Patent Publication Number: US-2021181064-A1

Title: Method of estimating tire conditions

Description:
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. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The invention will be described by way of example and with reference to the accompanying drawings, in which: 
         FIG. 1  is a schematic side view of a vehicle with tires that employ an exemplary embodiment of the method of estimating tire conditions of the present invention; 
         FIG. 2  is an enlarged perspective view of a portion of the vehicle and dual-tire configuration shown in  FIG. 1 ; 
         FIG. 3  is a schematic perspective view, partially in section, of a tire and wheel shown  FIG. 1 ; 
         FIG. 4  is a plan view of a portion of a tire and wheel shown in  FIG. 1  mounted on axle; 
         FIG. 5  is a graphical representation showing a shift in vibration frequency with tire wear; 
         FIG. 6  is a general flow diagram showing a time domain signal of tire vibration input into a machine learning algorithm to generate predictions in accordance with exemplary steps of the method of estimating tire conditions of the present invention; 
         FIG. 7  is a schematic representation of an aspect of an optional deep learning model that may be employed in the method of estimating tire conditions of the present invention; 
         FIG. 8  is a schematic representation of an aspect of an optional support vector machine model that may be employed in the method of estimating tire conditions of the present invention; 
         FIG. 9  is a schematic representation of a computing structure that may be employed in the method of estimating tire conditions of the present invention; and 
         FIG. 10  is a flow diagram showing exemplary steps of the method of estimating tire conditions of the present invention. 
     
    
    
     Similar numerals refer to similar parts throughout the drawings. 
     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. 
     “Circumferential” means lines or directions extending along the perimeter of the surface of the annular tread perpendicular to the axial direction. 
     “Equatorial centerplane (CP)” means the plane perpendicular to the tire&#39;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 at  10  and is shown in  FIGS. 1 through 10 . The method of estimating tire conditions  10  attempts 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 to  FIG. 1 , the method  10  is employed to estimate certain conditions, to be described below, of on one or more tires  12  supporting a vehicle  14 . While the vehicle  14  is 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 in  FIG. 1 . 
     With additional reference to  FIG. 2 , the vehicle  14  may include a dual-tire configuration. A dual tire configuration includes a pair of tires  12 A and  12 B mounted adjacent one another on a respective end of an axle  18  ( FIG. 4 ). 
     Turning to  FIG. 3 , the tire  12  includes a pair of bead areas  16 , each one of which is formed with a bead core. Each one of a pair of sidewalls  20  extends radially outwardly from a respective bead area  16  to a ground-contacting tread  22 . The tread  22  is formed with multiple tread elements  24  that are separated by grooves  26  extending in circumferential, lateral and/or angular directions. The tire  12  is reinforced by a carcass  28  that toroidally extends from one bead area  16  to the other bead area, as known to those skilled in the art. An innerliner  30  is formed on the inner or inside surface of the carcass  28 . The tire  10  is mounted on a wheel  32 , as known in the art, and defines a cavity  34  when mounted. Each wheel  32  is rotatably mounted on a respective axle  18  ( FIG. 4 ) in a manner known to those skilled in the art. 
     As shown in  FIGS. 3 and 4 , a first sensor  38  is mounted to the wheel  32 , the tire  12 , an end  36  of the axle  18  inboardly of the wheel, or to a component of the vehicle brake system proximate the tire. The first sensor  38  may be mounted to an outboard or inboard surface of the wheel  32 , to an internal or external surface of the tire  12 , to an internal or external surface of the axle  18 , or to a bracket attached to a disc foundation brake or a cam tube of a drum foundation brake. The first sensor  38  preferably is an accelerometer, which is an electromechanical sensor that measures acceleration forces associated with vibration of the wheel  32  and/or the tire  12 . Preferably, the accelerometer  38  measures at least vertical acceleration of the wheel  32 , which yields vibrational data. More preferably, the accelerometer  38  measures vertical, lateral and longitudinal acceleration of the wheel  32  to yield vibrational data. More than one accelerometer  38  may be employed, with the accelerometers being disposed in different locations on the tire  12 , wheel  32  and/or axle  18 . 
     Optionally, a second sensor  40  is mounted proximate the first sensor  38 . The second sensor may be mounted to the wheel  32 , the tire  12 , the end  36  of the axle  18  inboardly of the wheel, or to a component of the vehicle brake system proximate the tire. The second sensor  40  may be mounted to an outboard or inboard surface of the wheel  32 , to an internal or external surface of the tire  12 , to an internal or external surface of the axle  18 , or to a bracket attached to a disc foundation brake or a cam tube of a drum foundation brake. The second sensor  40  may be mounted to the same surface as the first sensor  38 , or to a different surface that is near the surface on which the first sensor is mounted. 
     The second sensor  40  preferably is an acoustic sensor, which may be a microphone, or other known type of sensor for collecting acoustic signal data of the tire  12  and/or the wheel  32  as they rotate during operation of the vehicle  14 . When the second sensor  40  is employed, the acoustic signal data from the acoustic sensor  40  yields vibrational data that supplements the vibrational data from the accelerometer  38 . 
     The sensors  38  and  40  may be separate units, as shown, or may be integrated into a single unit. In addition, one or both of the sensors  38  and  40  may be integrated into a tire pressure monitoring system (TPMS) sensor, which is a sensor for measuring the temperature and pressure in the tire cavity  34 , and which may be mounted to the innerliner  30  or to another component of the tire  12  or to the wheel  32 . 
     With additional reference to  FIG. 1 , each sensor  38 ,  40  includes means for transmitting the sensed or measured data to a processor  42 . The processor  42  may be a locally disposed processor that is mounted on the vehicle  14 , in which case the transmission means may include a wired connection or a wireless connection  44  between the processor and the sensors  38 ,  40 . The processor  42  and the sensors  38 ,  40  may 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 to  FIG. 9 , the processor  42  may be a remote processor, in which case the transmission means preferably include an antenna electrically connected to each sensor  38 ,  40  for wirelessly transmitting the measured data to the processor. For example, each sensor  38 ,  40  may be wireless connected  46  to a vehicle-mounted transmitter  48 , which is connected to the Internet  50  through a wired or wireless connection  52 . A server  54  is also connected to the Internet  50  through a wired or wireless connection  56 , and includes or is in electronic communication with the processor  42  and storage means  58  to execute the steps of the method of estimating tire conditions  10 . 
     Turning to  FIG. 10 , exemplary steps of the method of estimating tire conditions  10  are shown. The method includes mounting the accelerometer  38  to the wheel  32 , the tire  12 , the axle  18  or to a component of the vehicle brake system proximate the tire, step  100 . When the acoustic sensor  40  is employed, it is mounted to the wheel  32 , the tire  12 , the axle  18  or to a component of the vehicle brake system proximate the tire, step  102 . Each sensor  38 ,  40  collects raw vibrational data, step  104 , and transmits the data to the processor  42  as described above, step  106 . 
     The processor  42  collects the data from the sensors  38 ,  40  and executes an analysis of the data. More particularly, with additional reference to  FIG. 5 , the raw vibrational data  60  from each sensor  38 ,  40  may be processed using a Fast Fourier Transform  62 , step  108 . The Fast Fourier Transform  62  is an algorithm computes the discrete Fourier Transform of a sequence, and is employed to convert the signals from the sensors  38 ,  40  from their original domains to representations in a frequency or time domain. 
     Referring now to  FIGS. 6 and 10 , an example of a resulting time domain signal of tire vibration is indicated at  72 . The vibration data  72  are processed on the processor  42  using a machine learning technique  74  to yield a prediction or estimation  76 , as will be described in greater detail below. To prepare the vibration data  72  for analysis, the data are normalized, step  110 , by subtracting a linear trend and normalizing to unit variance. 
     Once the vibration data  72  have been normalized, a power spectral density (PSD)  78  preferably is calculated, step  112 , as the power spectral density for the data provide improved processing in the machine learning technique  74 . It is to be understood that pre-processing of the vibration data  72  other than by calculation of the PSD  78  may be employed in step  112 . Alternatively, depending on the vibration data  72 , 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 data  78 , with the understanding that step  112  may involve other pre-processing techniques or may not be performed. 
     The machine learning technique  74  includes inputting any PSD data  78  into a machine learning model  80 , step  114 . While a variety of machine learning models  80  may be employed, a first preferred model or technique is a deep learning model  82  and a second preferred model or technique is a support vector machine (SVM) algorithm or model  84 . Deep learning  82  is a machine learning model or technique  80  that excels at analyzing unstructured data, including the vibration data  72  and any corresponding PSD data  78 . Deep learning  82  employs 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 learning  82  in the method of estimating tire conditions  10  is a convolutional neural network (CNN)  86 . The CNN  86  employs a multilayer neural network. The layers of the CNN  86  include 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 CNN  86  is shown in  FIG. 7 , which schematically illustrates layers of the CNN. Input vectors  88  corresponding to the PSD data  78  of the vibration data  72  are fed into to the connected network  90 . The network  90  generates the predictions  76  of tire conditions. In this manner, the CNN  86  is trained with data to provide effective predictions  76 . 
     The support vector machine algorithm (SVM)  84  is an alternative machine learning model or technique  80 . As shown in  FIG. 8 , SVM  84  includes locating a hyperplane  92  that classifies data points  94 . The SVM analysis  84  includes generating predictions  76  of tire conditions from similar data points  94  using the PSD data  78 . 
     Returning to  FIG. 10 , in step  116 , the machine learning model  80  thus generates the predictions  76  of conditions of the tire  12 . A resulting estimation  96  based on the predictions  76  is then output, step  118 . 
     Identification (ID) information for the tire  12  may be provided in a memory unit of one or both of the sensors  38 ,  40  or may be stored in a separate unit, referred to as a tire ID tag. The tire ID information is transmitted to the processor  42  to enable correlation of the tire condition estimation  96  to the specific tire  12 . Such tire identification enables the estimation  96  to be compared to data of historical conditions for the tire  12 , step  120 , to increase the fidelity or accuracy of the method  10 . 
     For example, the storage means  58  ( FIG. 9 ) that are in communication with the processor  42  may include a database that stores estimations  96  of the tread depth of each tire  12  over time. When the machine learning model  80  outputs a new estimation  96 , the new estimation may be compared to the historical data in step  120 . The new estimation  96  is added to historical estimates over a look-back period of time, and a final predicted tread depth  130  is obtained by combining all estimates over the historical period, step  128 . In addition, in step  128 , if new estimation  96  consistently shows a higher tread depth when compared to recent historical data, a conclusion may be drawn that there has been a replacement of the tire  12 . 
     To further increase the fidelity or accuracy of the method  10 , additional inputs  98  may be employed. For example, weather conditions  98 A may be obtained from the Internet  50  ( FIG. 9 ) based on a geographic location of the vehicle  14 , road conditions  98 B 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 speed  98 C of the vehicle may be obtained from a speedometer or a GPS calculation through the CAN bus system. One or more of the additional inputs  98  are provided through the processor  42  to the machine learning model  80 . By taking such additional inputs  98  into account, the accuracy of the estimation  96  and/or the final predicated tread depth  130  generated by the model  80  is further increased. 
     Optionally, the estimation  96  and/or the final predicted tread depth  130  may be classified based on the state of the vehicle  14 , step  124 . For example, the state of the vehicle  14  may be monitored. For example, in step  124 , it may be determined if the vehicle  14  is moving, such as by obtaining a speedometer signal or a GPS calculation through the CAN bus. It may also be determined if the vehicle  14  is 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 estimation  96  and/or the final predicted tread depth  130  according to the additional criteria of the vehicle state, the accuracy of the estimation  96  and/or the final predicted tread depth  130  generated by the model  80  may be further increased. 
     Because the processor  42  may be electrically connected to other systems of the vehicle  14  through the CAN bus as described above, the final predicted tread depth  130  may 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 depth  130  may be compared in the processor  42  to a predetermined limit. If the final predicted tread depth  130  does 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 vehicle  14 , to a hand-held device, such as an operator&#39;s smartphone, and/or to a remote management center. The method  10  thus may provide notice or a recommendation to a vehicle operator or a manager that one or more conditions of each tire  12  does not satisfy the predetermined limit, thereby enabling appropriate action to be taken. 
     Using tread depth as an example of a specific tire condition estimation  96 , as shown in  FIG. 5 , a plot  64  of vibration frequency  66  versus time  68  for tires  12  with diminishing tread depths  70 A,  70 B,  70 C and  70 D indicates a shift in vibration frequency with tire wear or decreasing tread depth. The relationship between vibration frequency  66  and wear of the tread  22  ( FIG. 3 ) may be represented by the following equation: 
     
       
         
           
             ω 
             = 
             
               
                 
                   k 
                   t 
                 
                 
                   m 
                   t 
                 
               
             
           
         
       
     
     Where ω is the vibration frequency, m t  is the mass of the tread  22  and k t  is a time-based constant. For a worn tire  12 , a reduction in the mass of the tread m t  causes an upward shift in vibration frequency ω. 
     Returning to  FIG. 10 , the machine learning model  80  employs the relationship between vibration frequency and tire wear or decreasing tread depth in step  114  to generate predictions  76  of tread depth of the tire  12  in step  116 . A resulting estimation  96  of tread depth is output in step  118 . Additional inputs  98  may be employed in the model  80  in step  122 , and a comparison to historical conditions may be made in step  120 , as well as classification based on the vehicle state in step  124 . The resulting final predicted tread depth  130  thus is an accurate estimate that may be transmitted to the vehicle control systems and/or to the vehicle operator. 
     As described above, the estimation  96  preferably is correlated to tire identification information for each specific tire  12 . Thus, when a vehicle  14  employs a dual-tire configuration with tires  12 A and  12 B as shown in  FIG. 2 , the method of estimating tire conditions  10  may identify a mismatch between the tires. More particularly, in step  126 , a tread depth estimation  96  and/or the final predicted tread depth  130  for the first tire  12 A is compared to a tread depth estimation for the second tire  12 B. If a difference in the estimations  96  and/or the final predicted tread depths  130  exceeds a predetermined threshold, a mismatch notice may be generated and transmitted as described above. For example, if the tread depth estimation  96  yields a difference in tread depth that is greater than about 2/32 of one inch between the first tire  12 A and the second tire  12 B, a tread depth mismatch notice may be generated. 
     The machine learning model  80  employs the relationship between vibration frequency and pressure in step  114  to generate predictions  76  of pressure of the tire  12  in step  116 . A resulting estimation  96  of tire pressure is output in step  118 . Additional inputs  98  may be employed in the model  80  in step  122 , and a comparison to historical conditions may be made in step  120  to obtain a final predicted tread depth  130 , which may be classified based on the vehicle state in step  124 . The resulting final predicted tread depth  130  thus is an accurate estimate that may be transmitted to the vehicle control systems and/or to the vehicle operator. 
     In step  126 , the method of estimating tire conditions  10  may identify a pressure-related mismatch between dual tires  12 A and  12 B. More particularly, in step  126 , a tire pressure estimation  96  for the first tire  12 A is compared to a tire pressure estimation for the second tire  12 B. If a difference in the estimations  96  exceeds a predetermined threshold, a mismatch notice may be generated and transmitted as described above. For example, if the pressure estimation  96  yields a difference that is greater than about 5 pounds per square inch between the first tire  12 A and the second tire  12 B, a pressure mismatch notice may be generated. 
     Optionally, the method of estimating tire conditions  10  may employ the vibrational data from the sensors  38 ,  40  to determine additional conditions of the tire  12 , the wheel  32  and/or the vehicle  14 . For example, the vibrational data from the sensors  38 ,  40  may be processed according to the steps described above to determine potential conditions including crown separation of one or more tires  12 , 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 conditions  10  of the present invention provides estimates 96 of conditions of the tire  12  by collecting vibrational data of the tire and/or the wheel  32  and analyzing the data with a machine learning technique  74 . The method of estimating tire conditions  10  of the present invention accurately and reliably estimates conditions of the tire  12  including tread depth, pressure and dual-tire mismatch. 
     It is to be understood that the method of the above-described tire condition estimation system  10  may 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 system  10  finds application on any type of tire  12 . 
     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.