Method for measuring tire wear using vehicle speed

Abstract

A method for determining tread remaining on a tire is provided in which first and second speed data of the vehicle is obtained, the second speed data being derived in part from measurement of a frequency of rotation of the tire. The first speed data is filtered to obtain filtered first speed data that is extracted over a first predefined speed limit, and the second speed data is filtered such that filtered second speed data is extracted over a second predefined speed limit. A ratio using the filtered first speed data and the filtered second speed data is calculated. Further, the method involves comparing the ratio to a tire model that links ratio to tread remaining in order to determine the tread remaining on the tire.

Description

METHOD FOR MEASURING TIRE WEAR USING VEHICLE SPEEDSTATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0001] This invention was made with government support under the Advanced Manufacturing Office Award Number DE-EE0007897 awarded by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE). The government has certain rights in the invention.FIELD OF THE INVENTION

[0002] The subject matter of the present invention relates to a method of measuring tread wear of a tire without requiring the user to make physical measurements of the tire with a tread depth gauge. More particularly, the present application involves a tread depth measurement method that utilizes first and second speed data of the vehicle that results in a calculated ratio that is used to determine the tread remaining.BACKGROUND OF THE INVENTION

[0003] Vehicle tires include tread on their exterior surface that engages the ground and wears through normal use. The tread pattern usually includes within it a tread bar that once the tread wears down to it, lets the owner know that the tread is worn and is in need of replacement. However, in order to manage the life of the tire it is desirable to know how much tread is left on the tire at points in time before it has reached its end of life. To evaluate the amount of tread remaining on the tire for replacement purposes, owners may measure the depth of the tread with a tread depth gauge. This instrument is placed into the groove of the tread and provides a mechanical or electrical read out to the owner of the amount of tread remaining. In order to make these measurements, the user must have the instrument on hand, take the time to make the measurement, and make the measurement accurately. This amount of remembering and labor makes the job of tracking tire wear difficult. This can be especially troublesome for the managers of fleets of vehicles, such as tractor trailers or taxi cabs, that have a large number of tires that have to be monitored.

[0004] One known way to measure the amount of tread remaining on a tire is through the use of systems that measure the tread and then report their findings to the user either at the point of measurement or remotely via the internet or some electronic means. These types ofsystems require a camera to evaluate the tread, or have a measurement device over which the tire and vehicle is driven. The ability to transmit tread depth data on tires to a remote server that a fleet manager can access is beneficial in managing the tires of a plurality of vehicles. However, these systems require the vehicle to be driven to a specific location where the vehicle and / or tires are identified and measured, and it may not always be possible to get the vehicle to that specific location for measurements.

[0005] A tread depth measurement system is disclosed in United States Patent No.11.590.807 which is owned by the Applicant of the present application and is incorporated by reference herein in its entirety for all purposes. This system detects the wear of tire tread by using the rolling radius of the tire to establish a wear profile, but does not use speeds of the tire in its process if the speeds are above a certain threshold. Although methods of measuring tire wear are known, there remains room for variation and improvement within the art.BRIEF DESCRIPTION OF THE DRAWINGS

[0006] A full and enabling disclosure of the present invention, including the best mode thereof, directed to one of ordinary skill in the art, is set forth in the specification, which makes reference to the appended figures, in which:

[0007] Fig. 1 is a schematic side view of an 18 wheeler.

[0008] Fig. 2 is a schematic view of a wheel of the 18 wheeler on the ground.

[0009] Fig. 3 is a graph of first and second speed data.

[0010] Fig. 4 is a graph of the first and second speed data of Fig. 3 after filtering.

[0011] Fig. 5 is a graph of frequency of appearance of the ratios of the measured first and second speed data.

[0012] Fig. 6 is a graph of the data of Fig. 5 after being approximated to a Gaussian function.

[0013] Fig. 7 is a graph of the Fig. 5 data but also with data of tires of three different tread depth levels being displayed on the graph.

[0014] Fig. 8 is a chart of the four tires measured in Fig. 7 that provides their mean, standard deviation, and tread depth.

[0015] Fig. 9 is a graph of tire model developed from the data of Fig. 7 and 8 in which the mean wheel speed ratios are plotted against tread depth.

[0016] Fig. 10 is a graph of the data of Fig. 9 with a confidence interval added.

[0017] Fig. 11 is a graph of measured first and second speed data that is offset from one another on the x-axis which are the time

[0018] Fig. 12 is a graph of the data of Fig. 11 with the second speed data shifted on the x-axis time to put it in sync with the first speed data.

[0019] Fig. 13 is a graph of the ratio of the data of Fig. 12 show n as a histogram and with a Gaussian function applied.

[0020] Fig. 14 is a graph of the mean of the data found in Fig. 13 of the tread depth taken over multiple points in time, and with actual inspection data shown.

[0021] Fig. 15 is a graph of tread depth over time of a tread different from that in Fig. 14 that show s replacement of the tire at a particular point in time.

[0022] Fig. 16 is a graph of tread depth over time of a tread different from that of previous figures in which the estimated tread depth does not match inspection tread depth data.

[0023] Fig. 17 is a graph of the data of Fig. 16 with a correction factor applied so that the estimated tread depth from the method matches the inspected tread depth data.

[0024] Fig. 18 is a graph of tread depth data over time with a linear regression applied to the last 20 data points to estimate a replacement date.

[0025] Fig. 19 is a graph of the data of Fig. 18 with a linear regression applied to a different data set from that of Fig. 18 to estimate a replacement date.

[0026] Fig. 20 is a graph of the data of Fig. 18 with linear regression applied to multiple data sets to arrive at a plurality of estimate replacement dates.

[0027] Fig. 21 is a graph of the data of Fig. 20 w ith a histogram of the plurality of estimated replacement dates.

[0028] The use of identical or similar reference numerals in different figures denotes identical or similar features.DETAILED DESCRIPTION OF THE INVENTION

[0029] Reference will now be made in detail to embodiments of the invention, one or more examples of which are illustrated in the drawings. Each example is provided by way of explanation of the invention, and not meant as a limitation of the invention. For example, features illustrated or described as part of one embodiment can be used with another embodiment to yield still a third embodiment. It is intended that the present invention include these and other modifications and variations.

[0030] The present invention provides for a method of determining the wear on a tire 10 so that the amount of tread 12 remaining on the tire 10 can be ascertained. The method uses speed data of the vehicle 20 to calculate a ratio which is then compared to a tire model in orderto calculate tire 10 wear. The system utilizes first speed data of the tire 10 and second speed data of the tire 10, which are two different measurements of tire 10 speed, to make the ratio. A user can thus be informed of the tread 12 remaining on the tire 10 as it wears without having to make a physical measurement of the tread 12 with the use of a tread depth gauge.

[0031] Fig. 1 shows a vehicle 20 that in this instance is an 18 wheeler tractor trailer. The vehicle 20 includes steer tires, drive tires, and trailer tires. One of the steer tires is denoted as tire 10 and is the tire of the vehicle 20 located at the front end of the tractor which can be turned to cause the vehicle 20 to change directions. The vehicle 20 has a GPS (global positioning system) element 24 that can provide location and speed data of the vehicle 20. In this regard, as the vehicle 20 travels forward the GPS element 24 may measure the traveling speed 18 of the vehicle 20 and communicate the traveling speed 18 to the operator and / or a processor 28 that is carried on the vehicle 20. The GPS element 24 measured traveling speed 18 is referred to as first speed data. The processor 28 can obtain information from the vehicle 20 and may manipulate this information as needed. The processor 28 is in communication with a communication link 30 of the vehicle 20 which may in turn communicate data from the vehicle 20 to a remote location and / or obtain information from the remote location and convey it to the processor 28. The tread 12 wear calculations may be executed on the processor 28 and thus onboard the vehicle 20, or can be executed on a computer remote from the vehicle 20 and sent if desired back to the vehicle 20, or can be executed both at the processor 28 on the vehicle 20 and at a location remote from the vehicle 20.

[0032] The system will also need to obtain second speed data of the vehicle 20. This second speed data will be obtained using a different technique than the first speed data. The vehicle 20 includes a controller area network, CAN bus 26 which is a vehicle bus standard to allow microcontrollers and devices to communicate with each other’s applications without a host computer. The CAN bus 26 data can be obtained from various subsystems of the vehicle 20 such as the engine, automatic start / stop system, parking assistance system, collision avoidance system, and braking system. Fig. 2 shows a tire 10 positioned on the ground 14. The tire 10 includes tread 12 that is in engagement with the ground 14, and the rolling radius 16 (R) of the tire 10 is defined as the length from the center axis of the tire 10 to the outer radial edge of the tread 12 that is in contact with the ground 14. The rotational frequency 22 (Q) is the number of times the tire 10 rotates per unit of time, which can be measured in rotations per second, rotations per minute, or rotations per hour.

[0033] The CAN bus 26 can measure the rotational frequency 22 and if the rolling radius 16 (R) is known, the traveling speed 18 (V) of the tire 10 can be calculated using the following equation where R is the rotational frequency 22 and is the rotational frequency 22:

[0034] V = R X Q

[0035] The traveling speed 18 is thus dependent upon the rotational frequency 22 and the rolling radius 16 such that if the same traveling speed 18 is desired upon reducing the rolling radius 16 through wear of the tread 12, the rotational frequency 22 must move up in a linear relationship. It is therefore the case that if the rotational frequency 22 and traveling speed 18 are known, the rolling radius 16 of the tire 10 can be calculated. As an example, if the tire 10 has a rolling radius 16 of 0.00028575 km, and the CAN bus 26 reports a rotational frequency 22 of the tire 10 of 403,499.56255 revolutions per hour, then the traveling speed 18 can be calculated as follows:

[0036] V = 0.00028575 km X 403,499.56255 km / hr = 115.3 km / hr

[0037] The CAN bus 26 data may thus provide the second speed data of the vehicle 20 which is the traveling speed 18 of the tire 10. The system will then have a first speed data from the GPS 24, and a second speed data from the CAN bus 26. However, it is to be understood that other measurements of the traveling speed 18 to arrive at a first speed data and a second speed data that are not those disclosed are possible. In this regard, the methods used to obtain the first speed data and the second speed data can be any method so long as at least one of them incorporates knowledge of a rolling radius 16, diameter, or other size parameter of the tire 10. In some embodiments, the CAN bus 26 data that is used in the vehicle 20 comes from the anti-lock braking system which broadcasts a traveling speed of each wheel through sensing pulses at each w heel from a sensor that detects revolutions of each w heel. The rolling radius 16 that this subsystem uses is a predefined rolling radius 16 that comes from a predefined tire size attributed to the vehicle 20. Since this remains constant in the system, if one were to compare the traveling speed 18 from the GPS 24 to the traveling speed 18 from the CAN bus 26, the evolution of the rolling radius 16 should be observable.

[0038] In accordance with an experiment carried out to demonstrate the method, a vehicle 20 that is a 2018 Freightliner Cascadia ® was fully loaded to result in loading of 2753 kg on the right hand side steer tire. The vehicle 20 was equipped with a telematics kit to record the GPS data 24 and the CAN bus 26 data. The tire 10 is a steer tire on the right hand side that is a 275 / 80R22.5 Michelin XLEZ ® inflated to a pressure of 120 psi. The vehicle 20 was run and speed data collected as shown with the graph in Fig. 3 of speed in KPH taken every 1 / 100thof a second. As shown, the two different speed calculations yield different traveling speeds 18 of the vehicle 20 such that the CAN bus 26 data designated as the second speed data shows the vehicle 20 traveling at a faster speed than the first speed data which is the GPS data 24. With reference to scale, the difference in traveling speed 18 calculation between the first and second speed data sets is less than 2 KPH difference when running the vehicle 20 faster than 100 KPH. The tire 10 is a worn tire 10 at the end of its tread life such that 0% tread 12 is remaining on the tire 10.

[0039] The method may filter the first and second speed data so that only first speed data that is over a first predefined speed limit is used, and so that only second speed data that is over a second predefined speed limit is used. In one embodiment, the first and second speed limits are the same and are each 100 KPH. In this manner, only speeds above the first and second speed limits are used in the method while those that are below these limits are discarded. If the filtering results in one of the first or second speed data to be above its predefined limit and the other below at a particular time, then both the first and second speed data at this time instance are discarded. As such, for the method to use the data, both the first and second speed data at a particular instance in time must both be above their respective first and second speed limits. In some embodiments, the predefined first speed limit is 85 KPH, 90 KPH, 100 KPH, 1 2 KPH, 105 KPH, 1 7 KPH, 1 10 KPH, 1 1 1 KPH, or 1 13 KPH. In some embodiments, the predefined second speed limits is 85 KPH, 90 KPH, 100 KPH, 102 KPH, 105 KPH, 107 KPH, 110 KPH, 111 KPH, or 113 KPH. The first and second predefined speed limits can be different from one another, or may be the same as one another.

[0040] As another filtering step of the method, the first and second speed data after being filtered via the first and second predefined speed limits can be compared to one another to ensure they are close enough to one another. In this regard, if the filtered first and second speed data at the same time stamp are within 5 KPH or less of one another, they are kept. If the filtered first and second speed data are over 5 KPH different from one another then this data is discarded by the method. This closeness may be 3 KPH or less, 2 KPH or less, or 1 KPH or less in other embodiments. The closeness of the data is compared between the first and second data at the same point in time.

[0041] As a third filtering of the data, acceleration can be calculated so that only first and second data that both has for the same point in time and acceleration that is -0.1 m / s2< acceleration < 0. 1 m / s2. To calculate the acceleration, the equation acceleration = A speed / A time can be used. In one example, a speed of the vehicle 20 is measured as being 115.500km / hr (32.0833 m / s) at a time of 400 / 100 seconds (4 seconds), and its subsequent measurement is 115.501 km / hr (32.0836 m / s) at a time of 401 / 100 seconds (4.01 seconds). The acceleration of the vehicle 20 at this point is thus (32.0836 m / s - 32.0833 m / s) / (4.01 s - 4.00 s) = (0.0003 m / s) I (0.01 s) = 0.03 m / s2. Since the acceleration of the vehicle 20 at this point is within the acceptable range, the data can be used. The method may seek to keep the acceleration within a particular smaller range so that values indicative of braking events or other larger acceleration or deceleration events are discarded. The filtering by acceleration can be applied to the first speed data and the second speed data and if either one of these two calculations at the same points in time are outside the acceleration range, both are thrown out and not used by the method. In other embodiments, the method does not use acceleration at all as a filtering criteria.

[0042] If the data in Fig. 3 is filtered using the above described criteria (in one instance filtering so that speeds above 100 KPH are used, and closeness less than 5 KPH is used, and accelerations between -0. 1 m / s2and 0. 1 m / s2are used) it can be seen that all of the data of the first and second speed data are used. In this regard, the speeds of all of the first and second speed data in Fig. 3 are above 100 KPH. Further, the difference in speed between the first and second speed data is around 1 or 2 KPH and all of the differences at each point in time are below 5 KPH. Finally, the acceleration data is mostly within the listed range so that most of this data is used if the acceleration is in fact used as a filtering criteria. The acceleration can be calculated as described above.

[0043] The resulting data after filtering in Fig. 3 is noisy, so a low pass filter can be applied to smooth the data. Fig. 4 shows the data of Fig. 3 with a low pass filter having a 100 Hz cut off frequency applied to smooth the measured data. As can be seen in examination of Fig. 4, the peaks and troughs of the two lines are not exactly in sync with one another. This could be because there is a slight error in the time recorded between the CAN bus 26 data and the GPS 24 data. The method may if desired and / or necessary execute a time shift at this point to cause the CAN bus 26 data to be put in sync with the GPS 24 data by moving one or both along the time / X axis until they are matched up. This time shifting may be done after the data is smooth by the low pass filter. Alternatively, the time shifting may be done before the smoothing step shown in Fig. 4, and could be done after the data acquisition step of Fig. 3. Again, in accordance with certain exemplary embodiments, the time shifting step and / or the smoothing step need not be applied to the data. A ratio may then be calculated as follows in which GPS 24 speed is the first speed data and the CAN bus 26 data is the second speed data:

[0045] As an example of this calculation, looking at the time on the x-axis that is 400 / 100thof a second, the GPS 24 speed is shown to be 115.45 KPH and the CAN bus 26 speed is shown as 117.15. the ratio R may thus be calculated as 1 15.45 KPH / 117.15 KPH = 0.98549. Ratios of the first speed data and second speed data can be measured at all of the measured points in time on the X axis to arrive at multiple ratios that can be distributed as values in a histogram as shown in Fig. 5. The number of measurements can be as those shown in Figs. 3 and 4 in which 1000 points in time are present and measurements (2 of them) are made at each one of these 1000 points - one with the GPS 24 data and one with the CAN bus 26 data. Taking these numbers over to Fig. 5, the wheel speed ratio which is a whole number is plotted against the number of times that whole number appears as identified as the frequency on the y-axis. The data in Fig. 5 can be further manipulated by the method through using a known mathematical function known as a Gaussian function. Fig. 6 shows the data of Fig. 5 after being approximated to a Gaussian function. The Gaussian function has a mean and a standard deviation with the dotted lines being the Gaussian with the best parameters fitting the data. The standard deviation will be the width of the bell-curve, and the mean will be the middle of the range. Again, the data in Figs. 5 and 6 are for the tire 10 having 0% tread 12 depth remaining. The displayed mean in Fig. 6 is 0.986465, and the calculation of the mean from a Gaussian is a known technique. Other techniques are possible, for example the mean may be calculated by adding the data set numbers together and then dividing by the total number of data set numbers. For example, if five ratio numbers were obtained and they were 0.985, 0.984, 0.990, 0.986, and 0.986, the mean may be calculated as (0.985 + 0.984 + 0.990 + 0.986 + 0.986) / 5 = 0.9862.

[0046] Fig. 7 is a graph that transfers the data of Fig. 5 onto it to show the wheel speed ratio to frequency distribution of the tire 10 when it has 0% tread 12 remaining. In addition, the method may be reproduced using the steps described and illustrated with respect to Figs. 3-6 with a tire 10 that has 25% tread 12 remaining to get similar data that can then be placed onto Fig. 7 with the 0% tread 12 data as a comparison. Additionally, the same steps can be run for a tire 10 with 75% tread 12 remaining, and a tire 10 with 100% tread 12 remaining, to get two additional data sets for placement onto the graph of Fig. 7. The graph then illustrates four different levels of tread 12 depth with four different, distinct distributions of wheel speed ratios. A similar graph can be constructed in which all of this data is converted using the Gaussian function, as discussed from Fig. 5 to Fig. 6, to display the Gaussian distribution of the data of the four tires 10 against one another. Fig. 8 is a chart that takes values of such agraph of the four tires 10 that each have a distinct percentage of tread remaining and shows for each tire 10 its Gaussian mean, Gaussian standard deviation, and the amount of tread 12 remaining on the tire 10 in millimeters.

[0047] A tire model can be constructed from the measured data to predict the amount of tread 12 remaining based upon the calculated ratio. Fig. 9 is a graph that plots data from Fig. 8 to show a line that establishes a generally linear relationship between the ratio and the tread 12 depth remaining. In particular, the ratio mean fitted number from Fig. 8 is plotted against the tread 12 remaining number from Fig. 8 so that four data points are plotted and connected with a line. Linear regression can be used to establish the second line drawn on Fig.9 form this data which is identified in the legend as the ‘'Prediction TD mm” which is the tread 12 depth that is predicted and measured in millimeters. As such, if measurements of the tire10 are taken as previously discussed, and the mean is calculated, one can estimate the amount of tread 12 left on the tire 10 by using the tire model of Fig. 9.

[0048] The tire model can be further refined from that shown in Fig. 9 to that shown in Fig. 10 if desired, although not necessary in accordance with certain exemplary7embodiments. This revised tire model of Fig. 9 uses a confidence interval that uses the standard deviation of the wheel speed ratios previously calculated. A bootstrap regression is performed by randomly drawing a point from each Gaussian previously calculated, generating a set of four points, and fitting a linear model to those four points. The graph in Fig. 10 is completed by generating 100 sets of four points and representing the upper and lower bounds of the linear regressions obtained in the shaded band. The shaded band has a confidence interval of 80% such that for any given tread 12 depth, 80% of the wheel speed ratios will fall within the bounds of the shaded band. This revised model shown in Fig. 10 may be the final model retained to link the wheel speed ratio to the tread 12 depth remaining on the tire 10. However, this revised model of ratio to tread 12 is only one example, and others are possible. For example, the displayed graph in Fig. 9 is another example of the tire model that can be employed in the present method. It is to be understood that the tire model developed in Figs. 9 and 10 that can be used to estimate the tread 12 depth remaining is a tire model for a specific type / brand / size of tire 10, and is not a model for multiple types / brands / sizes of tires 10. As such, the tire model that is generated for use in the method is specific to a particular combination of tire type / size and vehicle model. However, it is the case that the tire model can be developed and used with different tire t pes / size and / or different vehicle models. However, in a preferred embodiment the tire model is specifically designed for a single known tire ty pe / size and a single knownvehicle model, however it is to be understood that this need not be true in other embodiments of the method. Further, the current method measures the tread 12 depth of a particular tire 10 location on the vehicle 20 and does not measure the tread 12 depth of all tires 10 of the vehicle 20. In this regard, it is understood that the second speed data, which includes the rolling radius 16 of a particular tire 10, must be measured to ascertain the tread 12 depth. As such, the method measures the tread 12 depth of an individual tire 10 and not every' tire 10 of the vehicle 20, unless of course the method also processes all of the second speed data of all of the tires 10 of the vehicle 20 in which instance the method could in fact measure all of the tires 10.

[0049] The present method allows for the measurement of tread 12 depth without requiring the user to make a manual check of the tire 10, and without requiring the user to transport the vehicle 20 to a specific site for measurement. A daily tread 12 depth measurement can be obtained if desired. The output from the method may be used to alert the driver of a low tread 12 depth, allow a fleet manager to estimate when new tires 10 will be needed, or can be used to feed any other system or process that needs tread 12 depth data. The disclosed method outputs the data in terms of millimeters (or any other unit) of tread 12 remaining.

[0050] EXAMPLES CARRIED OUT IN ACCORDANCE WITH CERTAIN EXEMPLARY EMBODIMENTS

[0051] Testing of tread 12 depth on various tires 10 was conducted in order to establish the validity of the disclosed method of measuring tire 10 wear. The vehicle 20 in one test was a 2022 FREIGHTLINER CASCADIA ® that has a telematics kit from which first and second speed data was obtained. The vehicle 20 had on its steer axle 275 / 80R22.5 X LINE ENERGY Z ® TL LRH VB MI tires from MICHELIN ®. First speed data (GPS 24) and second speed data (CAN bus 26) was collected and is shown in Fig. 11. Filtering of the data was applied in which only data above 100 KPH (in this instance all of it was above 100 KPH) with a closeness less than 5 KPH (in this instance for each point in time every first speed data was within 5 KPH to every second speed data) was used. This resulted in all of the data showin in Fig. 11 being used. The first speed data was smoothed using a low pass filter to achieve the third data line in Fig. 11. As shown in Fig. 11, there is a mismatch in the time stamping of the data between the first and second speed numbers. The GPS 24 first speed data is lagging the CAN bus 26 second speed data because peaks of the GPS 24 first speed data are appearing later than the peaks reported by the CAN bus 26 second speed data. The CAN bus 26 second speed data can be time shifted by being moved slightly to the right in Fig. 11 so that the peaks match up with those of the filtered GPS 24 first speed data. This time shifting will minimize errorbetween the filtered GPS 24 first speed data and the CAN bus 26 second speed data. The data after the time shifting has been performed is shown with reference to Fig. 12. This time shifting will minimize errors between GPS 24 first speed filtered data and CAN bus 26 second speed data. The time shifting produces a more realistic speed signal from the case where there is a time lag between the first and second speed data and is accomplished by moving one or both so that their peaks are aligned with one another on the X-axis. Computing the ratio between the first and second speed data after this time shifting will yield a resulting histogram that has a tighter standard deviation.

[0052] Ratios of the first speed data and second speed data were measured at 1940 points and then plotted onto the histogram shown in Fig. 13 and as previously explained. This data was further manipulated by the method through the use of the Gaussian function to arrive at the smooth line shown in Fig. 13. The standard deviation of this curve is the width of the bell-curve and is 0.00142309. The mean is the middle of the range and is 1.006. This measurement may be taken at some point during a particular day. A tire model that links mean to tread depth has been established as previously described and by know ing the mean, it can be compared to the known ratio to tread 12 depth to arrive at a tread 12 depth for that particular measurement.

[0053] Fig. 14 is a graph of tread depth 12 over time as acquired from the analysis in Fig. 13 after comparison to the previously established tire model. The tread depth 12 on any particular day can be calculated and then placed onto the graph in Fig. 14 which shows the tread depth 12 as recorded on various days over a nine month period. The shaded area in Fig. 14 is the 80% confidence interval as previously discussed. Also included in Fig. 14 are actual tread 12 depth measurements of the tread 12 using a tread gauge. The tread 12 depth data from the method has a downw ard trend as would be expected over time as the tread 12 wears, and the tread 12 depth data from the method also tracks the actual inspection measurements when the tread 12 is checked using a tread gauge measurer.

[0054] Fig. 15 is a graph of tread 12 depth over time of the same type of tire 10 and the same type of vehicle 20 as previously described with respect to the tire 10 and vehicle 20 of the Fig. 11-14 example. The tire 10 is again present on the steer position of the vehicle 20, but it is to be understood that this is a different tire 10 and vehicle 20 than that previously discussed, but of the same type. The tread 12 depth trends downward over time and is consistent with the inspection data points that represent actual tread 12 depth upon measurement of the tread 12 with a tire gauge. In September of 2022 the estimated tread 12depth from the method shows an increase in the tread 12 depth at which point it continues to move downward as time goes on at generally the same rate as it was moving downward before September of 2022. It can be theorized that the tire 10 was replaced in September of 2022 with a different tire 10 that has a greater amount of tread 12 thereon due to the increase in tread 12 depth at that time.

[0055] Fig. 16 is a graph of tread 12 depth over time of a tire 10 that is on a steer axle and is a 275 / 80R22.5 X LINE ENERGY Z ® TL LRH VB MI tire from MICHELIN ®. The vehicle 20 is a 2018 VOLVO ® VN780 truck. As shown, the tread 12 depth estimated from the ratio comparison to the tire model does not match the actual tread 12 depths obtained from inspection of the tire 10 with a tire gauge. Applicant theorizes the reason for the poor performance of the method in this example is due to the use of a different nominal rolling radius 16 used in the electronic control unit of the vehicle 20 to calculate the second speed data from the CAN bus 26. In this regard, the tire model to which the ratios are compared is not accurate for the VOLVO ® VN780 truck. The tire model that is used in the present method may then be associated with a specific type of vehicle 20 from a manufacturer and a specific tire 10 at a specific position on the vehicle 20, and not with every type of vehicle 20 from different manufacturers and every tire 10 type on every position of the vehicle 20. In order to arrive at the correct tread 12 depth information from the method, a correction factor is applied. In this regard, a coefficient is applied to the speed ratio from back calculation to better fit the resulting tread 12 depths to the actual tread 12 depth measurements from the tire gauge as shown in Fig. 17. The correction factor that can be added to the method in some instances where the tire model does not sufficiently coincide with the actual measurements through inspection of the tire may be calculated as follows:GPS speed [kphl Radius Actual

[0056] r = - Front Axle Speed [kph] = - a Volvo X Radius ECU predefined

[0057] In this instance, the a Volvo is equal to 0.992, and is a correction factor that may be applied to the ratio to cause the values estimated by the method to be closer to the actual measured values obtained through inspection with a tire gauge. This correction factor will involve knowing the actual, measured rolling radius 16 of the tire 10 along with the predefined rolling radius 16 of the tire 10 as programmed into the electronic control unit that is provide with the vehicle 20 for use in determining the traveling speed 18 via the CAN bus 26 data.

[0058] The method can report to the user the results of the tread 12 wear estimation by an output to a display in the vehicle 20, or to a phone or computer. In some instances the output may be the amount of tread 12 estimated by the method to be remaining on the tire 10. Theoutput may not be sent to the user until the tread 12 remaining moves below some limit at which time a replacement warning is sent to the user. Another way of reporting output by the method is for the method to calculate an estimated removal date of the tire 10 and report this date to the user. One example of how the estimated removal date can be calculated and reported can be described first by reference to Fig. 18 which shows the tread 12 depth estimated by the method at various points in time. The tire 10 is to be removed when the tread 12 reaches a depth of 2 millimeters remaining which is shown in the chart as a horizontal dashed line. The method starts from the latest point measured, and looks backwards such that the last 20 measurements of tread 12 depth are evaluated. A linear regression is calculated with these last 20 measured points to form a line that is extended until it contacts the horizontal line representing 2 millimeters of tread 12 depth remaining. The engagement with the horizontal line at 2 millimeters of tread 12 remaining is the estimated date the tire 10 must be removed.

[0059] To further refine the estimated date of removal, a larger data set of points can be evaluated with the linear regression to cause a second line to be formed. Fig. 19 show the same data of Fig. 18 but with the last 40 points evaluated such that linear regression causes a line to be formed through these last 40 points that intersects the 2 millimeter horizontal replacement line. This intersection point is at a different point than that discovered in Fig. 18. The method may repeat this process by using different sets of points to form different lines through linear regression to in turn find different intersection points with the 2 millimeter horizontal replacement line. The method may in some instances use all of the data points to form a line through linear regression. In other instances, multiple data sets of points are used to estimate the removal date, and these sets could include 10, 15, 20, 25, or 30 measured points of tread 12 wear in accordance with various embodiments. Fig. 20 is a chart of the same data of Figs. 18 and 19 but with multiple data sets evaluated through linear regression to yield a plurality of lines that intersect the 2 millimeter horizontal replacement line. After obtaining a plurality’ of dates of replacement, the method may organize the dates into a histogram to show the most frequent date projected. Fig. 21 is a graph of tread 12 depth over time that includes the same data as that presented in Figs. 18-20 but that also includes a histogram of the collected data of Fig. 20. The histogram shows the number of times the various measurements resulted in a particular month being designated as the replacement date. The removal date / month is located on the X-axis of this chart and the frequency is on the Y-axis which is the number of times that particular month was estimated. April of 2023 is the month of the highest frequency and the method may report to the user that the tire 10 is anticipated to be removed in April of 2023.Alternatively, the chart of Fig. 21, the histogram of Fig. 21, or both may be presented to the user by the method to inform the user of the anticipated removal date. The chart shows but a single dashed line that extends to the 2 millimeter horizontal replacement line, and this single dashed line is representative of the results of the histogram that shows April of 2023 as being the month of anticipated removal. This dashed line is thus not a linear regression of the data set as previously discussed, but is rather a graphical indicator of the histogram results to give a visual representation to the user of the future expected wear of the tread 12 to its anticipated removal date.

[0060] While the present subject matter has been described in detail with respect to specific embodiments and methods thereof, it will be appreciated that those skilled in the art, upon attaining an understanding of the foregoing may readily produce alterations to, variations of, and equivalents to such embodiments. Accordingly, the scope of the present disclosure is by way of example rather than by way of limitation, and the subject disclosure does not preclude inclusion of such modifications, variations and / or additions to the present subject matter as would be apparent.

Claims

CLAIMSWhat is claimed is:

1. A method for determining tread remaining on a tire, comprising: obtaining first speed data of a vehicle onto which the tire is located; obtaining second speed data of the vehicle onto which the tire is located, wherein the second speed data is derived in part from measurement of the frequency of rotation of the tire; filtering the first speed data such that filtered first speed data is extracted that is over a first predefined speed limit; filtering the second speed data such that filtered second speed data is extracted that is over a second predefined speed limit; calculating a ratio using the filtered first speed data and the filtered second speed data; comparing the ratio to a tire model that links ratio to tread remaining in order to determine the tread remaining on the tire.

2. The method as set forth in claim 1 , wherein the first predefined speed limit is the same as the second predefined speed limit.

3. The method as set forth in claim 2, wherein the first predefined speed limit is 100 kilometers per hour, and wherein the second predefined speed limit is 1 0 kilometers per hour.

4. The method as set forth in any one of claims 1-3, wherein the filtered first speed data that is extracted is also within 5 kilometers per hour of the filtered second speed data that is extracted.

5. The method as set forth in any one of claims 1-4, wherein the ratio that is calculated uses the filtered first speed data and the filtered second speed data that are obtained at the same point in time.

6. The method as set forth in any one of claims 1-5, wherein the calculating the ratio and the comparing the ratio are executed without obtaining acceleration data of the vehicle.

7. The method as set forth in any one of claims 1-6, wherein the first speed data is obtained using geolocation signals, and wherein the second speed data is obtained by reading CAN bus data of the vehicle.

8. The method as set forth in any one of claims 1-7, wherein the calculating the ratio comprising using a Gaussian function to obtain a mean value that is designated as the ratio that is subsequently compared to the tire model.

9. The method as set forth in any one of claims 1-8, wherein the tire model that links ratio to tread remaining is built using ratio mean fitted data of model ratios of tires having different amounts of tread remaining.

10. The method as set forth in claim 9, wherein the tire model that links ratio to tread remaining is further built by using a confidence interval and linear regression of the ratio mean fitted data of model ratios of tires.

11. The method as set forth in any one of claim 1-10, further comprising time shifting the filtered first speed data and / or the filtered second speed data to cause the filtered first speed data and the filtered second speed data to occur at the same points in time.

12. The method as set forth in any one of claims 1-11, wherein the tire is a heavy truck tire and is a steer tire.

13. The method as set forth in any one of claims 1-12, further comprising: repeating the method of claim 1 to obtain a plurality of tread remaining measurements at different points in time; predicting a plurality of removal dates by using linear regression on a plurality of different data sets of the plurality of tread remaining measurements; andpredicting a single removal date by selecting the highest number of appearing removal date from the plurality' of removal dates.

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