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 and/or measure the amount of tread wear experienced by a tire, which indicates the tire wear state. The amount of tread wear is often represented by a remaining tread depth of the 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 and/or measurement 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 measuring 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 impair the operation of 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 measurement 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 some 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 the prior art, one approach to an indirect estimation of the tire wear state has been to obtain a speed of a wheel on which the tire is mounted, which is referred to as a wheel speed signal. In this approach, the tire wear state has been determined from the wheel speed signal by correlating the wear state to a resonance frequency of the wheel speed signal. However, extraction of precise resonance frequencies from wheel speed signals may be challenging. For example, pulse width errors may be present, which are caused by manufacturing errors, wear of wheel speed sensor components, and corrosion of wheel speed sensor components. In addition, vibration disturbances from the engine and the driveline may creep into wheel speed measurements. Such challenges decrease the accuracy of the tire wear state determinations.

As a result, there is a need in the art for a system that accurately and reliably estimates the remaining tread depth on a tire.

<CIT> and <CIT> describe a system and method in accordance with the preamble of claims <NUM> and <NUM>, respectively.

According to an aspect of an exemplary embodiment of the invention, a system for estimation of a depth of a tread of a tire supporting a vehicle is provided. The system includes a processor in electronic communication with an electronic control system of the vehicle. A wheel speed signal processing module is in electronic communication with the processor, receives measured wheel speed signals, and generates processed wheel speed signals from the measured wheel speed signals. A Fast Fourier Transform computation module is in electronic communication with the processor, receives the processed wheel speed signals, and generates a Fast Fourier Transform curve. A summation module is in electronic communication with the processor, selects a predefined range of the Fast Fourier Transform curve, generates a reference curve from the predefined range of the Fast Fourier Transform curve, and determines a sum of residuals between a real-time Fast Fourier Transform curve and the reference curve. A regression model is in electronic communication with the processor and determines an estimate of tire tread depth from the sum of residuals.

"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, used to model complex relationships between inputs and outputs or to find patterns in data.

"CAN bus" or "CAN bus system" is an abbreviation for controller area network system, which is a vehicle bus standard designed to allow microcontrollers and devices to communicate with each other within a vehicle without a host computer.

"Equatorial centerplane" 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" is a continuous channel molded or cut into the tread.

"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.

"Tread" is the portion of the tire that comes into contact with the road.

"Tread depth" is the radial distance measured from the tread surface to the bottom of the grooves.

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

With reference to <FIG>, an exemplary embodiment of the system <NUM> for estimation of tire tread depth of the present invention is presented. With particular reference to <FIG>, the system <NUM> estimates a tread depth <NUM> (<FIG>) of each tire <NUM> supporting a vehicle <NUM>. When the tires <NUM> are disposed in a front position on the vehicle <NUM>, they are referred to as front tires 12a. When the tires <NUM> are disposed in a rear position on the vehicle <NUM>, they are referred to as rear tires 12b. 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. A measure of the wear on the tire <NUM> is the remaining tread depth <NUM>. 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 tire 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, such as tire pressure <NUM> (<FIG>) and/or tire temperature, and may be of any known configuration. It is to be understood that the tire 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>, and/or on the wheel <NUM>. The tire sensor unit <NUM> preferably also includes electronic memory capacity for storing identification (ID) information for each tire <NUM>, known as tire ID information.

Turning to <FIG>, aspects of the system for estimation of tire tread depth <NUM> preferably are executed on a processor <NUM>. The processor <NUM> enables input of parameters and execution of specific techniques, to be described below, which are stored in a suitable storage medium and are in electronic communication with the processor. The processor <NUM> may be mounted on the vehicle <NUM>, may be in communication with an electronic control system <NUM> of the vehicle, such as the vehicle CAN bus system, and/or may be a remote processor in a cloud-based server <NUM>.

Wireless transmission means <NUM>, such as an antenna, may wirelessly send data from sensors that are in electronic communication with the vehicle electronic control system <NUM> to the processor <NUM>. Output from the system <NUM> may be wirelessly transmitted by an antenna <NUM> from the processor <NUM> to a display or controller device <NUM> and/or to the electronic control system <NUM> of the vehicle <NUM>. By way of example, the device <NUM> may include a device that is accessible to a user of the vehicle <NUM> or a technician for the vehicle, such as a smartphone, and/or a device that is accessible to a fleet manager, such as a computer.

Turning to <FIG>, the system for estimation of tire tread depth <NUM> includes a wheel speed signal processing module <NUM>, which is stored on or is in electronic communication with the processor <NUM>. The wheel speed signal processing module <NUM> receives a raw measured wheel speed signal <NUM> from an electronic control system <NUM> of the vehicle <NUM>, such as the vehicle CAN bus. The wheel speed signal processing module <NUM> preferably compiles data sets according to timestamps, which are digital identifications of each time at which a measured wheel speed signal <NUM> is transmitted to the wheel speed signal processing module. The wheel speed signal processing module <NUM> generates processed wheel speed signals <NUM>.

The processed wheel speed signals <NUM> are communicated or transmitted from the wheel speed signal processing module <NUM> to a Fast Fourier Transform computation module <NUM>, which is stored on or is in electronic communication with the processor <NUM>. With additional reference to <FIG>, the Fast Fourier Transform computation module <NUM> generates a Fast Fourier Transform curve <NUM>, which is a plot of frequency <NUM> of the processed wheel speed signals <NUM> versus a Fast Fourier Transform amplitude <NUM> of the processed wheel speed signals. To generate the Fast Fourier Transform curve <NUM>, the Fast Fourier Transform computation module <NUM> converts the processed wheel speed signals <NUM> from their original time domain to a representation in the frequency domain.

Returning to <FIG>, once the Fast Fourier Transform computation module <NUM> generates the Fast Fourier Transform curve <NUM>, a normalization module <NUM> may optionally normalize the Fast Fourier Transform curve. The normalization module <NUM> is stored on or is in electronic communication with the processor <NUM> and accounts for impacts by certain factors upon the Fast Fourier Transform amplitude <NUM>. By way of example, the factors may include the tire inflation pressure <NUM>, a speed <NUM> of the vehicle <NUM>, and a roughness <NUM> of the road over which the vehicle travels. The normalization module <NUM> executes scaling and shift correction to adjust the values of the Fast Fourier Transform amplitude <NUM> to a common scale to account for these factors.

With additional reference to <FIG>, the impact of a change in tire pressure <NUM> on the Fast Fourier Transform curve <NUM> is shown. A lower pressure 40a on the front tires 12a results in a Fast Fourier Transform curve 50a with lower wheel hop acceleration levels for the front tires than for the rear tires 12b. The values of the Fast Fourier Transform amplitude <NUM> are normalized by inputting the inflation pressure <NUM> from the tire sensor unit <NUM>, which enables adjustment of the amplitude to a common pressure scale.

Referring now to <FIG> and <FIG>, the impact of the speed <NUM> of the vehicle <NUM> on the Fast Fourier Transform curve <NUM> is shown. Because acceleration amplitudes scale linearly with speed, as the speed <NUM> of the vehicle <NUM> increases, the amplitude of the Fast Fourier Transform curve increases from a first level 50b to a second level 50c. The values of the Fast Fourier Transform amplitude <NUM> are normalized by employing the vehicle speed <NUM> from an electronic control system <NUM> of the vehicle <NUM>, such as the vehicle CAN bus. Use of the vehicle speed <NUM> thus enables the Fast Fourier Transform amplitudes <NUM> to be scaled according to speed.

The impact of the roughness <NUM> of the road on the Fast Fourier Transform curve <NUM> is shown in <FIG> and <FIG>. When the vehicle <NUM> travels over road with a low roughness 60a, the amplitude of the Fast Fourier Transform curve is at a first level 50d. When the vehicle <NUM> travels over a road with a high roughness 60b, the amplitude of the Fast Fourier Transform curve increases to a second level 50e. The results reflect a large contribution of higher frequencies of the amplitude of the Fast Fourier Transform curve at the second level 50e due to a high road roughness 60b, indicating that the tire dynamics are of greater relevance on rough roads.

To normalize the values of the Fast Fourier Transform amplitude <NUM>, a vertical acceleration <NUM> of the vehicle <NUM> is employed, which may be measured by an accelerometer. Vertical acceleration data <NUM> from the accelerometer may be communicated to the processor <NUM> from a telematics control unit in which the accelerometer is mounted, or from an electronic control system <NUM>, such as the vehicle CAN bus system. The vertical acceleration <NUM> is input into a roughness assessment module <NUM>, which is stored on or is in electronic communication with the processor <NUM> and correlates the vertical acceleration to the road roughness <NUM>. For example, the roughness assessment module <NUM> may include pre-determined values of road roughness <NUM>, which may be indicated by an international roughness index (IRI), and are correlated to certain values of vertical acceleration <NUM>.

The values of the Fast Fourier Transform amplitude <NUM> are normalized by employing the road roughness <NUM> as determined by the roughness assessment module <NUM>. Use of the road roughness <NUM> thus enables the Fast Fourier Transform amplitudes <NUM> to be scaled according to road roughness.

Returning to <FIG> and <FIG>, after any optional normalization of the Fast Fourier Transform curve <NUM> by the normalization module <NUM>, a predefined region or range <NUM> of the curve is selected in a summation module <NUM>. The summation module <NUM> is stored on or is in electronic communication with the processor <NUM>. Preferably, the predefined range <NUM> is determined based on domain knowledge of the Fast Fourier Transform curve <NUM>. For example, the predefined range <NUM> may be the portion of the Fast Fourier Transform curve <NUM> between eighty (<NUM>) Hertz (Hz) and one hundred (<NUM>) Hz, as there may be an expected shift in the torsional mode of the tire <NUM> in this frequency range.

Optionally, an evaluator <NUM>, which is stored on or is in electronic communication with the processor <NUM>, may receive the above-described vehicle speed <NUM>. To ensure an optimum predefined range <NUM> is selected by the summation module <NUM>, the evaluator <NUM> may evaluate the measured vehicle speed <NUM> to determine if the vehicle speed is in a predetermined acceptable range. If the vehicle speed <NUM> is in the predetermined range, operation of the summation module <NUM> is enabled. If the vehicle speed <NUM> is outside of the predetermined range, operation of the summation module <NUM> is suspended.

As shown in <FIG>, once the predefined range <NUM> is selected, the summation module <NUM> generates a fitted reference curve <NUM>. To generate the reference curve <NUM>, the summation module <NUM> preferably fits a polynomial or an exponential function to the Fast Fourier Transform curve <NUM> in the predefined range <NUM> for a nominal or reference condition. Because the system for estimation of tire tread depth <NUM> concerns wear of the tire <NUM>, the nominal or reference condition preferably is for a new tire, which is when the tread <NUM> is unworn and the tread depth <NUM> is at a known or maximum value.

With reference to <FIG> and <FIG>, once the fitted reference curve <NUM> is generated by the summation module <NUM>, a real-time Fast Fourier Transform curve 50f, <NUM>, <NUM> is generated in the same manner as described above for the Fast Fourier Transform curve <NUM>. The summation module <NUM> compares the level of similarity of the predefined range <NUM> of the real-time Fast Fourier Transform curve 50f, <NUM>, <NUM> to the fitted reference curve <NUM> by determining a sum of residuals <NUM>. The sum of residuals <NUM> is a measure of a discrepancy between variable data, which is the real-time Fast Fourier Transform curve 50f, <NUM>, <NUM>, and a model, which is the reference curve <NUM>. A small value for the sum of residuals <NUM> indicates a tight fit of the variable data to the model. For example, a first real-time Fast Fourier Transform curve 50f yields a small sum of residuals 74a when the tire <NUM> is new. When the tire <NUM> is half worn, a second real-time Fast Fourier Transform curve <NUM> yields a larger sum of residuals 74b. When the tire <NUM> is fully worn, a third real-time Fast Fourier Transform curve <NUM> yields an even larger sum of residuals 74c.

Turning now to <FIG> and <FIG>, the sum of residuals <NUM> is employed in a regression model <NUM>, which is stored on or is in electronic communication with the processor <NUM>. The regression model <NUM> preferably employs linear regression to generate a linear relationship <NUM> between the sum of residuals <NUM> as the input variable and the remaining tread depth <NUM> as the response variable. In the linear relationship <NUM>, as the sum of residuals <NUM> increases, the remaining tread depth <NUM> decreases. Thus, when the real-time Fast Fourier Transform curve 50f, <NUM>, <NUM> is used to determine a specific value 74d of the sum of residuals <NUM>, the linear relationship <NUM> enables the regression model <NUM> to determine a specific value 80a for the remaining tire tread depth <NUM>.

The value 80a of the remaining tire tread depth <NUM> may be wirelessly transmitted from the processor <NUM> to the display or controller device <NUM> and/or to the electronic control system <NUM> of the vehicle <NUM>. The device <NUM> may include a device that is accessible to a user of the vehicle <NUM> or a technician for the vehicle, such as a smartphone, and/or a device that is accessible to a fleet manager, such as a computer. In this manner, the estimate of remaining tread depth 80a may be employed by various control systems that are in communication with the electronic control system <NUM> of the vehicle <NUM>, by a user of the vehicle, by a technician, and/or by a fleet manager.

In this manner, the system for estimation of tire tread depth <NUM> accurately and reliably estimates the tread depth 80a that remains on a tire <NUM>. The system <NUM> may execute an estimation of tread depth 80a for the front tires 12a and a separate estimation of tread depth for the rear tires 12b. Instead of extracting and comparing a particular resonance frequency, the system <NUM> estimates tread depth <NUM> using wheel speed signals <NUM> to generate a Fast Fourier Transform curve <NUM>. The system <NUM> further employs a sum of residuals <NUM> between the real-time Fast Fourier Transform curve 50f, <NUM>, <NUM> and a reference curve <NUM> to generate a specific value 80a of remaining tread depth <NUM> on the tire <NUM>. The system <NUM> is repeatable and may be employed across a wide variety of tires.

The present invention also includes a method for estimating the depth <NUM> of the tread <NUM> remaining on a tire <NUM>. The method includes steps in accordance with the description that is presented above and shown in <FIG>.

Claim 1:
A system for estimation of a depth of a tread (<NUM>) of a tire (<NUM>) supporting a vehicle (<NUM>), the system (<NUM>) comprising:
a processor (<NUM>) in electronic communication with an electronic control system (<NUM>) of the vehicle (<NUM>);
a wheel speed signal processing module (<NUM>) in electronic communication with the processor (<NUM>), the wheel speed signal processing module being configured for receiving measured wheel speed signals and for generating processed wheel speed signals (<NUM>) from the measured wheel speed signals;
a Fast Fourier Transform computation module (<NUM>) in electronic communication with the processor (<NUM>), the Fast Fourier Transform computation module being configured for receiving the processed wheel speed signals (<NUM>) and for generating a Fast Fourier Transform curve (<NUM>);
characterized in that the system (<NUM>) further comprises:
a summation module (<NUM>) in electronic communication with the processor (<NUM>), the summation module being configured for selecting a predefined range of the Fast Fourier Transform curve (<NUM>), for generating a reference curve (<NUM>) from the predefined range of the Fast Fourier Transform curve, and for determining a sum of residuals (<NUM>) between a real-time Fast Fourier Transform curve (50f, <NUM>, <NUM>) and the reference curve (<NUM>); and
a regression model (<NUM>) in electronic communication with the processor (<NUM>), the regression model being configured for determining an estimate of tire tread depth (<NUM>) from the sum of residuals (<NUM>).