIG. 6 is a flowchart showing a procedure for generating an arithmetic model by the arithmetic model generation system 110. The vehicle information acquisition unit 12 starts acquiring vehicle measurement information and tire measurement information (S1). Further, in step S1, the vehicle information acquisition unit 12 of the calculation model generation device 20 reads out the maximum load capacity of the vehicle and the wear resistance performance of the tire from the storage unit 15 as other information. The vehicle information acquisition unit 12 starts calculating the mileage (S2).

The tire harshness calculation unit 13 calculates the tire harshness based on the temperature of the tire 7 and the amount of change in altitude (S3). As described above, the tire severity is calculated as frequency data in a plurality of sections in which the range of temperature and altitude change is divided.

The wear amount calculation unit 14 acquires input data from the vehicle information acquisition unit 12 and the tire severity calculation unit 13, and calculates and estimates the wear amount of the tire 7 by the calculation model 14a (S4). When the road surface condition or the like is used as input data for the calculation model 14a, a processing unit (not shown) for estimating the road surface condition is provided, and the estimated road surface condition is input from the processing unit to the wear amount calculation unit 14.

The learning processing unit 21 compares the wear amount of the tire 7 calculated by the calculation model 14a with the wear amount of the tire 7 as the teacher data measured by the tire wear amount measuring device 60 (S5). The learning processing unit 21 updates the calculation model 14a based on the comparison result in step S7 (S7), and ends the processing. By repeating these processes, the arithmetic model generation device 20 updates the arithmetic model 14a and improves the accuracy of estimating the tire wear amount.

The wear amount estimation system 100 estimates the wear amount of the tire 7 by using the learned calculation model 14a generated by the calculation model generation device 20. The wear amount estimation system 100 estimates the wear amount of the tire 7 by executing the processes from step S1 to step S4 in the flowchart shown in FIG.

The wear amount estimation system 100 can improve the accuracy of the wear amount estimation of the tire 7 by using the mileage and the tire harshness based on the tire temperature and the altitude change amount as the input data of the calculation model 14a. Similarly, the arithmetic model generation system 110 uses the mileage, the tire harshness based on the tire temperature and the amount of altitude change as the input data of the arithmetic model 14a, and thereby accurately estimates the wear amount of the tire 7 in the arithmetic model 14a. Can be generated.

When the mileage of the vehicle is measured every moment and provided by the in-vehicle device or the like, the wear amount calculation unit 14 may use the provided mileage instead of the calculation by the vehicle information acquisition unit 12. .. Further, the wear amount estimation system 100 can also construct an arithmetic model 14a that outputs the uneven wear amount of the tire 7 and estimate the uneven wear amount. In this case, the wear amount estimation system 100 estimates uneven wear of the tire 7 caused by running with a large change in acceleration of the vehicle and a lot of acceleration / deceleration (running with frequent sudden braking and sudden start). Further, the calculation model generation system 110 can generate a calculation model 14a for estimating the amount of uneven wear by learning the calculation model 14a using the amount of wear measured in each groove of the tire as teacher data for uneven wear. ..

The tire severity calculation unit 13 of the wear amount estimation system 100 divides the range of the temperature and altitude change of the tire 7 into a plurality of sections SC1 to SC9, and calculates the frequency data in each section as the tire severity. .. The tire harshness calculation unit 13 can calculate the tire harshness in consideration of both the temperature and the altitude change by using the frequency data in each section.

In the wear amount estimation system 100 and the arithmetic model generation system 110, the wear amount calculation unit 14 uses the data of the sections SC3, SC6 and SC9 in the high temperature region as the tire severity among the frequency data in the plurality of sections SC1 to SC9. May be good. The wear amount calculation unit 14 can estimate the tire wear amount by using the data of the sections SC3, SC6 and SC9 in the high temperature region where the influence on the tire wear is large.

Further, the wear amount calculation unit 14 includes data in sections SC7, SC8 and SC9 in which the amount of change in altitude indicates uphill running, and sections SC1, SC2 and SC3 in which the amount of altitude change indicates downhill running, which has a large effect on tire wear among the frequency data. Can be used to estimate the amount of tire wear.

Further, the wear amount calculation unit 14 obtains the data in the section SC9 and the section SC3 in which the temperature is in the high temperature range and the altitude change amount indicates the uphill running and the downhill running, which has a large influence on the tire wear among the frequency data. It can be used to estimate the amount of tire wear.

The wear amount calculation unit 14 can reduce the calculation amount by using the data of some of the plurality of sections SC1 to SC9 that have a large influence on the tire wear.

(Example 1)
In the first embodiment, the calculation model 14a was generated based on the data measured in the actual vehicle, the wear amount of the tire 7 was estimated, and the estimation accuracy was verified. The vehicles used in Example 1 were 17 truck vehicles, and the amount of tire wear was measured for each month acquired over 3 months and used as teacher data. The computational model 14a was constructed by regression analysis and decision tree analysis.

FIG. 7 is a chart showing the results of wear amount estimation according to the first embodiment. As shown in FIG. 7, the amount of wear is estimated in five cases. In case C1, the wear amount calculation unit 14 estimates the wear amount of the tire 7 based on the mileage calculated based on the position information by the GPS receiver. In case C2, the wear amount calculation unit 14 uses the data in the section SC9 in which the temperature is in the high temperature range and the altitude change amount indicates the uphill running and the section SC3 in which the downhill running is shown, in addition to the mileage. The amount of wear is estimated.

In case C3, the wear amount calculation unit 14 estimates the tire wear amount using the data of the sections SC3, SC6 and SC9 in the high temperature region in addition to the mileage. In the case C4, the wear amount calculation unit 14 uses the data in the sections SC7, SC8 and SC9 in which the altitude change amount indicates the uphill running, and the sections SC1, SC2 and SC3 in which the altitude change amount indicates the downhill running, in addition to the mileage. The amount of tire wear is estimated. In case C5, the wear amount calculation unit 14 estimates the tire wear amount using the data of all the sections SC1 to SC9 in addition to the mileage.

In each case, the RMSE (root mean square error) obtained by plotting the measured amount of wear and the amount of wear predicted by the arithmetic model 14a was better in the decision tree analysis than in the regression analysis. Further, for the case C1, in addition to the mileage in the case of the case C2, the data in the section SC9 where the temperature is in the high temperature range and the altitude change amount indicates the uphill running and the section SC3 showing the downhill running are used. As a result, the estimation accuracy was improved. In addition, the estimation accuracy was improved in cases C3, C4 and C5.

(Example 2)
In Example 2, among the data acquired in Example 1, the data for 12 truck vehicles was used as learning data, and the data for 5 vehicles was used as verification data to estimate the amount of wear of the tire 7. FIG. 8 is a chart showing the results of wear amount estimation according to the second embodiment. Each case shown in FIG. 8 is the same as each case shown in FIG. 7.

In Example 2, since the learning data is smaller than that in Example 1, the RMSE by the decision tree analysis is larger than that in Example 1, and the accuracy of estimating the amount of wear is slightly deteriorated, but the number of vehicles to be learned is increased. Then, it can be seen that the accuracy of estimating the wear amount of the tire 7 is improved.

Both regression analysis and decision tree analysis show section SC9 and downhill running, where the temperature is in the high temperature range and the amount of altitude change indicates uphill running, in addition to the mileage in case C2, for case C1. The results obtained that the estimation accuracy is improved by using the data in section SC3. In addition, the estimation accuracy was improved in cases C3, C4 and C5.

Next, the features of the wear amount estimation system 100 and the arithmetic model generation system 110 according to each embodiment will be described.
The wear amount estimation system 100 includes a vehicle information acquisition unit 12, a tire severity calculation unit 13, and a wear amount calculation unit 14. The vehicle information acquisition unit 12 acquires information including the temperature of the tire 7 mounted on the vehicle and the mileage and altitude of the vehicle. The tire harshness calculation unit 13 calculates the harshness against tire wear based on the temperature of the tire 7 and the altitude of the vehicle acquired by the vehicle information acquisition unit 12. The wear amount calculation unit 14 has a calculation model 14a for calculating the tire wear amount based on the input information, the mileage acquired by the vehicle information acquisition unit 12, and the harshness calculated by the tire harshness calculation unit 13. The degree is input to the calculation model 14a to calculate the amount of tire wear. Thereby, the wear amount estimation system 100 can improve the accuracy of the wear amount estimation by using the severity of the tire wear based on the temperature of the tire 7 and the altitude of the vehicle.

The tire harshness calculation unit 13 divides the range of the temperature and the amount of altitude change into a plurality of sections, and calculates the frequency data in each section as the harshness. As a result, the wear amount estimation system 100 can calculate the tire harshness in consideration of both the temperature and the altitude change.

The wear amount calculation unit 14 inputs the data in the section where the temperature is high in the frequency data into the calculation model 14a. Thereby, the wear amount estimation system 100 can estimate the tire wear amount by using the data of the sections SC3, SC6 and SC9 in the high temperature region having a large influence on the tire wear.

The wear amount calculation unit 14 inputs data in the frequency data, the section in which the altitude change amount indicates uphill travel and the section in which the altitude change amount indicates downhill travel, into the calculation model 14a. Thereby, the wear amount estimation system 100 can estimate the tire wear amount by using the frequency data in both the uphill running and the downhill running.

The wear amount calculation unit 14 inputs data in the frequency data in the section where the temperature is in the high temperature range and the altitude change amount indicates the uphill running and the downhill running section to the calculation model 14a. As a result, the wear amount estimation system 100 can estimate the tire wear amount by using the frequency data in both the uphill running and the downhill running when the temperature is in the high temperature range.

The calculation model generation system 110 includes a vehicle information acquisition unit 12, a tire severity calculation unit 13, a wear amount calculation unit 14, and a learning processing unit 21. The vehicle information acquisition unit 12 acquires information including the temperature of the tire 7 mounted on the vehicle and the mileage and altitude of the vehicle. The tire harshness calculation unit 13 calculates the harshness against tire wear based on the temperature of the tire 7 and the altitude of the vehicle acquired by the vehicle information acquisition unit 12. The wear amount calculation unit 14 has a calculation model 14a for calculating the wear amount of the tire 7 based on the input information, and is calculated by the mileage acquired by the vehicle information acquisition unit 12 and the tire severity calculation unit 13. The degree of harshness is input to the calculation model 14a to calculate the amount of tire wear. The learning processing unit 21 learns the calculation model 14a by comparing the wear amount measured by the tire 7 with the wear amount calculated by the wear amount calculation unit 14. As a result, the arithmetic model generation system 110 can generate the arithmetic model 14a that accurately estimates the amount of wear of the tire 7.

The wear amount estimation method includes a vehicle information acquisition step, a tire severity calculation step, and a wear amount calculation step. The vehicle information acquisition step acquires information including the temperature of the tire 7 mounted on the vehicle and the mileage and altitude of the vehicle. The tire severity calculation step calculates the severity of tire wear based on the temperature of the tire 7 and the altitude of the vehicle acquired in the vehicle information acquisition step. In the wear amount calculation step, the mileage acquired by the vehicle information acquisition step and the severity calculated by the tire severity calculation step are input to the calculation model 14a that calculates the wear amount of the tire 7 based on the input information. To calculate the amount of tire wear. According to this method, the accuracy of the wear amount estimation can be improved by using the severity of the tire wear based on the temperature of the tire 7 and the altitude of the vehicle.

The above description has been made based on the embodiment of the present invention. It will be appreciated by those skilled in the art that these embodiments are exemplary and that various modifications and modifications are possible within the claims of the invention and that such modifications and modifications are also within the claims of the present invention. It is about to be done. Therefore, the descriptions and drawings herein should be treated as exemplary rather than limiting.

7 Tires, 12 Vehicle Information Acquisition Department, 13 Tire Severity Calculation Department,
14 Wear amount calculation unit, 14a calculation model, 21 Learning processing unit,
100 wear estimation system, 110 computational model generation system.

Claims (7)

A vehicle information acquisition unit that acquires information including the temperature of tires mounted on the vehicle, as well as the mileage and altitude of the vehicle.
A tire severity calculation unit that calculates the severity of tire wear based on the tire temperature and the altitude of the vehicle acquired by the vehicle information acquisition unit.
It has a calculation model that calculates the amount of tire wear based on the input information, and inputs the mileage acquired by the vehicle information acquisition unit and the severity calculated by the tire severity calculation unit into the calculation model. A wear amount calculation unit that calculates the wear amount of a tire, and
A wear amount estimation system characterized by being equipped with. The wear according to claim 1, wherein the tire severity calculation unit divides the range of the temperature and the amount of altitude change into a plurality of sections, and calculates the frequency data in each section as the severity. Quantity estimation system. The wear amount estimation system according to claim 2, wherein the wear amount calculation unit inputs data in a section of the high temperature region of the frequency data into the calculation model. The second aspect of claim 2, wherein the wear amount calculation unit inputs data in a section in which the altitude change amount indicates uphill travel and a section in which the altitude change amount indicates downhill travel among the frequency data to the calculation model. Wear estimation system. The wear amount calculation unit is characterized in that, among the frequency data, the data in the section where the temperature is in the high temperature range and the altitude change amount indicates the uphill running and the downhill running section are input to the calculation model. The wear amount estimation system according to claim 2. A vehicle information acquisition unit that acquires information including the temperature of tires mounted on the vehicle, as well as the mileage and altitude of the vehicle.
A tire severity calculation unit that calculates the severity of tire wear based on the tire temperature and the altitude of the vehicle acquired by the vehicle information acquisition unit.
It has a calculation model that calculates the amount of tire wear based on the input information, and inputs the mileage acquired by the vehicle information acquisition unit and the severity calculated by the tire severity calculation unit into the calculation model. A wear amount calculation unit that calculates the wear amount of a tire, and
A learning processing unit that learns the calculation model by comparing the wear amount measured by the tire with the wear amount calculated by the wear amount calculation unit.
A computational model generation system characterized by being equipped with. A vehicle information acquisition step that acquires information including the temperature of tires mounted on the vehicle, as well as the mileage and altitude of the vehicle.
A tire harshness calculation step for calculating the harshness against tire wear based on the tire temperature and the vehicle altitude acquired in the vehicle information acquisition step, and
The tire wear amount is calculated by inputting the mileage acquired by the vehicle information acquisition step and the severity calculated by the tire severity calculation step into the calculation model that calculates the tire wear amount based on the input information. Wear amount calculation step to be performed and
A wear amount estimation method comprising.

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