METHOD OF ESTIMATING TIRE CONDITIONS

A method for estimating a condition of a tire is provided. The tire supports a vehicle and is mounted on a wheel. The wheel is rotatably mounted on an axle. A sensor is mounted on at least one of the tire, the wheel, the axle, and a component of the brake system. Vibrational data is measured with the sensor. The data from the sensor is transmitted to a processor, and the data is processed. The processed data is normalized and at least one of the normalized data and pre-processed data is input into a machine learning model. A condition estimation for the tire is generated, which includes at least one of a tread depth of the tire, a pressure of the tire, and a dual tire mismatch.

FIELD OF THE INVENTION

The invention relates generally to tire monitoring systems. More particularly, the invention relates to systems that predict or estimate conditions of a tire, such as wear and pressure. The invention is directed to a method of estimating conditions of a tire including tread depth or wear state, pressure and dual-tire mismatch by sensing vibrational data and analyzing the data with a machine learning technique.

BACKGROUND OF THE INVENTION

Tires include various conditions that are beneficial to monitor and estimate, particularly as the tires age. Such conditions include tire wear, tire pressure, and mismatch of dual tires.

Tire wear plays an important role in vehicle factors such as safety, reliability, and performance. As the tire wears, the tread and loses material and directly affects such vehicle factors. As a result, it is desirable to monitor and/or measure the tread depth of a tire, which directly correlates to the amount of wear experienced by the tire. It is to be understood that for the purpose of convenience, the term “tread depth” shall be used, which indicates the degree of wear of the tire.

One approach to the monitoring and/or measurement of tread depth has been through the use of sensors disposed in the tire tread, which has been referred to as a direct method or approach. For example, a sensor is embedded in the tread, and as the tread depth decreases with tire wear, electrical properties of the sensor change, such as the electrical resistance. Some prior art techniques correlate the change in electrical properties to a loss of material from the tread, while other techniques correlate the change in electrical properties to a depth of material that remains on the tread. The direct approach to measuring tire depth 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 sensors. In addition, sensor durability can prove to be an issue in meeting the millions of cycles requirement for tires. Moreover, the sensors in a direct measurement approach must be small enough not to cause any uniformity problems as the tire rotates at high speeds. Finally, the 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 depth over the life of the tire, including indirect estimations of the tread depth or 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 depth or wear predictions. For example, many such techniques involve data or information that are not easily obtained or data that are not accurate under all driving conditions.

Regarding tire pressure, pneumatic tires are filled with air to a recommended inflation pressure. However, pneumatic tires are subject to air pressure losses due to puncture by nails and other sharp objects, temperature changes, and/or diffusion of air through the tire itself. Such pressure losses may lead to reduced fuel economy, tire life, and/or tire performance.

Tire pressure monitoring systems (TPMS) have been developed, which are automated systems that alert drivers and/or central systems when the air pressure in the vehicle tires drops below a predetermined level. Such systems often employ sensors in each tire that are expensive. Also, TPMS sensors may be difficult to install and may thus be installed improperly, which leads to inaccurate measurements by the sensors. Moreover, some sensors encounter reduced accuracy and/or reliability, which in turn undesirably reduces the pressure estimations generated by the system.

In addition, certain vehicles, such as heavy-duty vehicles, are equipped with dual tires, in which a pair of tires is mounted on each end of an axle, for a total of four tires on the axle. It is desirable for both tires in each pair to match one another to optimize the life and performance of the tires. For example, the tires should be of the same size, of the same outside diameter, have about the same inflation pressure and/or about the same tread depth. When both tires in each pair are not of the same size, are not of the same outside diameter, do not have about the same inflation pressure or do not have about the same tread depth, a mismatch occurs. Such mismatches are referred to as dual-tire mismatches, and may undesirably reduce the life and/or performance of one or both tires in the pair.

As a result, there is a need in the art for a method that accurately and reliably estimates conditions of a tire including tread depth, pressure and dual-tire mismatch.

SUMMARY OF THE INVENTION

According to an aspect of an exemplary embodiment of the invention, a method for estimating a condition of a tire is provided. The tire supports a vehicle and is mounted on a wheel, which is rotatably mounted on an axle. The method includes the steps of mounting a sensor on at least one of the tire, the wheel, the axle, and a component of the brake system. Vibrational data is measured with the sensor. The data from the sensor is transmitted to a processor. The data is processed in the processor and the processed data is normalized. At least one of the normalized data and pre-processed data is input into a machine learning model. A condition estimation for the tire is generated, which includes at least one of a tread depth of the tire, a pressure of the tire, and a dual tire mismatch.

Definitions

“Axial” and “axially” means lines or directions that are parallel to the axis of rotation of the tire.

“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. CAN bus is a message-based protocol, designed specifically for vehicle applications.

“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.

“Inboard side” means the side of the tire nearest the vehicle when the tire is mounted on a wheel and the wheel is mounted on the vehicle.

“Lateral” means an axial direction.

“Lateral edges” means a line tangent to the axially outermost tread contact patch or footprint of the tire as measured under normal load and tire inflation, the lines being parallel to the equatorial centerplane.

“Net contact area” means the total area of ground contacting tread elements between the lateral edges around the entire circumference of the tread of the tire divided by the gross area of the entire tread between the lateral edges.

“Outboard side” means the side of the tire farthest away from the vehicle when the tire is mounted on a wheel and the wheel is mounted on the vehicle.

“Radial” and “radially” means directions radially toward or away from the axis of rotation of the tire.

“Tread element” or “traction element” means a block element defined by a shape having adjacent grooves.

“Tread Arc Width” means the arc length of the tread of the tire as measured between the lateral edges of the tread.

DETAILED DESCRIPTION OF THE INVENTION

An exemplary embodiment of the method of estimating tire conditions of the present invention is indicated at10and is shown inFIGS. 1 through 10. The method of estimating tire conditions10attempts to overcome the challenges posed by prior art methods that measure tire conditions, including tread depth, pressure and dual-tire mismatch, through direct measurements. As such, the subject method is referred herein as an “indirect” condition estimation method.

With particular reference toFIG. 1, the method10is employed to estimate certain conditions, to be described below, of on one or more tires12supporting a vehicle14. While the vehicle14is depicted as a commercial truck, the invention is not to be so restricted. The principles of the invention find application in other vehicle categories, such as passenger vehicles, off-the-road vehicles and the like, in which vehicles may be supported by more or fewer tires than shown inFIG. 1.

With additional reference toFIG. 2, the vehicle14may include a dual-tire configuration. A dual tire configuration includes a pair of tires12A and12B mounted adjacent one another on a respective end of an axle18(FIG. 4).

Turning toFIG. 3, the tire12includes a pair of bead areas16, each one of which is formed with a bead core. Each one of a pair of sidewalls20extends radially outwardly from a respective bead area16to a ground-contacting tread22. The tread22is formed with multiple tread elements24that are separated by grooves26extending in circumferential, lateral and/or angular directions. The tire12is reinforced by a carcass28that toroidally extends from one bead area16to the other bead area, as known to those skilled in the art. An innerliner30is formed on the inner or inside surface of the carcass28. The tire10is mounted on a wheel32, as known in the art, and defines a cavity34when mounted. Each wheel32is rotatably mounted on a respective axle18(FIG. 4) in a manner known to those skilled in the art.

As shown inFIGS. 3 and 4, a first sensor38is mounted to the wheel32, the tire12, an end36of the axle18inboardly of the wheel, or to a component of the vehicle brake system proximate the tire. The first sensor38may be mounted to an outboard or inboard surface of the wheel32, to an internal or external surface of the tire12, to an internal or external surface of the axle18, or to a bracket attached to a disc foundation brake or a cam tube of a drum foundation brake. The first sensor38preferably is an accelerometer, which is an electromechanical sensor that measures acceleration forces associated with vibration of the wheel32and/or the tire12. Preferably, the accelerometer38measures at least vertical acceleration of the wheel32, which yields vibrational data. More preferably, the accelerometer38measures vertical, lateral and longitudinal acceleration of the wheel32to yield vibrational data. More than one accelerometer38may be employed, with the accelerometers being disposed in different locations on the tire12, wheel32and/or axle18.

Optionally, a second sensor40is mounted proximate the first sensor38. The second sensor may be mounted to the wheel32, the tire12, the end36of the axle18inboardly of the wheel, or to a component of the vehicle brake system proximate the tire. The second sensor40may be mounted to an outboard or inboard surface of the wheel32, to an internal or external surface of the tire12, to an internal or external surface of the axle18, or to a bracket attached to a disc foundation brake or a cam tube of a drum foundation brake. The second sensor40may be mounted to the same surface as the first sensor38, or to a different surface that is near the surface on which the first sensor is mounted.

The second sensor40preferably is an acoustic sensor, which may be a microphone, or other known type of sensor for collecting acoustic signal data of the tire12and/or the wheel32as they rotate during operation of the vehicle14. When the second sensor40is employed, the acoustic signal data from the acoustic sensor40yields vibrational data that supplements the vibrational data from the accelerometer38.

The sensors38and40may be separate units, as shown, or may be integrated into a single unit. In addition, one or both of the sensors38and40may be integrated into a tire pressure monitoring system (TPMS) sensor, which is a sensor for measuring the temperature and pressure in the tire cavity34, and which may be mounted to the innerliner30or to another component of the tire12or to the wheel32.

With additional reference toFIG. 1, each sensor38,40includes means for transmitting the sensed or measured data to a processor42. The processor42may be a locally disposed processor that is mounted on the vehicle14, in which case the transmission means may include a wired connection or a wireless connection44between the processor and the sensors38,40. The processor42and the sensors38,40may also be electrically connected to an electronic control system of the vehicle, such as the vehicle CAN bus, which enables communication between the sensors and the processor.

Referring toFIG. 9, the processor42may be a remote processor, in which case the transmission means preferably include an antenna electrically connected to each sensor38,40for wirelessly transmitting the measured data to the processor. For example, each sensor38,40may be wireless connected46to a vehicle-mounted transmitter48, which is connected to the Internet50through a wired or wireless connection52. A server54is also connected to the Internet50through a wired or wireless connection56, and includes or is in electronic communication with the processor42and storage means58to execute the steps of the method of estimating tire conditions10.

Turning toFIG. 10, exemplary steps of the method of estimating tire conditions10are shown. The method includes mounting the accelerometer38to the wheel32, the tire12, the axle18or to a component of the vehicle brake system proximate the tire, step100. When the acoustic sensor40is employed, it is mounted to the wheel32, the tire12, the axle18or to a component of the vehicle brake system proximate the tire, step102. Each sensor38,40collects raw vibrational data, step104, and transmits the data to the processor42as described above, step106.

The processor42collects the data from the sensors38,40and executes an analysis of the data. More particularly, with additional reference toFIG. 5, the raw vibrational data60from each sensor38,40may be processed using a Fast Fourier Transform62, step108. The Fast Fourier Transform62is an algorithm computes the discrete Fourier Transform of a sequence, and is employed to convert the signals from the sensors38,40from their original domains to representations in a frequency or time domain.

Referring now toFIGS. 6 and 10, an example of a resulting time domain signal of tire vibration is indicated at72. The vibration data72are processed on the processor42using a machine learning technique74to yield a prediction or estimation76, as will be described in greater detail below. To prepare the vibration data72for analysis, the data are normalized, step110, by subtracting a linear trend and normalizing to unit variance.

Once the vibration data72have been normalized, a power spectral density (PSD)78preferably is calculated, step112, as the power spectral density for the data provide improved processing in the machine learning technique74. It is to be understood that pre-processing of the vibration data72other than by calculation of the PSD78may be employed in step112. Alternatively, depending on the vibration data72, no pre-processing may be necessary and thus would not be employed. For the purpose of convenience, reference shall be made to the use of PSD data78, with the understanding that step112may involve other pre-processing techniques or may not be performed.

The machine learning technique74includes inputting any PSD data78into a machine learning model80, step114. While a variety of machine learning models80may be employed, a first preferred model or technique is a deep learning model82and a second preferred model or technique is a support vector machine (SVM) algorithm or model84. Deep learning82is a machine learning model or technique80that excels at analyzing unstructured data, including the vibration data72and any corresponding PSD data78. Deep learning82employs algorithms that combine feature construction, modeling, and prediction into a single end-to-end system, and thus reduces unstructured data to an information-dense representation that is optimized for prediction.

A preferred technique for deep learning82in the method of estimating tire conditions10is a convolutional neural network (CNN)86. The CNN86employs a multilayer neural network. The layers of the CNN86include an input layer, an output layer, and a hidden layer that includes multiple convolutional layers, pooling layers, fully connected layers and normalization layers. An example of an aspect of the CNN86is shown inFIG. 7, which schematically illustrates layers of the CNN. Input vectors88corresponding to the PSD data78of the vibration data72are fed into to the connected network90. The network90generates the predictions76of tire conditions. In this manner, the CNN86is trained with data to provide effective predictions76.

The support vector machine algorithm (SVM)84is an alternative machine learning model or technique80. As shown inFIG. 8, SVM84includes locating a hyperplane92that classifies data points94. The SVM analysis84includes generating predictions76of tire conditions from similar data points94using the PSD data78.

Returning toFIG. 10, in step116, the machine learning model80thus generates the predictions76of conditions of the tire12. A resulting estimation96based on the predictions76is then output, step118.

Identification (ID) information for the tire12may be provided in a memory unit of one or both of the sensors38,40or may be stored in a separate unit, referred to as a tire ID tag. The tire ID information is transmitted to the processor42to enable correlation of the tire condition estimation96to the specific tire12. Such tire identification enables the estimation96to be compared to data of historical conditions for the tire12, step120, to increase the fidelity or accuracy of the method10.

For example, the storage means58(FIG. 9) that are in communication with the processor42may include a database that stores estimations96of the tread depth of each tire12over time. When the machine learning model80outputs a new estimation96, the new estimation may be compared to the historical data in step120. The new estimation96is added to historical estimates over a look-back period of time, and a final predicted tread depth130is obtained by combining all estimates over the historical period, step128. In addition, in step128, if new estimation96consistently shows a higher tread depth when compared to recent historical data, a conclusion may be drawn that there has been a replacement of the tire12.

To further increase the fidelity or accuracy of the method10, additional inputs98may be employed. For example, weather conditions98A may be obtained from the Internet50(FIG. 9) based on a geographic location of the vehicle14, road conditions98B may be obtained from the Internet based on the geographic location of the vehicle using a global positioning system (GPS) or from a road friction estimation calculator as known to those skilled in the art, and/or a speed98C of the vehicle may be obtained from a speedometer or a GPS calculation through the CAN bus system. One or more of the additional inputs98are provided through the processor42to the machine learning model80. By taking such additional inputs98into account, the accuracy of the estimation96and/or the final predicated tread depth130generated by the model80is further increased.

Optionally, the estimation96and/or the final predicted tread depth130may be classified based on the state of the vehicle14, step124. For example, the state of the vehicle14may be monitored. For example, in step124, it may be determined if the vehicle14is moving, such as by obtaining a speedometer signal or a GPS calculation through the CAN bus. It may also be determined if the vehicle14is stationary and idling, or is stationary and running on its internal power unit, such as by obtaining engine engagement and brake engagement signals through the CAN bus. By classifying the estimation96and/or the final predicted tread depth130according to the additional criteria of the vehicle state, the accuracy of the estimation96and/or the final predicted tread depth130generated by the model80may be further increased.

Because the processor42may be electrically connected to other systems of the vehicle14through the CAN bus as described above, the final predicted tread depth130may be communicated to other control systems of the vehicle, such as an anti-lock braking system (ABS) and/or an electronic stability control system (ESC), to improve performance of such systems.

In addition, each final predicted tread depth130may be compared in the processor42to a predetermined limit. If the final predicted tread depth130does not satisfy the predetermined limit, a notice may be transmitted through the CAN bus or other control system to a display that is visible to an operator of the vehicle14, to a hand-held device, such as an operator's smartphone, and/or to a remote management center. The method10thus may provide notice or a recommendation to a vehicle operator or a manager that one or more conditions of each tire12does not satisfy the predetermined limit, thereby enabling appropriate action to be taken.

Using tread depth as an example of a specific tire condition estimation96, as shown inFIG. 5, a plot64of vibration frequency66versus time68for tires12with diminishing tread depths70A,70B,70C and70D indicates a shift in vibration frequency with tire wear or decreasing tread depth. The relationship between vibration frequency66and wear of the tread22(FIG. 3) may be represented by the following equation:

Where ω is the vibration frequency, mtis the mass of the tread22and ktis a time-based constant. For a worn tire12, a reduction in the mass of the tread mtcauses an upward shift in vibration frequency ω.

Returning toFIG. 10, the machine learning model80employs the relationship between vibration frequency and tire wear or decreasing tread depth in step114to generate predictions76of tread depth of the tire12in step116. A resulting estimation96of tread depth is output in step118. Additional inputs98may be employed in the model80in step122, and a comparison to historical conditions may be made in step120, as well as classification based on the vehicle state in step124. The resulting final predicted tread depth130thus is an accurate estimate that may be transmitted to the vehicle control systems and/or to the vehicle operator.

As described above, the estimation96preferably is correlated to tire identification information for each specific tire12. Thus, when a vehicle14employs a dual-tire configuration with tires12A and12B as shown inFIG. 2, the method of estimating tire conditions10may identify a mismatch between the tires. More particularly, in step126, a tread depth estimation96and/or the final predicted tread depth130for the first tire12A is compared to a tread depth estimation for the second tire12B. If a difference in the estimations96and/or the final predicted tread depths130exceeds a predetermined threshold, a mismatch notice may be generated and transmitted as described above. For example, if the tread depth estimation96yields a difference in tread depth that is greater than about 2/32 of one inch between the first tire12A and the second tire12B, a tread depth mismatch notice may be generated.

The machine learning model80employs the relationship between vibration frequency and pressure in step114to generate predictions76of pressure of the tire12in step116. A resulting estimation96of tire pressure is output in step118. Additional inputs98may be employed in the model80in step122, and a comparison to historical conditions may be made in step120to obtain a final predicted tread depth130, which may be classified based on the vehicle state in step124. The resulting final predicted tread depth130thus is an accurate estimate that may be transmitted to the vehicle control systems and/or to the vehicle operator.

In step126, the method of estimating tire conditions10may identify a pressure-related mismatch between dual tires12A and12B. More particularly, in step126, a tire pressure estimation96for the first tire12A is compared to a tire pressure estimation for the second tire12B. If a difference in the estimations96exceeds a predetermined threshold, a mismatch notice may be generated and transmitted as described above. For example, if the pressure estimation96yields a difference that is greater than about 5 pounds per square inch between the first tire12A and the second tire12B, a pressure mismatch notice may be generated.

Optionally, the method of estimating tire conditions10may employ the vibrational data from the sensors38,40to determine additional conditions of the tire12, the wheel32and/or the vehicle14. For example, the vibrational data from the sensors38,40may be processed according to the steps described above to determine potential conditions including crown separation of one or more tires12, irregular tire wear, flatspotting of the tires, imbalance of the wheels and/or tires, and/or potential brake component issues.

In this manner, the method of estimating tire conditions10of the present invention provides estimates 96 of conditions of the tire12by collecting vibrational data of the tire and/or the wheel32and analyzing the data with a machine learning technique74. The method of estimating tire conditions10of the present invention accurately and reliably estimates conditions of the tire12including tread depth, pressure and dual-tire mismatch.

It is to be understood that the method of the above-described tire condition estimation system10may be altered or rearranged, or components or steps known to those skilled in the art omitted or added, without affecting the overall concept or operation of the invention. For example, the tire condition estimation system10finds application on any type of tire12.

The invention has been described with reference to a preferred embodiment. Potential modifications and alterations will occur to others upon a reading and understanding of this description. It is to be understood that all such modifications and alterations are included in the scope of the invention as set forth in the appended claims, or the equivalents thereof.