Tread wear prediction from sections of tire life

Abstract

The invention relates to tread wear prediction from sections of tire life. Various methods for estimating a tread wear condition of a tire are disclosed. Section analysis data for a given section and a given tire may be obtained. The segment analysis data may include tire data, vehicle data, or manual inspection data. A start tread depth corresponding to a tread depth of the tire at the start point of the section may be determined. A segment distance corresponding to a distance by which the tire travels to a segment end point is determined. The starting tread depth and the segment distance are applied as inputs to the trained tread wear condition model. A tread wear condition is predicted based at least in part on an output of the tread wear condition model.

Description

Technical Field

[0001] The present application relates to tread wear prediction according to segments of tire life. Background Art

[0002] Tire wear refers to the loss of material from the tread of the tire, as indicated by the depth of the tire tread. It may be useful to measure or predict the wear state of a tire. For example, information about the wear state of a tire may be useful in predicting tire performance during vehicle braking and / or handling procedures, and may be used to determine when a tire should be replaced. Additionally, the wear rate of a tire, which is the wear of a tire over time, may be useful in estimating changes in tread depth over time in order to predict tire performance and / or tire life. Summary of the invention

[0003] The present invention includes the following technical solutions:

[0004] Solution 1. A system comprising:

[0005] a computing device comprising a processor and a memory; and

[0006] machine-readable instructions stored in the memory, which, when executed by the processor, cause the computing device to at least:

[0007] obtaining segment analysis data for a given segment and a given tire, the segment analysis data comprising at least one of: tire data, vehicle data, or manual inspection data;

[0008] determining a remaining tread depth of the tire based at least in part on the segment analysis data, the remaining tread depth corresponding to a tread depth of the tire at a start point of a segment;

[0009] determining a segment distance based at least in part on the segment analysis data, the segment distance corresponding to a distance traveled by the tire to an end point of a segment;

[0010] applying at least the remaining tread depth and the segment distance as input to a tread wear condition model; and

[0011] Tread wear conditions are predicted based at least in part on the output of the tread wear condition model.

[0012] Option 2. A system according to Option 1, wherein the segment corresponds to a time period without manipulation of the tire, and the machine-readable instructions further cause the computing device to determine at least the segment start point and the segment end point based at least in part on a predetermined travel distance of the vehicle, a predetermined time period, or a manual inspection date.

[0013] Option 3. A system according to Option 1, wherein the tread wear condition includes available time to reach a tread replacement depth, a remaining available distance for the tire to reach a tread replacement depth, or an estimated current tread depth.

[0014] Option 4. A system according to Option 1, wherein the segment analysis data includes the tire data, and the machine-readable instructions also cause the computing device to obtain at least the tire data from a sensor unit that communicates data with the at least one computing device, and the tire data includes tire parameters measured by the sensor unit installed on the tire.

[0015] Option 5. A system according to Option 1, wherein the segment analysis data includes the vehicle data, and the machine-readable instructions further cause the computing device to obtain at least the vehicle data from a vehicle CAN bus that communicates with one or more vehicle systems of a vehicle supported by the tire, the vehicle data including at least one of a vehicle speed, a vehicle load, an odometer value, or a brake cylinder pressure value.

[0016] Option 6. A system according to Option 1, wherein the segment analysis data includes the manual inspection data, and the machine-readable instructions further cause the computing device to obtain at least the manual inspection data in response to one or more user interactions with a user interface presented on a client device, wherein the manual inspection data includes measured tread depth, and at least one of an inspection date, an inspector's name, a tire location, or a tire pressure.

[0017] Option 7. The system according to Option 1, wherein the input of the tread wear condition model also includes at least one of tire position, average temperature during the segment, average pressure during the segment, or average brake cylinder pressure.

[0018] Embodiment 8. The system of embodiment 7, wherein the input further comprises the average temperature and the average pressure, and the machine-readable instructions further cause the computing device to at least:

[0019] calculating an average temperature during the segment based at least in part on temperature data included in the tire data; and

[0020] An average pressure during the segment is calculated based at least in part on temperature data included in the tire data.

[0021] Option 9. A system according to Option 1, wherein the tread wear condition model includes a linear machine learning model, and the machine-readable instructions also cause the computing device to at least execute the tread wear condition model.

[0022] Option 10. A system according to Option 1, wherein the at least one computing device includes a vehicle computing device installed in a vehicle supported by the tire or a cloud computing device remote from the vehicle.

[0023] Embodiment 11. A method comprising:

[0024] obtaining, via at least one computing device, segment analysis data for a given segment and a given tire, the segment analysis data comprising at least one of tire data, vehicle data, or manual inspection data;

[0025] determining, via the at least one computing device, a remaining tread depth of the tire based at least in part on the segment analysis data, the remaining tread depth corresponding to a tread depth of the tire at a start point of a segment;

[0026] determining, via the at least one computing device, a segment distance based at least in part on the segment analysis data, the segment distance corresponding to a distance traveled by the tire to an end point of a segment;

[0027] applying, via the at least one computing device, at least the remaining tread depth and the segment distance as input to a tread wear condition model; and

[0028] Tread wear conditions are predicted via the at least one computing device based at least in part on an output of the tread wear condition model.

[0029] Option 12. A method according to Option 11, wherein the segment corresponds to a time period without manipulation of the tire, and it also includes determining the segment start point and the segment end point based at least in part on a predetermined travel distance, a predetermined time period or a manual inspection date of the vehicle.

[0030] Option 13. The method according to Option 11, wherein the tread wear condition includes available time to reach a tread replacement depth, a remaining available distance for the tire to reach a tread replacement depth, or an estimated current tread depth.

[0031] Option 14. A method according to Option 11, wherein the section analysis data includes the tire data, and further includes obtaining the tire data from a sensor unit that communicates data with the at least one computing device, the tire data including tire parameters measured by the sensor unit installed on the tire.

[0032] Option 15. A method according to Option 11, wherein the section analysis data includes the vehicle data, and further includes obtaining the vehicle data from a vehicle CAN bus that communicates with one or more vehicle systems of a vehicle supported by the tire, the vehicle data including at least one of a vehicle speed, a vehicle load, an odometer value, or a brake cylinder pressure value.

[0033] Option 16. A method according to Option 11, wherein the segment analysis data includes the manual inspection data, which also includes obtaining the manual inspection data in response to one or more user interactions with a user interface presented on a client device, and the manual inspection data includes measured tread depth, and at least one of an inspection date, an inspector's name, a tire location, or a tire pressure.

[0034] Option 17. The method according to Option 11, wherein the input of the tread wear condition model also includes at least one of tire position, average temperature during the segment, average pressure during the segment, or average brake cylinder pressure.

[0035] Embodiment 18. The method according to embodiment 17, wherein the input further includes the average temperature and the average pressure, and further comprising:

[0036] calculating an average temperature during the segment based at least in part on temperature data included in the tire data; and

[0037] An average pressure during the segment is calculated based at least in part on temperature data included in the tire data.

[0038] Option 19. A method according to Option 11, wherein the tread wear condition model includes a linear machine learning model and also includes executing the tread wear condition model.

[0039] Option 20. The method according to Option 11, wherein the at least one computing device comprises a vehicle computing device installed in the vehicle supported by the tire or a cloud computing device remote from the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Many aspects of the present disclosure may be better understood with reference to the following drawings. The components in the drawings are not necessarily drawn to scale, but emphasis is placed on clearly illustrating the principles of the present disclosure. Furthermore, in the drawings, similar reference numerals throughout the several views indicate corresponding parts.

[0041] Figure 1 is a diagram of a vehicle with one or more tires including tread wear sensor plugs according to various embodiments of the present disclosure.

[0042] Figure 2According to various embodiments of the present disclosure Figure 1 A perspective cross-sectional view of the tire shown in FIG. 1 prior to installation of the tread wear sensor plug.

[0043] Figure 3 is a schematic block diagram of a network environment according to various embodiments of the present disclosure.

[0044] Figure 4 is an example diagram of segment analysis data and how the segment analysis data can be different for each segment according to various embodiments of the present disclosure.

[0045] Figure 5 According to various embodiments of the present disclosure, Figure 3 A flow diagram of one example of functionality of a portion of a sensor placement monitoring application executed in a computing environment within a network environment.

[0046] definition

[0047] "CAN bus" is the abbreviation for Controller Area Network.

[0048] "Innerliner" means the layer or layers of elastomer or other material that forms the inside surface of a tubeless tire and contains the inflating fluid within the tire.

[0049] “TPMS” stands for Tire Pressure Monitoring System, which is an electronic system that measures the internal pressure of a tire and can communicate that pressure to a processor mounted on the vehicle and / or electronically communicate with the vehicle's electronic systems.

[0050] "Tread element" or "traction element" means a rib or block element defined by a shape having adjacent grooves.

[0051] "Ideal tire life period" refers to the operating time span during which the tire is not manipulated (maintained, replaced, repaired (retreat), removed, etc.). DETAILED DESCRIPTION

[0052] Various methods for estimating the tread wear condition of a tire are disclosed. According to various examples, a machine learning model can be trained to predict the estimated tread wear condition of a tire. The tread wear condition can include the available time to reach the replacement tread depth, the remaining available distance for the tire to reach the replacement tread depth, the estimated remaining tread depth, and / or other data associated with the condition of the tread wear of a given tire. According to various examples, the predicted tread wear condition can be based at least in part on tire data, vehicle data, manual inspection data, and / or other data. In addition, the tread wear condition can be predicted based on an ideal segment of tire life. The ideal segment is a portion of the tire life in which the tire is not manipulated (e.g., the tire's position on the vehicle does not change, and no maintenance operations are performed). By predicting the tread wear condition of the tire based at least in part on the ideal segment of the tire life, the prediction does not suffer from inaccuracies caused by tire manipulation during the tire's service life. In addition, the model trained according to the concept of the ideal tire life segment can guarantee high performance regardless of the operations performed by the consumer. In fact, it is not necessary to track the complete tire operation history, nor is it necessary to assume the impossible situation that no events have occurred on the tire.

[0053] Commercial fleet or consumer fleet customers rely on vehicle maintenance cycles to identify worn tires. Commercial fleets may correspond to fleets of vehicles including semi-trucks, semi-trailers, dump trucks, etc. Consumer fleets may correspond to fleets of vehicles including cars, sport utility vehicles (SUVs), pickup trucks, etc. Reliance on vehicle maintenance cycles may result in the inability to plan tire purchases, missed worn tires, and / or prematurely removing tires from service. In order to support the management of tires installed on a consumer's fleet, the present disclosure provides predictions associated with tread wear conditions. Using the predicted tread wear conditions, commercial or consumer users can effectively comply with legal tread requirements, save tire inspection labor, and optimize future tire purchases. In addition, tire manufacturers can proactively supply tires to fleet customers.

[0054] Techniques have been developed to directly measure the wear state of a tire using sensors attached to the tire. Direct techniques have certain advantages, such as a relatively simple method of using sensors to measure pressure, temperature, and / or tread depth. Direct techniques also face challenges, such as properly mounting the sensors without compromising tire integrity, sensor life, and / or transmitting sensor data in the harsh environment of the tire.

[0055] Due to these challenges, indirect techniques have been developed. Indirect techniques consider certain tire and / or vehicle sensor measurements and then generate a prediction or estimate of the tire state and / or tire wear rate. While indirect techniques do not encounter the challenges of sensor installation, sensor life, and / or transmission of sensor data, they also face challenges in achieving accuracy and repeatability of the generated estimates or predictions. For example, many indirect techniques suffer from shortcomings due to the lack of optimal prediction technology, which correspondingly reduces the accuracy and / or reliability of tread wear predictions. Therefore, there is a need for a system that accurately and reliably estimates the wear rate of a tire and generates predictions for tire replacement.

[0056] In the following discussion, a general description of the system and its components is provided, followed by a discussion of its operation. Although the following discussion provides illustrative examples of the operation of various components of the present disclosure, the use of the following illustrative examples does not exclude other embodiments consistent with the principles disclosed by the following illustrative examples.

[0057] Now turn to Figure 1 and Figure 2 , shows an example of a sensor unit 103 attached to a tire 106 of a vehicle 109 according to various embodiments. In particular, Figure 1 An example of a vehicle 109 is shown that includes a plurality of tires 106 (eg, 106a, 106b, 106c, 106d, 106e). Each tire 106 includes a sensor unit 103 (eg, 103a, 103b, 103c, 103d, 103e). Figure 2 A cross-sectional view of a tire 106 is shown, illustrating the sensor unit 103 mounted to the inner surface of the tire 106 .

[0058] The tires 106 are of conventional construction, and each is mounted on a respective wheel 115, as known to those skilled in the art. Each tire 106 includes a pair of sidewalls 203 (only one shown) extending to a circumferential tread 206. An inner liner 209 is disposed on the inner surface of the tire 106 and, when the tire is mounted on the wheel 115, forms an interior cavity 200 that is filled with a pressurized fluid, such as air.

[0059] like Figure 2 As shown in FIG. 1 , the sensor unit 103 is attached to the inner liner 209 of each tire 106 by means of, for example, an adhesive, and is configured to measure certain parameters or conditions of the tire 106, as will be described in more detail below. Figure 2106, it will be appreciated that the sensor unit 103 may be attached in this manner, or to other components of the tire 106, such as on or in one of the sidewalls 203, on or in the tread 206, on the wheel 115, and / or combinations thereof. For purposes of convenience, reference will be made herein to mounting the sensor unit 103 on the tire 106, with the understanding that such mounting includes all such types of attachments.

[0060] In various examples, the sensor unit 103 is mounted on each tire 106 for the purpose of detecting certain real-time parameters of the tire 106, such as tire pressure and tire temperature. In various examples, the sensor unit 103 may include at least one of the following sensors: a pressure sensor to sense the inflation pressure within the cavity 200; a temperature sensor to measure the temperature of the tire; an accelerometer to measure the acceleration of the wheel 115 on which the tire 106 is mounted; a tachometer to measure the rotation time of the wheel 115; and / or other types of sensors. The sensor unit 103 may be part of a commercially available tire pressure monitoring system (TPMS) module or sensing unit.

[0061] In various examples, the sensor unit 103 includes a processor and a memory to store vehicle tire information for each specific tire 106. For example, the tire vehicle tire information may include a tire identifier (ID), manufacturing information of the tire 106 (e.g., model, manufacturer name, etc.), size information (e.g., rim size, width, and outer diameter), manufacturing location, manufacturing date, tread cap code including or associated with compound identification, mold code including or associated with tread structure identification, and / or other information. The vehicle tire information may also include a maintenance history or other information to identify specific features and parameters of each tire 106. Alternatively or additionally, the vehicle tire information may be included in another sensor unit 103, or in a separate vehicle storage medium (such as a tire ID tag), which is preferably in electronic communication with the sensor unit 103.

[0062] The sensor unit 103 also includes an antenna (not shown) to wirelessly transmit the measured parameters and tire information data to a remote processor (such as a processor integrated into the vehicle computing device 112, a controller area network (CAN) bus associated with the vehicle 109, and / or a cloud computing device) for analysis. According to various embodiments, each tire 106 on the vehicle 109 may include one or more sensor units 103.

[0063] Although Figure 1The vehicle 109 is depicted as a commercial truck, but the vehicle 109 may include any type of vehicle that uses tires, and is shown here as an example of a commercial truck. To this end, the vehicle 109 may include other vehicles belonging to various categories (such as passenger vehicles, off-road vehicles, etc.), wherein such vehicles 109 include a greater number of tires 106 than the vehicle 109. Figure 1 A greater or lesser number of tires 106 may be shown.

[0064] Now go to Figure 3 , a networked environment 300 is shown according to various embodiments. The networked environment 300 includes a computing environment 303, a client device 306, and a vehicle 109, which are in data communication with each other via a network 309. The vehicle 109 may include one or more vehicle computing devices 112, one or more sensor units 103, and a controller area network (CAN) bus 310, which facilitates data communication between various systems on the vehicle 109. In one embodiment, the vehicle computing device 112 is coupled to the CAN bus 310 and can communicate with the systems included on the CAN bus 310.

[0065] The network 309 may include a wide area network (WAN), a local area network (LAN), a personal area network (PAN), or a combination thereof. These networks may include wired or wireless components or a combination thereof. Wired networks may include Ethernet, cable networks, fiber optic networks, and telephone networks such as dial-up, digital subscriber line (DSL), and integrated services digital network (ISDN) networks. Wireless networks may include cellular networks, satellite networks, Institute of Electrical and Electronics Engineers (IEEE) 802.11 wireless networks (i.e., wireless networks). ), Networks, microwave transmission networks, and other networks that rely on radio broadcasting. Network 309 may also include a combination of two or more networks 309. Examples of network 309 may include the Internet, an intranet, an extranet, a virtual private network (VPN), and the like.

[0066] The computing environment 303 may include, for example, a server computer or any other system that provides computing power. Alternatively, the computing environment 303 may employ multiple computing devices, which may be arranged in, for example, one or more server arrays or computer arrays or other arrangements. Such computing devices may be located in a single installation, or may be distributed in many different geographical locations. For example, the computing environment 303 may include multiple computing devices, which may together include a hosted computing resource, a grid computing resource, and / or any other distributed computing arrangement. In some cases, the computing environment 303 may correspond to an elastic computing resource, in which the allocated capacity of processing, network, storage, or other computing-related resources may vary over time.

[0067] According to various embodiments, various applications and / or other functions may be executed in the computing environment 303. Furthermore, various data are stored in a data store 312 accessible to the computing environment 303. As may be appreciated, the data store 312 may represent a plurality of data stores 312. For example, the data stored in the data store 312 is associated with the operation of various applications and / or functional entities described below.

[0068] Various applications or other functions may be executed in the computing environment 303. Components executing on the computing environment 303 include a vehicle tire management system 315, a tread wear condition system 318, and other applications, services, processes, systems, engines, or functions not discussed in detail herein. The vehicle tire management system 315 is executed to track the location and status of tires 106 installed on multiple vehicles 109. Such a vehicle tire management system 315 may track hundreds or even thousands of tires 106 on many vehicles 109. The vehicle tire management system 315 indicates to an operator when a tire 106 may need to be repaired, replaced, or the vehicle tire management system 315 may provide other information.

[0069] Tread wear condition system 318 may be executed to predict tread wear conditions associated with tire 106 of vehicle 109. Tread wear conditions may include available time to reach a replacement tread depth, remaining available distance for the tire to reach a replacement tread depth, estimated current tread depth, and / or other data associated with tread wear conditions for a given tire 106. In various examples, tread wear condition system 318 predicts tread wear conditions associated with tire 106 based on vehicle tire data 321, vehicle data 324, manual inspection data 327, and / or other data.

[0070] In various examples, the tread wear condition system 318 is configured to provide a given ideal tire life segment 330 ( Figure 4 ) predicts tread wear conditions. Segment 330 corresponds to a time range associated with the life of the tire in which the tire is not manipulated (e.g., maintained, replaced, repaired, removed, etc.). For example, the tire 106 may be manipulated when it is moved to a different location on the vehicle. In another example, the tire 106 may be manipulated during a maintenance operation (e.g., repair, remodeling, etc.).

[0071] In various examples, the tread wear condition system 318 may be configured to determine the tread wear condition based on the segment start point 333 ( Figure 4 ) and segment end 336 ( Figure 4) to determine the segment 330. The segment start point 333 may initially correspond to the initial installation of the tire 106 on the vehicle. Thereafter, the segment start point 333 may correspond to the segment end point 336 of the previous segment 330, a time associated with the manipulation of the tire (e.g., maintenance report, inspection data, etc.), and / or other times. The segment end point 335 may be determined based at least in part on a predetermined threshold time value (e.g., 3 months, 9 months, 1 year, 2 years, etc.), a predetermined threshold distance value associated with the amount of distance traveled by the vehicle 109 supported by the tire 106 (e.g., 50,000 kilometers, 100,000 kilometers, etc.), and / or other factors. Thus, for the first segment 330 of tire life, and using the example of a threshold distance of 100,000 kilometers, the segment start point 333 may correspond to the time where the tire 106 is installed on the vehicle 109, and the segment end point 336 may correspond to the time where the vehicle 109 has traveled 100,000 kilometers. However, in some examples, if the maintenance report indicates manipulation of tire 106 more than 100,000 kilometers ago, segment start 333 may correspond to the time of the tire manipulation, rather than the time in which tire 106 was originally installed.

[0072] In various examples, the tread wear condition system 318 may obtain segment analysis data associated with a given segment 330. The segment analysis data may include vehicle tire data 321, vehicle data 324, manual inspection data 327, and / or other data. In some examples, the vehicle tire data 321 may be obtained from a sensor unit 103 in data communication with the tread wear condition system 318. In some examples, the vehicle data 324 may be obtained from a vehicle CAN bus in data communication with one or more vehicle systems of the vehicle 109 supported by the tire 106. The manual inspection data 327 may be obtained from a data store 339 of the computing environment 303 or a data store 341 of the vehicle computing device 112. The manual inspection data 327 may be stored in the data stores 339, 341 in response to one or more user interactions with the user interface 342 presented on the display 345 of the client device 306 (e.g., an inspector or maintenance provider updates a maintenance or inspection record associated with the tire 106 and / or the vehicle 109).

[0073] In response to obtaining the segment analysis data, the tread wear condition system 318 may determine a starting tread depth for the tire 106 based at least in part on the segment analysis data. For example, if the segment is the first segment 330, the starting tread depth may correspond to the original tread depth of the tire 106. Otherwise, the starting tread depth may be determined based at least in part on previous tread wear condition estimates, manual inspection data 327, and / or other data. The tread wear condition system 318 may also determine a segment distance associated with a given segment 330. The segment distance corresponds to the distance traveled by the vehicle 109 supported by the tire 106 to the end time associated with the segment 330. For example, the segment distance may correspond to an odometer reading value included in the vehicle data 324.

[0074] The tread wear condition system 318 may execute a tread wear condition model 348 that is trained to output a tread wear condition and apply the segment distance, starting or remaining tread depth, and / or other factors as inputs to the tread wear condition model 348. Each tire 106 includes a segment starting tread depth. For a new tire 106, the segment starting tread depth may correspond to the original tread depth. However, as the tire 106 wears, the tread depth decreases, and the segment starting tread depth will represent a reduced tread depth. This reduced tread depth may correspond to the segment starting tread depth of a subsequent segment 330 of the tire life. In some examples, the segment 330 starting tread depth may correspond to the tread depth measured during manual inspection or maintenance. In other examples, the segment starting tread depth may correspond to the estimated remaining tread depth associated with the previous segment 330.

[0075] In some examples, the tread wear condition model 348 can be trained to output the remaining available distance, the remaining available time for the tire 106 to reach the replacement tread depth, the estimated remaining tread depth, and / or other tire wear conditions. In various examples, the tread wear condition model 348 analyzes data inputs (such as distance, segment start tread depth, etc.) to estimate tire wear conditions. In various examples, the tread wear condition model 348 generates tread wear conditions based on input factors, replacement tread depth, and / or other factors. The remaining tread depth can be expressed as a dimension or percentage of the original tread depth. The replacement tread depth can correspond to a specific dimension or a specific percentage of the original tread depth. In some examples, the replacement tread depth can be predetermined and included in a segment rule 352 and / or a portion of the function of the tread wear condition model 248 data.

[0076] Tread wear condition system 318 may be executed to generate a notification including tread wear condition 320 associated with the output of tread wear condition model 348. Tread wear condition system 318 may transmit the notification to client device 306, vehicle computing device 112, and / or other devices for presentation on display 345 of the client device, vehicle computing device 112, vehicle, and / or other computing devices.

[0077] It should be noted that although the tread wear condition system 318 is Figure 3 Although shown as being separate from the vehicle tire management system 315, in various embodiments, at least a portion of the functionality of the tread wear condition system 318 may be implemented as part of the vehicle tire management system 315. Similarly, in some embodiments, at least a portion of the vehicle tire management system 315 may be implemented as part of the tread wear condition system 318.

[0078] Furthermore, various data are stored in a data store 339 accessible to the computing environment 303. The data store 339 may represent a plurality of data stores 339, which may include a relational database or a non-relational database (such as an object-oriented database, a hierarchical database, a hash table, or a similar key-value data store), as well as other data storage applications or data structures. Again, combinations of these databases, data storage applications, and / or data structures may be used together to provide a single logical data store. The data stored in the data store 339 is associated with the operation of various applications or functional entities described below. The data may include vehicle tire data 321, vehicle data 324, manual inspection data 327, segment rules 352, tread wear condition model 348, and possibly other data.

[0079] The vehicle tire data 321 may include information for each specific tire 106. For example, the vehicle tire data 321 may include a tire identifier, manufacturing information of the tire 106 (e.g., manufacturer name, tire model, etc.), tire size information (e.g., rim size, width, and outer diameter, etc.), manufacturing location, manufacturing date, tread cap code including or associated with compound identification, mold code including or associated with tread structure identification, and / or other information. The vehicle tire data 321 may also include a maintenance history or other information to identify specific features and parameters of each tire 106. The vehicle tire data 321 may also include a sensor unit ID 355, sensor parameter data 358, tire wear condition 320, and / or other data.

[0080] The sensor unit ID 355 may include an alphanumeric identifier that may be used to identify the sensor unit 103 on a given tire 106. In some examples, the sensor unit ID 355 may include additional information about the sensor unit 103, including the location of the tire, on which axle the tire 106 is mounted, on which side of the vehicle 109 the tire 106 is located (e.g., left, right), and / or other information.

[0081] The sensor parameter data 358 may include data collected from the sensor unit 103 on a given tire 106. As previously discussed, the sensor unit 103 may include at least one of a pressure sensor, a temperature sensor, an accelerometer, a tachometer, and / or other types of sensors. Thus, the sensor parameter data 358 may include temperature data, pressure data, accelerometer data, rotation data, and / or other types of data.

[0082] The tread wear condition data 320 may include data associated with the tread wear condition for a given segment 330. For example, the tread wear condition data 320 may include the remaining available distance, the remaining available time for the tire 106 to reach the replacement tread depth, the estimated remaining tread depth, and / or other tire wear condition data for the given segment 330. In addition, the tread wear condition data 320 may include a segment start point 333, a segment end point 336, a starting segment tread depth, and / or other data.

[0083] The vehicle data 324 may include data associated with the vehicle 109 supported by the tire 106. The vehicle data 324 may be obtained from a vehicle CAN bus that communicates with one or more vehicle systems of the vehicle supported by the tire 106. The vehicle data 324 may include vehicle speed, vehicle load, odometer values, brake cylinder pressure values, and / or other types of vehicle data.

[0084] Manual inspection data 327 may include data associated with an inspection or repair performed on vehicle 109 and / or a given tire 106. Manual inspection data 327 may include measured tread depth, inspection date, inspector name, tire location, tire pressure, repairs performed, and / or other information available during the inspection or repair.

[0085] The segment rules 352 include rules, models, and / or configuration data for various algorithms or methods employed by the tread wear condition system 318, the vehicle tire management system 315, and / or other applications or devices. For example, the segment rules 352 may include various models and / or algorithms for determining the segment start point 333 and the segment end point 336 for a given segment. In some examples, the segment rules 352 may include thresholds associated with defining segment lengths and / or when tread wear conditions will be determined. For example, the segment rules 353 may include predetermined segment distances, predetermined time values, and / or other rules or values ​​that may be used to trigger the tread wear condition system 318 to identify a segment 330 and calculate the tread wear condition for a given tire 106. In some examples, the segment rules 352 may include a replacement tread depth and / or other values ​​that may be used by the tread wear condition system and / or the tread wear condition model 348 to generate a tread wear condition for the tire 106 for a given segment 330.

[0086] The tread wear condition model 348 comprises a machine learning model that is trained to predict the tread wear condition of a given tire 106. In various examples, the tread wear condition model 348 comprises a linear machine learning model. For example, the tread wear condition model 348 may include a parametric model that takes a fixed mathematical form to calculate the output. The residual, which is the difference between the observed value and the predicted value, may then be adjusted by a non-parametric model. The results are generated in different forms, such as charts, files, and variables in an interactive environment. Survival analysis techniques and non-parametric models take multiple continuous and categorical parameters as input.

[0087] The vehicle 109 includes a plurality of sensor units 103, each sensor unit 103 being positioned on the inside of a respective one of a plurality of tires 106 of the vehicle 109. The sensor units 103 may be employed as part of a tire pressure monitoring system (TPMS), as described below. Some of the tires 106 are mounted on dual hubs, such as in Figure 1 The depicted vehicle 109 comprises an eighteen wheel tractor trailer.

[0088] Each sensor unit 103 includes a processor and memory to store vehicle tire information for each specific tire 106. For example, the vehicle tire information may include a tire identifier (ID), manufacturing information of the tire 106 (e.g., model, manufacturer name, etc.), size information (e.g., rim size, width, and outer diameter), manufacturing location, manufacturing date, tread cap code including or associated with compound identification, mold code including or associated with tread structure identification, and / or other information. The vehicle tire information may also include a service history or other information to identify specific features and parameters of each tire 106. Alternatively or additionally, the vehicle tire information may be included in another sensor unit 103, or in a separate vehicle storage medium (such as a tire ID tag), which is preferably in electronic communication with the sensor unit 103.

[0089] The sensor unit 103 also includes an antenna (not shown) to wirelessly transmit the measured parameters and tire information data to a remote processor (such as a processor integrated into the vehicle computing device 112 and / or a controller area network (CAN) bus 310 associated with the vehicle 109) for analysis.

[0090] The vehicle computing device 112 includes a processor circuit that executes, for example, the tread wear condition system 318 and / or other applications. In one embodiment, the vehicle computing device 112 can be integrated with other systems in the vehicle 109. In the event that the vehicle includes a towing trailer, the vehicle computing device 112 can be located at the rear of the trailer so as to be within range of wireless communication with the sensor unit 103 in the tire 106 of the trailer. The vehicle computing device 112 may also include a receiver 361 to obtain sensor parameter data 358 transmitted from the sensor unit 103 on the tire 106 of the vehicle. In various examples, the vehicle computing device 112 may include a communication system to facilitate communication with a computing environment over the network 309. In this regard, the vehicle computing device 112 may include appropriate communication capabilities to link to a cellular network, a Wi-Fi network, network, microwave transmission network, radio broadcast network or other communications network.

[0091] Furthermore, various data is stored in a data store 341 accessible to the vehicle computing device 112. As can be appreciated, the data store 341 can represent a plurality of data stores 341. For example, the data stored in the data store 341 is associated with the operation of various applications and / or functional entities associated with the vehicle 109, the computing environment 303, and / or other computing devices or servers. For example, the data store 341 can include vehicle tire data 321, vehicle data 324, manual inspection data 327, and / or other information.

[0092] It should be noted that although the functionality of the tread wear condition system 318 and the vehicle tire management system 315 is Figure 3 303, but in some embodiments, as can be appreciated, the functionality of at least a portion of these systems can be implemented in the vehicle computing device 112, the sensor unit 103, and / or other computing devices or environments.

[0093] The client device 306 represents a plurality of client devices that can be connected to the network 309. The client device 306 may include a processor-based system, such as a computer system. Such a computer system may be in the form of a personal computer (e.g., a desktop computer, a laptop computer, or a similar device), a mobile computing device (e.g., a personal digital assistant, a cellular phone, a smart phone, a web tablet, a tablet computer system, a music player, a portable game console, an e-book reader, and a similar device), a media playback device (e.g., a media streaming device, a Blu-ray player, a digital video disc (DVD) player, a set-top box, and a similar device), a video game console, or other devices with similar capabilities. The client device 306 may include one or more displays 345, such as a liquid crystal display (LCD), a gas plasma-based flat panel display, an organic light emitting diode (OLED) display, an electrophoretic ink ("E-ink") display, a projector, or other types of display devices. In some cases, the display 345 may be a component of the client device 306, or may be connected to the client device 306 via a wired or wireless connection.

[0094] The client device 306 may be configured to execute various applications, such as client application 364 or other applications. The client application 364 may be executed in the client device 306 to access network content provided by the computing environment 303 or other servers, thereby presenting the user interface 342 on the display 345. To this end, the client application 364 may include a browser, a dedicated application, or other executable application, and the user interface 342 may include a web page, an application screen, or other user mechanism for obtaining user input. The client device 306 may be configured to execute applications other than the client application 364, such as an email application, a social networking application, a word processor, a spreadsheet, or other applications.

[0095] Next, refer to Figure 4-5 , provides a general description of the operation of the various components of the network environment 300. First, Figure 4An example of segment analysis data 403 and how segment analysis data 403 can be different for each segment 330 is shown. According to various examples, segment analysis data 403 can include or otherwise be generated based at least in part on tire vehicle data 321, vehicle data 324, manual inspection data 327, and / or other data. Tread wear conditions are predicted based at least in part on analysis of a tread wear condition model 348, where segment analysis data 403 is applied as an input to tread wear condition model 348.

[0096] In various examples, tread wear conditions may be predicted based on an ideal segment of tire life. The ideal segment is a portion of the tire life in which the tire is not manipulated (eg, the tire remains in place on the vehicle, no maintenance operations are performed).

[0097] Figure 4 Three segments 330 (e.g., 330a, 330b, 330c) are shown, each segment having a different segment start point 333 (e.g., 333a, 333b, 333c) and a different segment end point 336 (e.g., 336a, 336b, 336c). For each segment 330, the segment analysis data 403 will be different. For example, for segment 330a, the starting tread depth is 8.85, the ending tread depth is 7.73, the segment distance is 30156, the tire position is located on the right side of the second axis, the average temperature value is 33.76, and the average pressure value is 373839. Differently, for segment 330b, the starting tread depth is 7.73, the ending tread depth is 7.67, the segment distance is 45204, the tire position is located on the right side of the second axis, the average temperature value is 17.34, and the average pressure value is 890495. By predicting the tread wear condition of tire 106 based at least in part on segment analysis data 403 associated with an ideal segment of tire life, the prediction is not subject to inaccuracies that may result from tire handling during the useful life of tire 106 .

[0098] Next reference Figure 5 , shows a flow chart providing one example of the operation of a portion of the tread wear condition system 318 . Figure 5 The flowchart of provides only an example of many different types of functional arrangements that may be employed to implement the operation of the depicted portion of tread wear condition system 318. As an alternative, Figure 5 The flowchart of FIG. 300 may be viewed as an example of depicting elements of a method implemented within the network environment 300 .

[0099] Beginning at block 503, the tread wear condition system 318 determines whether to analyze a segment 330. The segment 330 corresponds to a time range associated with the tire life in which the tire is not manipulated. In various examples, the tread wear condition system 318 may determine whether to analyze a segment 330 based on the segment start point 333 ( Figure 4 ) and segment end 336 ( Figure 4 ) to determine the segment 330. The segment start point 333 may initially correspond to the initial installation of the tire 106 on the vehicle. Thereafter, the segment start point 333 may correspond to the segment end point 336 of the previous segment 330, a time associated with the manipulation of the tire (e.g., maintenance report, inspection data, etc.), and / or other times. The segment end point 335 may be determined based at least in part on a predetermined threshold time value (e.g., 3 months, 9 months, 1 year, 2 years, etc.), a predetermined threshold distance value associated with the amount of distance traveled by the vehicle 109 supported by the tire 106 (e.g., 50,000 kilometers, 100,000 kilometers, etc.), and / or other factors. The tread wear condition system 318 may be triggered to analyze the segment 330 in response to the indication of the segment end point 335. If the segment 330 is to be analyzed, the process continues to box 506, otherwise, the process waits at box 503.

[0100] At box 506, the tread wear condition system 318 obtains the segment analysis data 403. The segment analysis data 403 may include tire vehicle data 321, vehicle data 324, manual inspection data 327, and / or other data. In some examples, the vehicle tire data 321 may be obtained from a sensor unit 103 in data communication with the tread wear condition system 318. In some examples, the vehicle data 324 may be obtained from a vehicle CAN bus in data communication with one or more vehicle systems of the vehicle 109 supported by the tire 106. The manual inspection data 327 may be obtained from a data store 339 of the computing environment 303 or a data store 341 of the vehicle computing device 112. The manual inspection data 327 may be stored in the data store 339, 341 in response to one or more user interactions with the user interface 342 presented on the display 345 of the client device 306 (e.g., an inspector or maintenance provider updates a maintenance or inspection record associated with the tire 106 and / or the vehicle 109).

[0101] At block 509, tread wear condition system 318 determines the remaining tread depth. If segment 330 is a first segment, tread wear condition system 318 may determine that the remaining tread depth corresponds to the original tread depth of tire 106. In some examples, the original tread depth is included in tire vehicle data 321. If segment 330 is not a first segment, tread wear condition system 318 may determine the remaining tread depth based at least in part on manual inspection data 327, tread wear condition data 320 associated with a previous segment 330, and / or other data.

[0102] At block 512, tread wear condition system 318 determines a segment distance. The segment distance corresponds to the distance traveled by vehicle 109 supported by tire 106 to the end time associated with segment 330. For example, the segment distance may correspond to an odometer reading value included in vehicle data 324.

[0103] At block 515, the tread wear condition system 318 generates a model input based at least in part on the segment distance and the remaining tread depth. In various examples, the model input may include other data associated with the tire 106 and / or the vehicle 109, such as, for example, tire position, average temperature during the segment, average pressure during the segment, average brake cylinder pressure, and / or other data. In examples that include average temperature or average pressure as a model input, the tread wear condition system 318 may calculate the average temperature and / or average pressure for a given segment 330 based at least in part on the temperature and / or pressure values ​​included in the sensor parameter data 358.

[0104] At box 518, the tread wear condition system 318 applies the model inputs to the tread wear condition model 348. In various examples, the tread wear condition system 318 may execute the tread wear condition model 348 and apply the segment distance, remaining tread depth, and / or other model inputs to the tread wear condition model 348. The tread wear condition system 318 is trained to output tread wear conditions and / or data that can be used to generate tread wear conditions. The tread wear conditions may include the available time to reach the replacement tread depth, the remaining available distance for the tire to reach the replacement tread depth, the estimated remaining tread depth, and / or other data associated with the condition of the tread wear of a given tire.

[0105] At block 521 , tread wear condition system 318 determines a tread wear condition of tire 106 for a given segment based at least in part on the output of tread wear condition model 348 .

[0106] At block 524, the tread wear condition system 318 generates a notification including the predicted tread wear condition. The notification may include a user interface 342 including content notifying an entity associated with the vehicle 109 (e.g., a fleet manager, a user, etc.) about the tread wear condition of the tire 106 for the given segment 330. At block 527, the tread wear condition system 318 may transmit the notification to the client device 306, the vehicle computing device 112, and / or other devices for presentation on a display 345 of the client device, the vehicle computing device 112, the vehicle, and / or other computing devices.

[0107] At block 530, the tread wear condition system 318 determines whether to continue monitoring the tire 106. For example, if the tire 106 is removed from the vehicle 109 due to tread wear condition, tire life has expired, and / or other reasons, the tread wear condition system 318 will determine that monitoring is no longer required. Thereafter, this portion of the process continues to completion. Otherwise, the tread wear condition system 318 will return to block 503.

[0108] Several software components discussed previously are stored in the memory of the corresponding computing device and can be executed by the processor of the corresponding computing device. In this regard, the term "executable" refers to a program file in a form that can ultimately be run by the processor. An example of an executable program can be a compiler that can be converted into machine code in a format that can be loaded into a random access portion of the memory and run by the processor; a source code that can be represented in an appropriate format, such as an object code that can be loaded into a random access portion of the memory and executed by the processor; or a source code that can be interpreted by another executable program to generate instructions in a random access portion of the memory for execution by the processor. The executable program can be stored in any part or component of the memory, including a random access memory (RAM), a read-only memory (ROM), a hard drive, a solid-state drive, a universal serial bus (USB) flash drive, a memory card, an optical disk (such as a compact disc (CD) or a digital versatile disc (DVD), a floppy disk, a tape, or other storage component.

[0109] The memory includes both volatile and non-volatile memory and data storage components. Volatile components are those components that do not retain data values ​​when the power is off. Non-volatile components are those components that retain data when the power is off. Therefore, the memory may include random access memory (RAM), read-only memory (ROM), hard disk drive, solid state drive, USB flash drive, memory card accessed via a memory card reader, floppy disk accessed via an associated floppy disk drive, optical disk accessed via an optical drive, magnetic tape accessed via an appropriate tape drive, or other storage components, or any combination of two or more of these storage components. In addition, RAM may include static random access memory (SRAM), dynamic random access memory (DRAM) or magnetic random access memory (MRAM) and other such devices. ROM may include programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM) or other similar storage devices.

[0110] Although the applications and systems described herein may be implemented in code or software executed by general-purpose hardware as discussed above, as an alternative, they may also be implemented in dedicated hardware or a combination of software / general-purpose hardware and dedicated hardware. If implemented in dedicated hardware, each may be implemented as a circuit or state machine that uses a combination of any one or more of a variety of technologies. These technologies may include, but are not limited to, discrete logic circuits with logic gates (which are used to implement various logic functions when one or more data signals are applied), application-specific integrated circuits (ASICs) with appropriate logic gates, field programmable gate arrays (FPGAs), or other components, etc. Such technologies are generally well known to those skilled in the art and are therefore not described in detail herein.

[0111] The flow chart shows the functions and operations of the implementation scheme of the part of various embodiments of the present disclosure. If implemented in software form, each box can represent a module, section or code including program instructions to implement a specific logic function. The program instruction can be implemented in the form of source code, and the source code includes a human-readable statement or machine code written in a programming language, and the machine code includes digital instructions that can be recognized by a suitable execution system (such as a processor in a computer system). Machine code can be converted from source code by various processes. For example, a compiler can be used to generate machine code from source code before executing the corresponding application. As another example, machine code can be generated from source code while using an interpreter to execute. Other methods can also be used. If implemented in hardware form, each box can represent a circuit or multiple interconnected circuits to implement a specific logic function.

[0112] Although the flowchart shows a specific execution order, it should be understood that the execution order may be different from the depicted order. For example, the execution order of two or more boxes may be disrupted relative to the shown order. Moreover, two or more boxes shown in succession may be executed simultaneously or partially simultaneously. In addition, in some embodiments, one or more boxes shown in the flowchart may be skipped or omitted. In addition, for the purpose of enhancing practicality, accounting, performance measurement, or providing troubleshooting assistance, any number of counters, state variables, warning semaphores, or messages may be added to the logic flow described herein. It is understood that all such changes are within the scope of the present disclosure.

[0113] Moreover, any logic or application described herein, including software or code, may be implemented in any non-transient computer-readable medium for use by or in connection with an instruction execution system (such as a processor in a computer system or other system). In this sense, logic may include statements including instructions and declarations that may be obtained from a computer-readable medium and executed by an instruction execution system. In the context of the present disclosure, a "computer-readable medium" may be any medium that may contain, store, or maintain the logic or application described herein for use by or in connection with an instruction execution system. Furthermore, a collection of distributed computer-readable media located on multiple computing devices (such as a storage area network or a distributed or clustered file system or database) may also be considered collectively as a single non-transient computer-readable medium.

[0114] Computer readable media may include any of many physical media, such as magnetic, optical or semiconductor media. More specific examples of suitable computer readable media will include, but are not limited to, magnetic tape, magnetic floppy disk, magnetic hard drive, memory card, solid state drive, USB flash drive or optical disk. Moreover, the computer readable medium may be a random access memory (RAM), including a static random access memory (SRAM) and a dynamic random access memory (DRAM), or a magnetic random access memory (MRAM). In addition, the computer readable medium may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM) or other types of storage devices.

[0115] In addition, any logic or application described herein can be implemented and constructed in a variety of ways. For example, one or more applications described herein can be implemented as modules or components of a single application. In addition, one or more applications described herein can be executed in a shared or separate computing device or a combination thereof. For example, multiple applications described herein can be executed in the same computing device, or in multiple computing devices in the same computing environment 303.

[0116] Unless expressly stated otherwise, disjunctive language, such as the phrase "at least one of X, Y, or Z," should be understood as generally used to indicate that an item, term, etc., may be any one of X, Y, or Z, or any combination thereof (e.g., X; Y; Z; X or Y; X or Z; Y or Z; X, Y, or Z; etc.), depending on the context. Thus, such disjunctive language is generally not intended to, and should not, imply that certain embodiments require that each of at least one X, at least one Y, or at least one Z be present.

[0117] It should be emphasized that the above-mentioned embodiments of the present disclosure are merely possible examples of implementation methods proposed for a clear understanding of the principles of the present disclosure. Many modifications and variations may be made to the above-mentioned embodiments without substantially departing from the spirit and principles of the present disclosure. All such modifications and variations are intended to be included herein within the scope of the present disclosure and protected by the following claims.

Claims

1. A system comprising: a computing device comprising a processor and a memory; as well as machine-readable instructions stored in the memory, which, when executed by the processor, cause the computing device to at least: obtaining segment analysis data for a given segment and a given tire, the segment analysis data comprising at least one of: tire data, vehicle data, or manual inspection data; determining a remaining tread depth of the tire based at least in part on the segment analysis data, the remaining tread depth corresponding to a tread depth of the tire at a start point of a segment; determining a segment distance based at least in part on the segment analysis data, the segment distance corresponding to a distance traveled by the tire to an end point of a segment; applying at least the remaining tread depth and the segment distance as input to a tread wear condition model; and Tread wear conditions are predicted based at least in part on the output of the tread wear condition model.

2. The system according to claim 1, wherein: The segment corresponds to a time period without manipulation of the tire, and the machine-readable instructions further cause the computing device to determine at least the segment start point and the segment end point based at least in part on a predetermined travel distance of the vehicle, a predetermined time period, or a manual inspection date.

3. The system according to claim 1, wherein: The tread wear condition includes available time to reach a tread replacement depth, a remaining available distance for the tire to reach a tread replacement depth, or an estimated current tread depth.

4. The system according to claim 1, wherein: The section analysis data includes the tire data, and the machine-readable instructions further cause the computing device to obtain at least the tire data from a sensor unit in data communication with the at least one computing device, the tire data including tire parameters measured by the sensor unit mounted on the tire.

5. The system according to claim 1, wherein: The segment analysis data includes the vehicle data, and the machine-readable instructions further cause the computing device to obtain at least the vehicle data from a vehicle CAN bus that communicates with one or more vehicle systems of a vehicle supported by the tire, the vehicle data including at least one of a vehicle speed, a vehicle load, an odometer value, or a brake cylinder pressure value.

6. The system according to claim 1, wherein: The segment analysis data includes the manual inspection data, and the machine-readable instructions further cause the computing device to obtain at least the manual inspection data in response to one or more user interactions with a user interface presented on a client device, the manual inspection data including measured tread depth and at least one of an inspection date, an inspector's name, a tire location, or a tire pressure.

7. The system according to claim 1, wherein: The input to the tread wear condition model also includes at least one of tire position, average temperature during the segment, average pressure during the segment, or average brake cylinder pressure.

8. The system according to claim 7, wherein: The inputs also include the average temperature and the average pressure, and the machine-readable instructions further cause the computing device to at least: calculating an average temperature during the segment based at least in part on temperature data included in the tire data; and An average pressure during the segment is calculated based at least in part on temperature data included in the tire data.

9. The system according to claim 1, wherein: The tread wear condition model includes a linear machine learning model, and the machine-readable instructions further cause the computing device to at least execute the tread wear condition model.

10. The system according to claim 1, wherein: The at least one computing device includes a vehicle computing device mounted in a vehicle supported by the tire or a cloud computing device remote from the vehicle.

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