Patent Description:
Tire wear plays an important role in vehicle factors such as safety, reliability, and performance. Tread wear, which refers to the loss of material from the tread of the tire, directly affects such vehicle factors. As a result, it is desirable to monitor the tread wear experienced by a tire. It is to be understood that, for the purpose of convenience, the terms "tread wear" and "tire wear" may be used interchangeably.

One approach to the monitoring of tread wear has been through the use of wear sensors disposed in the tire tread, which has been referred to as a direct method or approach. The direct approach to monitoring tire wear from tire-mounted sensors has multiple challenges. Placing the sensors in an uncured or "green" tire to then be cured at high temperatures may cause damage to the wear sensors. In addition, sensor durability can prove to be an issue in meeting the millions of cycles requirement for tires. Moreover, wear sensors in a direct monitoring approach must be small enough not to cause any uniformity problems as the tire rotates at high speeds. Finally, wear sensors can be expensive and add significantly to the cost of the tire.

Due to such challenges, alternative approaches have been developed, which involve prediction of tread wear over the life of the tire, including indirect estimations of the tire wear state. These alternative approaches have experienced certain disadvantages in the prior art due to a lack of optimum prediction techniques, which reduces the accuracy and/or reliability of the tread wear predictions. For example, many such techniques involve data or information that is not easily obtained, such as non-standard vehicle system signals, or data that is not accurate under all driving conditions.

In addition, certain prior art techniques of indirectly estimating tire wear involve obtaining data from the vehicle controller area network, which is referred to in the art as the vehicle CAN bus. It may be undesirably difficult to access or utilize the vehicle CAN bus in an economical and reliable manner.

Furthermore, prior art indirect techniques do not detect uneven or irregular wear of the tread. More particularly, in order to maintain optimum grip or traction as the tire wears, it is desirable for the tread to wear uniformly across the width of the tire. Uneven or irregular tire wear occurs when the tread wears more rapidly at one or both shoulders than at the center of the tread. Such irregular wear may be caused by improper alignment of the tires on the vehicle and/or improper inflation, and may compromise the traction or life of the tire. It is therefore advantageous to detect irregular wear of a tire with an indirect technique.

As a result, there is a need in the art for a system and method that accurately and reliably detects irregular wear of a tire using easily obtained and accurate parameters.

<CIT>, <CIT>, <CIT>, <CIT> and <CIT> each describe a system and method in accordance with the preamble of claim1 or with the preamble of claim <NUM> respectively.

<CIT> describes a tire wear estimation system and method. It does however not detect irregular wear.

According to an exemplary embodiment of the invention, an irregular wear detection system for a tire supporting a vehicle is provided. The system includes a sensor unit that is mounted on the tire. The sensor unit includes a footprint centerline length measurement sensor to measure a centerline length of a footprint of the tire. A processor is in electronic communication with the sensor unit and receives a plurality of measured centerline lengths over time. An analysis module is stored on the processor and receives the measured centerline lengths as inputs. The analysis module detects irregular wear of the tire from the measured footprint centerline lengths. An irregular wear determination is generated by the analysis module when the measured footprint centerline lengths remain the same or increase.

According to another exemplary embodiment of the invention, a method for detecting irregular wear of a tire supporting a vehicle is provided. The method includes the step of mounting a sensor unit on the tire. The sensor unit includes a footprint centerline length measurement sensor. A centerline length of a footprint of the tire is measured with the footprint centerline length measurement sensor. A processor is provided in electronic communication with the sensor unit, and the processor receives a plurality of measured centerline lengths over time. An analysis module is stored on the processor and the measured centerline lengths are received in the analysis module as inputs. Irregular wear of the tire is detected from the measured footprint centerline lengths, and an irregular wear determination is generated with the analysis module when the measured footprint centerline lengths remain the same or increase.

"ANN" or "artificial neural network" is an adaptive tool for non-linear statistical data modeling that changes its structure based on external or internal information that flows through a network during a learning phase. ANN neural networks are non-linear statistical data modeling tools used to model complex relationships between inputs and outputs or to find patterns in data.

"CAN bus" is an abbreviation for controller area network.

"Cloud computing" or "cloud" means computer processing involving computing power and/or data storage that is distributed across multiple data centers, which is typically facilitated by access and communication using the Internet.

"Equatorial center plane (CP)" means the plane perpendicular to the axis of rotation of the tire and passing through the center of the tread.

"Footprint" means the contact patch or area of contact created by the tire tread with a flat surface as the tire rotates or rolls.

With reference to <FIG>, an exemplary embodiment of the tire irregular wear detection system <NUM> of the present invention is presented. The tire irregular wear detection system <NUM> and an accompanying method are referred herein as an "indirect" system and method. The system <NUM> and method utilize an indirect approach to detect irregular wear of a tire and avoid issued associated with the use of sensors mounted directly to the tire tread.

With particular reference to <FIG>, the system <NUM> detects irregular wear on each tire <NUM> supporting a vehicle <NUM>. For the purpose of convenience, the system <NUM> is described with reference to one tire <NUM>, with the understanding that the description applies to each tire supporting the vehicle <NUM>. In addition, while the vehicle <NUM> is depicted as a passenger car, the invention is not to be so restricted. The principles of the invention find application in other vehicle categories, such as commercial trucks, in which vehicles may be supported by more or fewer tires than those shown in <FIG>.

The tires <NUM> are of conventional construction, and each tire is mounted on a respective wheel <NUM> as known to those skilled in the art. Each tire <NUM> includes a pair of sidewalls <NUM> that extend to a circumferential tread <NUM>, which wears with age from road abrasion. An innerliner <NUM> is disposed on the inner surface of the tire <NUM>, and when the tire is mounted on the wheel <NUM>, an internal cavity <NUM> is formed, which is filled with a pressurized fluid, such as air.

A sensor unit <NUM> is attached to the innerliner <NUM> of each tire <NUM> by means such as an adhesive, and measures certain parameters or conditions of the tire, as will be described in greater detail below. It is to be understood that the sensor unit <NUM> may be attached in such a manner, or to other components of the tire <NUM>, such as on or in one of the sidewalls <NUM>, on or in the tread <NUM>, on the wheel <NUM>, and/or a combination thereof. For the purpose of convenience, reference herein shall be made to mounting of the sensor unit <NUM> on the tire <NUM>, with the understanding that such mounting includes all such attachment.

The sensor unit <NUM> is mounted on each tire <NUM> for the purpose of detecting certain real-time tire parameters, such as tire pressure <NUM> (<FIG>) and temperature <NUM>. For this reason, the sensor unit <NUM> preferably includes a pressure sensor and a temperature sensor and may be of any known configuration.

The sensor unit <NUM> also optionally includes electronic memory capacity for storing identification (ID) information for each tire <NUM>, known as tire ID information and indicated at <NUM> (<FIG>). Alternatively, tire ID information <NUM> may be included in another sensor unit, or in a separate tire ID storage medium, such as a tire ID tag, which preferably is in electronic communication with the sensor unit <NUM>. The tire ID information <NUM> may include tire parameter and/or manufacturing information, which will be described in greater detail below.

Turning to <FIG>, the sensor unit <NUM> preferably also measures a length <NUM> of a centerline <NUM> of a footprint <NUM> of the tire <NUM>. More particularly, as the tire <NUM> contacts the ground, the area of contact created by the tread <NUM> with the ground is known as the footprint <NUM>. The centerline <NUM> of the footprint <NUM> corresponds to the equatorial center plane of the tire <NUM>, which is the plane that is perpendicular to the axis of rotation of the tire and which passes through the center of the tread <NUM>. The sensor unit <NUM> thus measures the length <NUM> of the centerline <NUM> of the tire footprint <NUM>, which is referred to herein as the footprint centerline length <NUM>. Any suitable technique for measuring the footprint centerline length <NUM> may be employed by the sensor unit <NUM>. For example, the sensor unit <NUM> may include a strain sensor or piezoelectric sensor that measures deformation of the tread <NUM> and thus indicates the footprint centerline length <NUM>.

It is to be understood that the pressure sensor, the temperature sensor, the tire ID capacity and/or the centerline length sensor may be incorporated into the single sensor unit <NUM> or may be incorporated into multiple units. For the purpose of convenience, reference herein shall be made to a single sensor unit <NUM>.

It has been observed that, as the tire <NUM> experiences uniform wear, the footprint centerline length <NUM> decreases. For example, turning to <FIG>, the footprint <NUM> for a new tire includes a footprint centerline length 28N. When the tire experiences uniform wear, as shown in <FIG>, the footprint <NUM> for the worn tire includes a footprint centerline length 28U. The footprint centerline length 28U of the uniformly worn tire is shorter than the footprint centerline length 28N of the new tire <NUM>.

More particularly, with additional reference to <FIG>, a wear distribution plot <NUM> for a tire <NUM> experiencing uniform wear shows a remaining tread depth <NUM> versus a position <NUM> across a width of the tread <NUM>. The resulting lines <NUM> indicate uniform or even wear across the tread <NUM>. As shown in <FIG>, a plot <NUM> of the footprint centerline length <NUM> for a tire <NUM> experiencing uniform wear versus the remaining tread depth <NUM> shows that the footprint centerline length decreases as the tire wears.

It has been discovered that, when the tire <NUM> experiences irregular wear, the footprint centerline length <NUM> increases or shows no change. For example, turning to <FIG>, the footprint <NUM> for a new tire includes the footprint centerline length 28N. When the tire experiences irregular wear, as shown in <FIG>, the footprint <NUM> for the worn tire includes a footprint centerline length 28I. The footprint centerline length <NUM> of the irregularly worn tire is longer than or is the same as the footprint centerline length 28N of the new tire <NUM>.

More particularly, with additional reference to <FIG>, a wear distribution plot <NUM> for a tire <NUM> experiencing irregular wear shows the remaining tread depth <NUM> versus the position <NUM> across a width of the tread <NUM>. The resulting lines <NUM> indicate irregular or uneven wear across the tread <NUM>. As shown in <FIG>, a plot <NUM> of the footprint centerline length <NUM> for a tire <NUM> experiencing irregular wear versus the remaining tread depth <NUM> shows that the footprint centerline length increases or remains the same as the tire wears.

Returning to <FIG> and <FIG>, the tread <NUM> includes a shoulder <NUM> near each respective sidewall <NUM>. When the tire <NUM> experiences uneven or irregular wear, the tread <NUM> wears more rapidly at one or both shoulders <NUM> than at the centerline <NUM>. <FIG> is a wear distribution plot <NUM> showing irregular wear of the tread <NUM> at both shoulders <NUM>. Irregular wear of both shoulders <NUM> is typically caused by uneven contact pressure of the tire <NUM> with the road surface, which may be due to under-inflation of the tire and/or the design of the tire. <FIG> is a wear distribution plot <NUM> showing irregular wear of the tread <NUM> at one shoulder <NUM>. Irregular wear of one shoulder <NUM> is typically caused by excessive positive or negative camber of the tire <NUM>, which is the vertical tilt of the tire with respect to the vehicle <NUM>. As described above, it is beneficial to detect such irregular wear of the tire <NUM>, as it may compromise the traction or life of the tire.

Turning now to <FIG>, the irregular wear detection system <NUM> measures the footprint centerline length <NUM> of the tire <NUM> to detect irregular wear. More particularly, as mentioned above, the sensor unit <NUM> measures the footprint centerline length <NUM>, and may measure other tire parameters, such as tire pressure <NUM> and tire temperature <NUM>. The sensor unit <NUM> includes transmission means <NUM> for sending the measured tire parameters, as well as the optional tire ID information <NUM>, to a processor <NUM>. The transmission means <NUM> may include an antenna for wireless transmission or wires for wired transmission. The processor <NUM> may be integrated into the sensor unit <NUM>, or may be a remote processor, which may be mounted on the vehicle <NUM> or may be cloud-based. For the purpose of convenience, the processor <NUM> will be described as a remote processor mounted on the vehicle <NUM>, with the understanding that the processor may alternatively be cloud-based or integrated into the sensor unit <NUM>.

Aspects of the irregular wear detection system <NUM> preferably are executed on the processor <NUM>, which enables input of data from the sensor unit <NUM> and execution of specific analysis techniques and algorithms, to be described below, which are stored in a suitable storage medium and are also in electronic communication with the processor.

In this manner, the sensor unit <NUM> measures the footprint centerline length <NUM>, and may measure the tire pressure <NUM> and tire temperature <NUM>, and transmits these measured parameters to the processor <NUM> with the optional tire ID information <NUM>. When employed, the tire ID information <NUM> enables a tire construction database <NUM> to be electronically accessed <NUM>. The tire construction database <NUM> stores tire construction data <NUM>, which will be described in greater detail below. The database <NUM> is in electronic communication with the processor <NUM> and may be stored on the processor, enabling transmission <NUM> of the tire construction data <NUM> to the processor <NUM>.

The tire ID information <NUM> may be correlated to specific construction data <NUM> for each tire <NUM>, including: the tire type; tire model; size information, such as rim size, width, and outer diameter; manufacturing location; manufacturing date; a tread cap code that includes or correlates to a compound identification; a mold code that includes or correlates to a tread structure identification; a tire footprint shape factor (FSF), a mold design drop; a tire belt/breaker angle; and an overlay material. The tire ID information <NUM> may also correlate to a service history or other information to identify specific features and parameters of each tire <NUM>, as well as mechanical characteristics of the tire, such as cornering parameters, spring rate, load-inflation relationship, and the like.

An analysis module <NUM> is stored on the processor <NUM>, and receives the footprint centerline length <NUM>, as well as the tire pressure <NUM> and tire temperature <NUM>. When the optional tire ID information <NUM> is employed, the analysis module <NUM> also receives the tire ID information and the tire construction data <NUM>. The analysis module <NUM> analyzes these inputs to generate a determination of irregular wear, indicated at <NUM>. For example, the analysis module <NUM> may compare measurements of the footprint centerline length <NUM> from the sensor unit <NUM> over time. Such a comparison may be made using an analytical model, such as a linear regression model or a non-linear regression model. If the footprint centerline length <NUM> remains the same or increases, the tire <NUM> is experiencing uneven wear. When the analysis module <NUM> determines that the tire <NUM> is experiencing uneven wear, the analysis module generates the irregular wear determination <NUM>.

Alternatively, with reference to <FIG>, the analysis module <NUM> may employ an event filter <NUM>, a denormalization filter <NUM>, a time filter <NUM>, and/or a prediction model <NUM>, which may improve the accuracy of the irregular wear determination <NUM>. When the analysis module <NUM> employs an event filter <NUM>, the analysis module <NUM> receives the tire-based data inputs of tire pressure <NUM>, tire temperature <NUM>, footprint centerline length <NUM> and the optional tire ID information <NUM>. The analysis module <NUM> also optionally receives data from a vehicle-mounted collection unit <NUM>. The data from the vehicle-mounted collection unit <NUM> preferably includes vehicle speed <NUM>, which may be calculated from global positioning system (GPS) data or other suitable source of vehicle speed data, and inertial measurements <NUM> for the vehicle <NUM> from an accelerometer.

When the event filter <NUM> is employed, it is applied to the data received from the vehicle-mounted collection unit <NUM>. More particularly, vehicle conditions are reviewed in the event filter, including the measured vehicle speed <NUM> from GPS data and the inertial measurements <NUM>. These measured values are compared to threshold values, including upper and lower limits. If the measured values are outside of the threshold values, the system <NUM> does not proceed, as the vehicle <NUM> is likely to be operating outside of normal or predictable conditions. If the measured values are within the threshold values, the measured data of tire pressure <NUM>, tire temperature <NUM>, footprint centerline length <NUM>, and vehicle speed <NUM> may be sent to a denormalization filter <NUM>.

When the denormalization filter <NUM> is employed, it accounts for the effect of inflation pressure <NUM>, temperature <NUM> and vehicle speed <NUM> on the footprint centerline length <NUM> of the tire <NUM>. In the denormalization filter <NUM>, a pre-trained regression model is used to account for the effects of inflation pressure <NUM>, temperature <NUM> and vehicle speed <NUM>. Regardless of the vehicle and tire operating conditions, the footprint centerline length <NUM> is regressed to a pre-defined nominal condition, that is, a pre-defined inflation pressure <NUM>, temperature <NUM> and vehicle speed <NUM>.

The denormalization filter <NUM> generates a normalized footprint length <NUM>. Because the footprint centerline length <NUM> of the tire <NUM> may also be affected by the vehicle load, it is preferred to account for the effect of load on the normalized footprint length <NUM>. To account for the effect of load on the normalized footprint length <NUM>, an optional historical footprint measurement database <NUM> may be accessed. The historical footprint measurement database <NUM> is in electronic communication with the processor <NUM> and may be stored on the processor, and contains a historical log of footprint measurements <NUM>. The normalized footprint length <NUM> is correlated to the historical log <NUM> and an average of the values is taken.

The average of the values is applied to an optional time filter <NUM>. When the time filter <NUM> is employed, it accounts for time-scale decomposition of the tire <NUM>. More particularly, the time filter <NUM> accounts for bias due to factors or parameters that may affect the tire <NUM> over time, and which are not among the above-described measured parameters. The technique employed in the time filter <NUM> is described in greater detail in <CIT>.

When employed, the time filter <NUM> yields a regularized footprint length <NUM> for the tire <NUM>. In addition, when the analysis module <NUM> employs the event filter <NUM>, the denormalization filter <NUM>, and/or the time filter <NUM>, the regularized footprint length <NUM> is input into a prediction model <NUM> to generate the irregular wear determination <NUM> for the tire <NUM>. The prediction model <NUM> preferably is a non-linear regression model. By way of background, non-linear regression models are a form of regression analysis in which observational data are modeled by a function that is a nonlinear combination of the model parameters, and depends on one or more independent variables. Examples of non-linear regression models that may be employed in the prediction model <NUM> include a Random Forest Regressor, an XgBoost Regressor, and a CatBoost Regressor.

If the regularized footprint length <NUM> remains the same or increases, the prediction model <NUM> determines that the tire <NUM> is experiencing uneven wear. When the prediction model <NUM> determines that the tire <NUM> is experiencing uneven wear, the analysis module <NUM> generates the irregular wear determination <NUM>.

Turning to <FIG>, in order to increase the accuracy of the irregular wear determination <NUM>, the irregular wear detection system <NUM> may employ inputs from additional models. For example, the irregular wear detection system <NUM> may receive an additional input <NUM> from a mileage-based model <NUM>. The technique employed in the mileage-based model <NUM> is described in greater detail in <CIT>. The mileage-based model <NUM> indicates when the vehicle <NUM> has been driven for a high number of miles, which in turn provides an indication that the tire <NUM> has experienced wear. A comparison of the input <NUM> from the mileage-based model <NUM> and the irregular wear determination <NUM> from the analysis module <NUM> may improve the accuracy of the irregular wear detection system <NUM>.

The irregular wear detection system <NUM> may receive another input <NUM> from a frictional-energy model <NUM>. The technique employed in the frictional-energy model <NUM> is described in greater detail in <CIT>. The frictional-energy model <NUM> indicates when the vehicle <NUM> has accumulated high frictional energy, which in turn provides an indication that the tire <NUM> has experienced wear. A comparison of the input <NUM> from the frictional-energy model <NUM> and the irregular wear determination <NUM> from the analysis module <NUM> may improve the accuracy of the irregular wear detection system <NUM>.

The comparison of the irregular wear determination <NUM> from the analysis module <NUM>, the input <NUM> from the mileage-based model <NUM>, and/or the input <NUM> from the frictional-energy model <NUM> preferably is executed in a comparator <NUM>. For example, when the irregular wear determination <NUM> from the analysis module <NUM> exceeds a predetermined irregular wear threshold, and when the input <NUM> from the mileage-based model <NUM> exceeds a predetermined threshold, the comparator <NUM> causes the system <NUM> to generate an irregular wear alert <NUM>. Likewise, when the irregular wear determination <NUM> from the analysis module <NUM> exceeds a predetermined irregular wear threshold, and when the input <NUM> from the frictional-energy model <NUM> exceeds a predetermined threshold, the comparator <NUM> causes the system <NUM> to generate the irregular wear alert <NUM>. In this manner, the additional inputs <NUM> and <NUM> from the mileage-based model <NUM> and the frictional-energy model <NUM>, respectively, may increase the accuracy of the irregular wear determination <NUM> made by the irregular wear detection system <NUM>.

Turning to <FIG>, as another option to increase the accuracy of the irregular wear determination <NUM>, the irregular wear detection system <NUM> may employ a peer-based comparison. More particularly, when multiple vehicles <NUM> with similar platforms employ similar tires <NUM> that are available for analysis, such as vehicles in a fleet <NUM>, a peer comparator <NUM> may be used. Similar tires <NUM> include tires with the same stock keeping unit (SKU) identification, the same product code, and the like. The data for each one of the similar tires <NUM>, including the footprint centerline length <NUM>, the tire pressure <NUM>, the tire temperature <NUM>, and the optional tire ID information <NUM>, are transmitted to the processor <NUM> and the analysis module <NUM> in the manner that is described above.

The analysis module <NUM> analyzes the inputs as described above to generate the regularized footprint length <NUM> for each tire <NUM>. The prediction model <NUM> includes the peer comparator <NUM>, which compares the regularized footprint lengths <NUM> among the tires <NUM> to examine trends. Because the tires <NUM> are similar, the same trends among the regularized footprint lengths <NUM> should occur. When an anomaly <NUM> occurs in the trends of the regularized footprint lengths <NUM>, it is detected by the peer comparator <NUM>. The peer comparator <NUM> then generates a peer-based determination <NUM>, which may include an irregular wear alert. The peer-based determination <NUM> may be more accurate in a vehicle fleet <NUM> than an individual irregular wear determination <NUM>.

Referring to <FIG>, when the irregular wear determination <NUM> is generated for each tire <NUM>, the data may be wirelessly transmitted <NUM> from the processor <NUM> on the vehicle <NUM> to a remote processor, such as a processor in a cloud-based server <NUM>. The irregular wear determination <NUM> may be stored and/or remotely analyzed and may also be wirelessly transmitted <NUM> to a display device <NUM> for a display that is accessible to a user of the vehicle <NUM>, such as a smartphone. Alternatively, the irregular wear determination <NUM> may be wirelessly transmitted <NUM> from the processor <NUM> directly to the display device <NUM>.

As mentioned above, when the irregular wear determination <NUM> exceeds a predetermined irregular wear threshold, an irregular wear alert <NUM> may be transmitted to the display device <NUM>. The irregular wear detection system <NUM> thus may provide notice to a vehicle operator that one or more tires <NUM> are experiencing irregular wear. The notice may include a recommendation to check the inflation pressure of the tire, to check the alignment of the wheel <NUM>, and/or to rotate the tires on the vehicle <NUM> to alleviate the irregular wear.

The irregular wear detection system <NUM> may also transmit or communicate the irregular wear determination <NUM> and/or the irregular wear alert <NUM> to a service center or a fleet manager. Moreover, the irregular wear detection system <NUM> may transmit or communicate the irregular wear determination <NUM> and/or the irregular wear alert <NUM> to an electronic control unit of the vehicle <NUM> and/or a vehicle control system, such as the braking system and/or the suspension system, to increase the performance of such systems.

In this manner, the irregular wear detection system <NUM> of the present invention detects irregular wear of a tire <NUM> wear based upon the footprint centerline length <NUM> of the tire. By analyzing whether the footprint length <NUM> remains the same or increases as the tread <NUM> of the tire <NUM> wears, the system <NUM> accurately and reliably detects irregular wear using easily obtained and accurate parameters. The irregular wear detection system <NUM> of the present invention provides an independent, standalone system that does not need to be integrated into the electronic systems of the vehicle <NUM> to function, including the CAN bus system.

The present invention also includes a method of detecting irregular wear of a tire <NUM>.

Claim 1:
An irregular wear detection system for a tire (<NUM>) supporting a vehicle (<NUM>), the system comprising a sensor unit (<NUM>) being mounted on the tire (<NUM>) and a processor (<NUM>) in electronic communication with the sensor unit (<NUM>), characterized in that:
the sensor unit includes a footprint centerline length measurement sensor to measure a centerline length of a footprint (<NUM>) of the tire;
the processor is configured for receiving a plurality of measured centerline lengths over time; and
an analysis module (<NUM>) is stored on the processor (<NUM>), the analysis module (<NUM>) being configured to receive the measured centerline lengths as inputs and to detect irregular wear of the tire (<NUM>) from the measured footprint centerline lengths;
the analysis module (<NUM>) being further configured to generate an irregular wear determination when the measured footprint centerline lengths remain the same or increase.