Patent Description:
Tire wear refers to the loss of material from the tread of the tire as indicated by the depth of the tire tread. Measuring or predicting the wear state of the tire may be beneficial. For example, information about the wear state of a tire may be useful in predicting tire performance during vehicle braking and/or handling and may be used to determine when a tire should be replaced. In addition, the wear rate of the tire, which is the wear of the tire over time, may be useful in estimating tread depth as a function of time for predictions of tire performance and/or tire life.

Techniques have been developed to directly measure tire wear state using sensors that are attached to the tire. Direct techniques include certain advantages, such as relative simplicity in the approach of measurement of pressure, temperature and/or tread depth with a sensor. Direct techniques also include challenges, such as proper sensor mounting without affecting tire integrity, sensor life and/or transmission of sensor data in the harsh environment of a tire.

Due to such challenges, indirect techniques have been developed. Indirect techniques take certain tire and/or vehicle sensor measurements into account and then generate a prediction or estimate of tire state and/or tire wear rate. While indirect techniques do not necessarily encounter the challenges of sensor mounting, sensor life, and/or transmission of sensor data, they include challenges in achieving accuracy and repeatability in the estimation or prediction that is generated. For example, many indirect techniques have experienced disadvantages in the prior art due to a lack of optimum prediction techniques, which in turn reduces the accuracy and/or reliability of the tread wear predictions.

As a result, there is a need in the art for a system that accurately and reliably estimates the wear rate of a tire and generates a forecast for replacement of the tire.

<CIT> describes a system in accordance with the preamble of claim <NUM>.

<CIT> describes a vehicle controller in which replacement time information, i.e. information on a scheduled replacement time of a tire according to wear rate may be stored in advance.

The invention relates to a system in accordance with claim <NUM> and to a method in accordance with claim <NUM> or <NUM> respectively.

According to an aspect of an exemplary embodiment of the invention, a tire replacement system for a tire supporting a vehicle is provided. The system includes a processor in electronic communication with an electronic system of the vehicle, and an electronic memory capacity for storing identification information for the tire. The processor receives identification information for the tire from the electronic memory capacity and vehicle data from the electronic system of the vehicle. A prediction model is in electronic communication with the processor and receives the identification information for the tire and the vehicle data. An identification of a replacement tread depth for the tire is included in the prediction model, and an estimation of remaining available distance for the tire to reach the replacement tread depth is determined by the prediction model. An estimation of remaining available time to reach the replacement tread depth is determined by the prediction model from the estimation of remaining available distance for the tire to reach the replacement tread depth. A residual correction module is in electronic communication with the processor and optimizes the estimation of the remaining available time for the tire to reach the replacement tread depth. A replacement lead time determination is generated by the tire replacement system and corresponds to the estimation of remaining available time for the tire to reach the replacement tread depth. A notification of the replacement lead time is generated by the tire replacement system and is transmitted to at least one of the electronic system of the vehicle, a cloud-based server, and a display device.

"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.

"EBS" is an abbreviation for a vehicle electronic braking system.

"Equatorial Centerplane (CP)" means the plane perpendicular to the tire's axis of rotation 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.

"Groove" means an elongated void area in a tread that may extend circumferentially or laterally about the tread in a straight curved, or zigzag manner.

"Kalman Filter" is a set of mathematical equations that implement a predictor-corrector type estimator that is optimal in the sense that it minimizes the estimated error covariance when some presumed conditions are met.

"Luenberger Observer" is a state observer or estimation model. A "state observer" is a system that provide an estimate of the internal state of a given real system, from measurements of the input and output of the real system. It is typically computer-implemented, and provides the basis of many practical applications.

"MSE" is an abbreviation for mean square error, the error between and a measured signal and an estimated signal which the Kalman filter minimizes.

"PSD" is power spectral density (a technical name synonymous with FFT (fast fourier transform).

"Rib" means a circumferentially extending strip of rubber on the tread which is defined by at least one circumferential groove and either a second such groove or a lateral edge, the strip being laterally undivided by full-depth grooves.

"Sipe" means small slots molded into the tread elements of the tire that subdivide the tread surface and improve traction, sipes are generally narrow in width and close in the tires footprint as opposed to grooves that remain open in the tire's footprint.

"Tread" means a molded rubber component which includes that portion of the tire that comes into contact with the road when the tire is normally inflated and under normal load.

"Tread depth" means a radial distance or dimension between the radially outermost surface of the tread elements and the radially outermost surface of the deepest groove of the tire.

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

Turning now to <FIG>, an exemplary embodiment of the tire replacement system <NUM> of the present invention is presented. With particular reference to <FIG>, the system <NUM> estimates the wear rate and forecasts replacement of each tire <NUM> supporting a vehicle <NUM>. While the vehicle <NUM> is depicted as a passenger car by way of example for the purpose of convenience, the invention is not to be so restricted. The principles of the invention find application in other vehicle categories such as commercial trucks and trailers, off-the-road vehicles, and the like, in which vehicles may be supported by more or fewer tires.

Each tire <NUM> includes a pair of bead areas <NUM> and a pair of sidewalls <NUM>, in which each sidewall extends radially outward from a respective bead area to a ground-contacting tread <NUM>. The tire <NUM> is reinforced by a carcass <NUM> that toroidally extends from one bead area <NUM> to the other bead area, as known to those skilled in the art. An innerliner <NUM> is formed on the inside surface of the carcass <NUM>. The tire <NUM> is mounted on a wheel <NUM> in a manner known to those skilled in the art and, when mounted, forms an internal cavity <NUM> that is filled with a pressurized fluid, such as air.

A sensor unit <NUM> may be attached to the innerliner <NUM> of each tire <NUM> by means such as an adhesive and measures certain parameters or conditions of the tire <NUM>, 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 between layers of the carcass <NUM>, on or in one of the sidewalls <NUM>, on or in the tread <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>), temperature <NUM>, and/or load <NUM>. Preferably the sensor unit <NUM> is a tire pressure monitoring system (TPMS) module or sensor, of a type that is commercially available, and may be of any known configuration. For the purpose of convenience, the sensor unit <NUM> shall be referred to as a TPMS sensor. Each TPMS sensor <NUM> preferably also includes electronic memory capacity for storing identification (ID) information for each tire <NUM>, known as tire ID information <NUM>. 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 <NUM>.

The tire ID information <NUM> may include manufacturing information for the tire <NUM>, such as: the tire type <NUM>, such as a passenger tire, truck tire, trailer tire, steer tire, non-steer tire, and the like; tire model; original tread depth <NUM>; size information, such as rim size <NUM>, width, and outer diameter; manufacturing location; manufacturing date; a treadcap code that includes or correlates to a compound identification; and a mold code that includes or correlates to a tread structure identification. The tire ID information <NUM> may also include a service history or other information to identify specific features and parameters of each tire <NUM>, as well as the location or position <NUM> of the tire on the vehicle <NUM>. In addition, global positioning system (GPS) capability may be included in the TPMS sensor <NUM> and/or the tire ID tag <NUM> to provide location tracking of the tire <NUM> during transport and/or location tracking of the vehicle <NUM> on which the tire is installed.

It is to be understood that the TMPS sensor <NUM> and the tire ID tag <NUM> may be separate units or may be incorporated into a single sensor unit. In addition, other sensors known to those skilled in the art may be employed in the tire <NUM> as integrated or separate units. For the purpose of convenience, reference shall be made to the TMPS sensor <NUM> and the tire ID tag <NUM> as separate units, with the understanding that they may be incorporated into one integrated unit, and that other sensors may be employed.

Turning now to <FIG>, the TMPS sensor <NUM> and the tire ID tag <NUM> each include an antenna for wireless transmission <NUM> of the measured parameters of tire pressure <NUM>, tire temperature <NUM>, and tire load <NUM>, as well as tire ID information <NUM>, to a processor <NUM>. The processor <NUM> may be integrated into the TPMS sensor <NUM> or the tire ID tag <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 TPMS sensor unit <NUM> or the tire ID tag <NUM>.

The processor <NUM> preferably is in electronic communication with an electronic system of the vehicle <NUM>, such as the vehicle CAN bus system <NUM>, which is referred to as the CAN bus, or the vehicle EBS. For the purpose of convenience, reference shall be made to the CAN bus <NUM>, with the understanding that such reference includes other electronic systems of the vehicle <NUM>, such as the vehicle EBS.

Aspects of the tire replacement system <NUM> preferably are executed on the processor <NUM>, which enables input of data from the TMPS sensor <NUM> and the tire ID tag <NUM>, as well as input of data from sensors that are mounted on the vehicle <NUM> and which are in electronic communication with the CAN bus. For example, vehicle-mounted or vehicle-based sensors include sensors that indicate vehicle data such as the vehicle speed <NUM>, vehicle load <NUM>, vehicle distance traveled <NUM> from an odometer, and the like.

Referring to <FIG>, when the tire data, tire ID information, and vehicle data described above are collected and correlated for each tire <NUM>, the data may be wirelessly transmitted <NUM> from the processor <NUM> (<FIG>) on the vehicle <NUM> to a processor in a cloud-based server <NUM>. The data may be stored and/or remotely analyzed on the cloud-based server <NUM>, 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>, a technician, or a fleet manager, such as a smartphone or a computer. Alternatively, the data may be wirelessly transmitted <NUM> from the processor <NUM> on the vehicle <NUM> directly to the display device <NUM>.

Turning to <FIG>, the tire replacement system <NUM> includes transmission of data <NUM> to the processor <NUM>. Preferably, the data <NUM> includes the tire ID information <NUM>, and specifically the original tread depth <NUM>, the rim size <NUM>, the tire type <NUM>, and the tire position <NUM> on the vehicle <NUM>, which are transmitted from the ID tag <NUM> to the processor <NUM>. Alternatively, some or all of the tire ID information <NUM> may be stored on a database that is in electronic communication with the processor <NUM>. The transmitted data <NUM> also includes vehicle data, such as vehicle distance traveled <NUM> from an odometer, which is transmitted from the CAN bus <NUM> to the processor <NUM>.

To increase the accuracy of the tire replacement system <NUM>, the transmitted data <NUM> may include additional data that is transmitted to the processor <NUM>. For example, the tire pressure <NUM>, the tire temperature <NUM>, and/or the tire load <NUM> may be transmitted from the TPMS sensor <NUM> to the processor <NUM>. The vehicle speed <NUM> and/or the vehicle load <NUM> may be transmitted from the CAN bus <NUM> to the processor <NUM>. It is to be understood that other types of information may be included in the transmitted data <NUM>, such as traffic conditions, road conditions, weather, and the like. Each set of transmitted data <NUM> is time-stamped, so that the transmitted data is correlated to a specific time of measurement. In this manner, multiple sets of transmitted data <NUM> may be generated, with each one having a specific time stamp.

The tire replacement system <NUM> includes a prediction model <NUM>, which is stored on or is in electronic communication with the processor <NUM>. The prediction model <NUM> receives the transmitted data <NUM> and generates a remaining available distance <NUM> and remaining available time <NUM> for the tire <NUM> to reach a replacement tread depth <NUM>, as will be described in greater detail below. Preferably, the prediction model <NUM> employs a survival analysis technique, which is a statistical technique that analyzes data inputs, such as the transmitted data <NUM>, to estimate a duration of time remaining until an event occurs for a given component, such as each tire <NUM>.

The survival analysis technique employed by the prediction model <NUM> preferably is a parametric model, which assumes a fixed mathematical form to calculate an output. Residuals, which are differences between observed and predicted values, are then adjusted by a non-parametric model. The results are generated in different forms such as plots, files, and variables in an interactive environment. The survival analysis technique and the non-parametric models take multiple continuous and categorical parameters as input.

Each tire <NUM> includes an original tread depth <NUM>, as shown in <FIG>. With reference to <FIG> and <FIG>, as the tire <NUM> wears, the tread depth decreases and is indicated as a remaining tread depth <NUM>. The remaining tread depth <NUM> may be expressed as a dimension or a percentage of the original tread depth <NUM>. A replacement tread depth <NUM> is identified, which may be a specific dimension or a specific percentage of the original tread depth <NUM>. In <FIG>, the replacement tread depth <NUM> is set at <NUM> percent (%) of the original tread depth <NUM>.

The survival analysis technique in the prediction model <NUM> generates a central decay curve <NUM> as a function of the remaining tread depth <NUM> versus a distance <NUM> traveled by the tire <NUM>. The central decay curve <NUM> represents a typical expected wear rate for the tire <NUM>. An upper decay curve <NUM> may also be generated, which represents a slower wear rate for the tire <NUM>. A lower decay curve <NUM> may further be generated, which represents a faster wear rate for the tire <NUM>.

For the typical expected wear rate <NUM>, an expected travel distance for <NUM> the tire <NUM> to reach the replacement tread depth <NUM> is indicated at <NUM>. For example, when the replacement tread depth <NUM> is set at <NUM>% of the original tread depth <NUM>, the expected distance <NUM> for the tire <NUM> to travel to reach the replacement tread depth is <NUM>,<NUM> kilometers (km). For the slower wear rate <NUM>, an expected distance for the tire <NUM> to reach the replacement tread depth <NUM> is indicated at <NUM>, which is <NUM>,<NUM>. For the faster wear rate <NUM>, an expected distance for the tire <NUM> to reach the replacement tread depth <NUM> is indicated at <NUM>, which is <NUM>,<NUM>.

Since the expected distance <NUM> for the tire <NUM> to reach the replacement tread depth <NUM> has been identified, a remaining available distance <NUM> (<FIG>) that the tire can travel may be generated. The tire ID information <NUM> provides a distance traveled by the vehicle <NUM> when the tire <NUM> was new and at its original tread depth <NUM>. A current vehicle distance traveled <NUM> may be obtained from the odometer. The travel distance <NUM> experienced by the tires <NUM> may be determined by subtracting the travel distance <NUM> of the vehicle <NUM> when the tire <NUM> was new from the current travel distance of the vehicle.

The remaining available distance <NUM> at the typical expected wear rate <NUM> is calculated by subtracting the travel distance experienced by the tire <NUM> from the expected distance <NUM> for the tire to reach the replacement tread depth <NUM> at the typical expected wear rate. The remaining available distance <NUM> at the slower wear rate <NUM> is calculated by subtracting the travel distance experienced by the tire <NUM> from the expected distance <NUM> for the tire to reach the replacement tread depth <NUM> at the slower wear rate. The remaining available distance <NUM> at the faster wear rate <NUM> is calculated by subtracting the travel distance experienced by the tire <NUM> from the expected distance <NUM> for the tire to reach the replacement tread depth <NUM> at the faster wear rate.

As shown in <FIG>, the remaining available distance <NUM> may be converted to remaining available time <NUM> to reach the replacement tread depth <NUM>. An average weekly distance <NUM> traveled by the vehicle <NUM> may be monitored through the vehicle distance traveled <NUM> from the odometer. The remaining available time <NUM> to reach the replacement tread depth <NUM> is determined by dividing the remaining available distance <NUM> by the average weekly distance <NUM>.

In order to provide greater accuracy for the tire replacement system <NUM>, the precision of the decay curves <NUM>, <NUM>, <NUM> may be improved. One approach to improve such precision includes estimation of the tread depth <NUM> using physical parameters of the tire <NUM>. For example, using an equation <NUM> shown in <FIG>, a first function <NUM> expresses an estimation of the tread depth <NUM> as a minimum tread depth <NUM> depending on several parameters, x<NUM>, x<NUM>, x<NUM>. Using an equation <NUM> shown in <FIG>, a second function <NUM> expresses an estimation of the tread depth <NUM> as a maximum tread depth <NUM> depending on the parameters, x<NUM>, x<NUM>, x<NUM>.

In each function <NUM> and <NUM>, the parameters x<NUM>, x<NUM>, x<NUM> include selected available parameters from the transmitted data <NUM>, such as the tire pressure <NUM>, the tire temperature <NUM>, and the tire load <NUM>. The tire travel distance <NUM> is a consistently available parameter according to the determination above. In this manner, the precision of the decay curves <NUM>, <NUM>, <NUM> is improved, as when the travel distance <NUM> of the tire <NUM> is zero (<NUM>), the function <NUM> should return the maximum value <NUM> of the tread depth, which is the original depth <NUM>. When the travel distance <NUM> of the tire <NUM> is an extremely large value, the function <NUM> should return the minimum value <NUM> of the tread depth, which is the replacement tread depth <NUM>.

An equation <NUM> shown in <FIG> is preferred to improve the precision of the precision of the decay curves <NUM>, <NUM>, <NUM> through estimation of the tread depth <NUM>, which is an exponential decay function that includes the limits of equations <NUM> and <NUM>. In equation <NUM>, TD is the remaining tread depth <NUM> to be predicted, preferably in millimeters, TreadDepthoriginal is the original tread depth <NUM> of the tire <NUM>, TreadDepthmin is the replacement tread depth <NUM>, distance is the travel distance <NUM> of the tire, preferably in kilometers, and alpha (α) is a shape parameter that modifies the slope of the decay curves <NUM>, <NUM>, <NUM>.

A modified prediction model <NUM> is shown in <FIG>, and illustrates an adjustment to decay curves <NUM> from the original decay curves <NUM>, <NUM>, <NUM> according to the shape parameter alpha α. An additional modified prediction model <NUM> is shown in <FIG> and illustrates further adjustment to decay curves <NUM> according to the original tread depth <NUM> of the tire <NUM> and the replacement tread depth <NUM>.

To account for as many variables or parameters as possible, the equation <NUM> may be modified to a final equation <NUM> as shown in <FIG>. In the final equation <NUM>, the shape parameter alpha (α) is replaced by a matrix A, and the tire travel distance <NUM> is replaced by a matrix P of parameters. The dot product of matrix A and matrix P may be read as a linear transformation, which is indicated at <NUM> in <FIG>. An example of a linear transformation including specific parameters from the transmitted data <NUM> is indicated at <NUM> in <FIG>. The linear transformation <NUM> includes the travel distance <NUM> of the tire <NUM>, the tire pressure <NUM>, and the tire temperature <NUM>. Additional parameters from the transmitted data <NUM> may be added to continue to increase the precision of the decay curves <NUM>, <NUM>, <NUM> and in turn increase the accuracy of the tire replacement system <NUM>.

Another approach to improve the precision of the decay curves <NUM>, <NUM>, <NUM> to provide greater accuracy for the tire replacement system <NUM> includes estimation of the tread depth <NUM> as a percentage. In this case, the equation <NUM> shown in <FIG> loses its geometric elements and assumes the form of equation <NUM> in <FIG>. In the equation <NUM>, TD is the remaining tread depth <NUM> to be predicted as a percentage, MinTreadDepth% is the replacement tread depth <NUM> of the tire <NUM> expressed as a percentage, distance is the travel distance <NUM> of the tire, preferably in kilometers, and alpha (α) is the shape parameter that modifies the slope of the decay curves <NUM>, <NUM>, <NUM>.

To account for as many variables or parameters as possible, the equation <NUM> may be modified to a final equation <NUM> as shown in <FIG>. In the final equation <NUM>, the shape parameter alpha (α) is replaced by a matrix A, and the tire travel distance <NUM> is replaced by the matrix P of parameters. A modified prediction model <NUM> is shown in <FIG> and illustrates an adjustment to decay curves <NUM> from the original decay curves <NUM>, <NUM>, <NUM> according to the shape parameter alpha α based on the replacement tread depth <NUM> of the tire <NUM> as a percentage.

As shown in <FIG>, the tire replacement system <NUM> preferably includes a residual or error correction module <NUM> to optimize the estimation of the remaining available time <NUM> for the tire <NUM> to reach the replacement tread depth <NUM>. More particularly, the residual correction module <NUM> preferably includes a machine learning model that trains an analysis model, such as a Random Forest Model or a Neural Network Model, to minimize statistical error and thus optimize the estimation of the remaining available time <NUM> for the tire <NUM> to reach the replacement tread depth <NUM>.

The model of the residual correction module <NUM> preferably is trained using about <NUM> percent (%) of the tires <NUM> for which the transmitted data <NUM> is received, while the remaining <NUM>% is used to estimate the remaining available time <NUM> to reach the replacement tread depth <NUM>. Preferably, the division between the <NUM>% and <NUM>% is determined using the tire ID information <NUM> to ensure that tires <NUM> used during the model training are not used to test the model to further optimize the accuracy of the tire replacement system <NUM>.

The metrics used in the model of the residual correction module <NUM> preferably include an adjusted R<NUM>, which is a modified coefficient of determination for the proportion of the variation in the dependent variable that is predicted from the independent variables as adjusted for the number of predictors in the model. The metrics preferably also include mean absolute error (MAE) between paired observations. The adjusted R<NUM> and the MAE are traditional metrics in model error computation.

The metrics in the model of the residual correction module <NUM> preferably also include predetermined percentiles of absolute error, as shown in <FIG>. For example, the metrics preferably include a <NUM>-percentile of absolute error, a <NUM>-percentile of absolute error, and a <NUM>-percentile of absolute error. A central value of prediction <NUM> may not return a perfect match with a real observation, so it is preferable to identify a confidence interval <NUM> around the central value that will include the observed points. The confidence interval <NUM> is a range of estimates defined by a lower bound and upper bound and refers to the level of accuracy in the prediction. The larger the confidence interval <NUM>, the larger the number of points that the interval includes. The objective is to include the largest part of points in the smaller interval of confidence <NUM>.

In order to have a representative metrics, it is preferred to calculate the interval of confidence <NUM> that includes <NUM>%, <NUM>% and <NUM>% of the error. For example, the remaining available time <NUM> for the tire <NUM> to reach the replacement tread depth <NUM> +/interval [<NUM>%] indicates that <NUM>% of the time the tire will reach its end of life in the estimated remaining available time. The confidence intervals <NUM> preferably are determined by calculating the absolute error of each point of the test database and generating a list, and the desired percentile on the list is generated at the previous point. The confidence interval <NUM> can be adjusted to any desired percentage, such as <NUM>%, <NUM>%, and <NUM>%.

It is to be understood that, because the replacement tread depth <NUM> of the tire <NUM> is expressed as a percentage, the error around the central value is also expressed as a percentage of the original tread depth <NUM>. To convert the error to a dimension, the error may be multiplied by the original tread depth <NUM> dimension. For example, if the <NUM>% percentile of the error is <NUM>%, to convert to a dimension, <NUM>% * <NUM> equals <NUM> when the tire <NUM> was at an original tread depth <NUM> of <NUM>. Consequently, <NUM>% of points will be included in a range between the central value provided by the model and <NUM>.

Referring to <FIG> and <FIG>, an optional filter module <NUM> is stored on or is in electronic communication with the processor <NUM>. More particularly, in order to improve the accuracy of the tire replacement system <NUM>, it is beneficial to filter certain data <NUM>. For example, data for certain tires <NUM> may be filtered out, such as tires that do not have recognizable ID information <NUM>, tires that have been retreaded as indicated by the tire ID information, and tires that have experienced significant tread wear before the tire replacement system <NUM> was implemented, such as more than <NUM> millimeters (mm) of tread loss.

The filter module <NUM> preferably also filters out data <NUM> tires <NUM> for which insufficient data points exist, so that the tire replacement system <NUM> may accurately analyze trends in the data transmitted to the processor <NUM>. Moreover, the filter module <NUM> preferably manages outliers in the transmitted data <NUM>. Specifically, to manage outliers, the filter module <NUM> removes individually isolated points or points for which tread depth is anomalous, such as tires <NUM> with no wear after <NUM>,<NUM>, or tires that are fully worn after <NUM>. A preferred technique employed by the filter module <NUM> includes using transmitted data <NUM> for only tires <NUM> with a predetermined wear rate range, such as a wear rate that is greater than <NUM> for each <NUM>,<NUM> in tire traveled distance <NUM> and lower than <NUM> for each <NUM>,<NUM> of tire traveled distance.

As shown in <FIG>, in the filter module <NUM>, a slow wear curve <NUM> and a fast wear curve <NUM> may be determined. An acceptance region <NUM> for data may be generated, which is obtained by shifting the slow wear curve <NUM> up by a predetermined amount, such as about <NUM>%, and by shifting the fast wear curve <NUM> down by a predetermined amount, such as about <NUM>%. Data in the acceptance region <NUM> thus is accepted and employed in the tire replacement system <NUM>.

Returning to <FIG>, after the transmitted data <NUM> is processed by the prediction model <NUM>, the model modifications <NUM>, <NUM>, and/or <NUM> are performed, the error correction is executed in the residual correction module <NUM>, and the data is filtered in the filter module <NUM>, a replacement lead time determination <NUM> is generated by the tire replacement system <NUM>. The replacement lead time determination <NUM> corresponds to the remaining available time <NUM> for the tire <NUM> to reach the replacement tread depth <NUM> at the time of the determination.

A notification <NUM> of the replacement lead time determination <NUM> is generated by the tire replacement system <NUM>, which is transmitted to the CAN bus <NUM> or other vehicle electronic control system, the cloud-based server <NUM>, and/or the display device <NUM>. In this manner, the notification <NUM> of the replacement lead time determination <NUM> is communicated to a user of the vehicle <NUM>, a technician, and/or a fleet manager. The notification <NUM> preferably is sent at a predetermined lead time, such as about three (<NUM>) months before the replacement tread depth <NUM> for a fast-wearing tire <NUM>. The number and frequency of the notifications <NUM> may be adjusted as desired, such as to <NUM> months, <NUM> months, <NUM> months, and <NUM> month before tire <NUM> reaches the replacement tread depth <NUM>.

In this manner, the tire replacement system <NUM> of the present invention accurately and reliably estimates the wear rate of a tire <NUM> and generates a forecast for replacement of the tire. Parameters can be added or suppressed for use in the system <NUM> based on their availability and on the accuracy of the estimation that is generated. The tire replacement system <NUM> focuses on the remaining time available for the tire <NUM> to be used until a replacement tread depth <NUM> is reached. The tire replacement system <NUM> finds application in tires <NUM>, vehicles <NUM>, and fleets of vehicles with different characteristics and uses, such as long haul trucks, regional haul trucks, mixed service trucks, buses, and passenger fleets.

The present invention also includes a method of estimating the wear rate of a tire <NUM> and a method of generating a forecast for replacement of the tire. Each method includes steps in accordance with the description that is presented above and shown in <FIG>.

Claim 1:
A replacement system for a tire (<NUM>) supporting a vehicle (<NUM>), the vehicle including an electronic system (<NUM>), the system (<NUM>) comprising:
an electronic memory capacity for storing identification information for the tire (<NUM>);
a processor (<NUM>) in electronic communication with the electronic system (<NUM>) of the vehicle (<NUM>), the processor being configured for receiving identification information for the tire (<NUM>) from the electronic memory capacity and vehicle data from the electronic system (<NUM>) of the vehicle (<NUM>);
a prediction model in electronic communication with the processor (<NUM>) and being configured for receiving the identification information for the tire (<NUM>) and the vehicle data;
an identification means of a replacement tread depth for the tire (<NUM>) included in the prediction model;
an estimation means of remaining available distance for the tire (<NUM>) to reach the replacement tread depth being determined by the prediction model;
characterized in that the system further comprises
an estimation means of remaining available time to reach the replacement tread depth being determined by the prediction model from the estimation means of remaining available distance for the tire to reach the replacement tread depth;
a residual correction module in electronic communication with the processor to optimize the estimation of the remaining available time for the tire to reach the replacement tread depth;
a replacement lead time determination being generated by the tire replacement system and corresponding to the estimation of remaining available time for the tire (<NUM>) to reach the replacement tread depth; and
a notification of the replacement lead time being generated by the tire replacement system and transmitted to at least one of the electronic system (<NUM>) of the vehicle (<NUM>), a cloud-based server (<NUM>), and a display device (<NUM>).