Detection of Tire Air Pressure Loss

Abstract

A method for detecting a pressure loss event in a vehicle tire to be evaluated, the method comprising: obtaining real-time tire air pressure data from a plurality of tire mount sensors (TMS) respectively attached to one of a plurality of tires including the tire to be evaluated in the same vehicle; calculating a tire pressure change rate over a fixed-length time window for each of the plurality of tires in the vehicle; comparing the pressure change rate of the tire to be evaluated with one or more pressure change rates of other tires other than the tire to be evaluated among the plurality of tires in the vehicle; and determining based on the comparison whether the pressure change rate of the tire to be evaluated is an abnormal pressure change rate that may indicate a pressure loss event in the tire.

Description

Technical Field

[0001] The present invention relates to a method for detecting a pressure loss event of a vehicle tire. The present invention also relates to a pressure loss event detection system.

Background Art

[0002] A pressure loss event in a vehicle tire may be related to a puncture, and the ability to detect such an event is important for vehicle safety. An accurate measurement of the tire pressure can be obtained using a sensor mounted inside the tire.

[0003] A sensor mounted inside the tire is also called a tire-mounted sensor (TMS). Using the TMS, in addition to monitoring some parameters of the tire itself such as the tire pressure and the tire temperature, information regarding the interaction between the tire and the surrounding environment such as the road or the vehicle is extracted.

[0004] Current pressure loss detection methods monitor the air pressure of a vehicle tire, compare the measured air pressure with a threshold value, and determine whether the air pressure of the tire that may indicate a puncture is extremely low.

[0005] The problem with such a method is that a pressure loss event is not detected unless the air pressure of the tire falls below a fixed threshold value, so it may be too late for the driver to take proactive actions such as driving to a tire repair shop. As an alternative, a warning can also be issued at an earlier stage by setting a higher threshold value, but in this case, the possibility of false detection increases, causing inconvenience to the user.

[0006] An improved method for reliably detecting a tire pressure loss event at an early stage is required.

Summary of the Invention

[0007] According to a first embodiment of the present invention, there is provided a method for detecting a pressure loss event in a vehicle tire to be evaluated, Obtaining real-time tire pressure data from a tire mount sensor (TMS) attached to one of a plurality of tires including the vehicle tire to be evaluated in the same vehicle, Calculating the tire pressure change rate in a fixed-length time window for each of the plurality of tires in the vehicle, Comparing the pressure change rate of the tire to be evaluated with the pressure change rate of one or more tires other than the tire to be evaluated among the plurality of tires in the vehicle, and Determining from the comparison whether the tire to be evaluated has an abnormal pressure change rate indicating a pressure loss event in the tire A method is provided that includes the above.

[0008] Those skilled in the art will understand that by comparing the pressure change rate of the tire to be evaluated with the pressure change rate of other tires other than the tire to be evaluated in the same vehicle, it is possible to detect a pressure loss event such as a puncture earlier than the method of comparing the tire air pressure or the pressure change rate with a fixed threshold. Furthermore, according to this method, this rapid detection can be performed accurately. On the other hand, in order to achieve rapid detection using a fixed threshold, it is necessary to set the threshold with a low tolerance error, which increases the possibility of false detection.

[0009] It should be understood that the abnormal pressure change rate of the tire to be evaluated does not substantially match the pressure change rate of one or more other tires of the same vehicle. This can be determined by repeatedly comparing the pressure change rates over a plurality of different fixed-length time windows, as will be further explained below.

[0010] Multiple TMSs are attached to multiple tires, and it should be understood that this means that each TMS is attached, for example, to the inner liner of a tire and is installed inside each tire. When attaching a certain TMS inside a tire, place the TMS at a position where the internal air pressure of the tire (and optionally the internal temperature of the tire) is directly measured. This position brings advantages such as protecting the sensor from curbs and road surface debris, and providing a more accurate tire temperature due to being less affected by changes in ambient temperature and heat from the braking system.

[0011] In some embodiments, the multiple tires are all on the same axle. In such embodiments, compare the rate of pressure change of the tire to be evaluated with the rate of pressure change of one or more of the other tires among the multiple tires on the same axle other than the tire to be evaluated.

[0012] In some embodiments, the method includes shifting a fixed-length time window forward in time (e.g., by a fixed increment such as 5 minutes) to the next fixed-length time window, and for each of the multiple tires, recalculating the rate of pressure change over the next fixed-length time window. Then, the method repeats the comparison step and the determination step to re-evaluate whether the tire to be evaluated exhibits an abnormal rate of pressure change in this next fixed-length time window. By repeating these steps each time the fixed-length time window shifts in time, that is, for each rolling fixed length time window, according to the method, it is possible to continuously determine whether the tire to be evaluated has an abnormal rate of pressure change. For example, the method can be repeated every 5 minutes. By repeatedly updating the calculation of the rate of pressure change at a relatively high frequency, the possibility of quickly detecting any pressure loss event is increased.

[0013] In some embodiments, when detecting pressure loss, the fixed increment may be decreased. For example, the fixed increment may be decreased to 2 minutes. By decreasing the fixed increment in this way, faster recalculation becomes possible, which is for the purpose of confirming the presence of a pressure loss event or identifying false detections. Further, in embodiments where the remaining time is calculated, the accuracy of the remaining time may be improved by a rapid recalculation of the pressure change rate.

[0014] In some embodiments, the method includes shifting a fixed-length time window forward in time by a fixed increment that is shorter than the length of the fixed-length time window. For example, for a time window with a fixed length of 30 minutes, the fixed increment can be 5 minutes.

[0015] In some embodiments, calculating the pressure change rate over a fixed-length time window includes evaluating the average of the pressure change rate. This average can be calculated as the total change in pneumatic pressure data over the fixed-length time window divided by the length of the fixed-length time window.

[0016] In some embodiments, a sub-window is applied to the pressure data within a fixed-length time window and the average pneumatic pressure of the sub-window is calculated. Thereby, the vibration of the pressure data can be smoothed and the influence of incorrect measurements can be reduced. In one embodiment, the length of the sub-window can be 10 minutes. By calculating the average pressure for each sub-window, the pressure data obtained within the fixed-length time window can be converted into a series of average pressure values.

[0017] In some embodiments, the pressure change rate (PCR) can be calculated as the slope m of the least-squares first-order polynomial fit of the pressure data obtained within a fixed-length time window. In embodiments where the average pressure is calculated for each sub-window, the pressure change rate (PCR) can be calculated as the slope m of the least-squares first-order polynomial fit within the fixed-length time window. As described above, the fixed-length time window may have a fixed length of dt (for example, 30 minutes).

[0018] In some embodiments, the method further includes obtaining real-time tire temperature data corresponding to real-time tire pressure data and normalizing the real-time tire pressure data using the real-time tire temperature data to remove the influence of temperature changes, and the calculated pressure change rate is the normalized pressure change rate. The real-time tire temperature data can be obtained from any sensor capable of measuring or estimating the temperature inside the tire.

[0019] In some embodiments, for the tire to be evaluated, both the normalized pressure change rate and the non-normalized pressure change rate are calculated.

[0020] Removing the influence of temperature changes has the effect of removing large fluctuations in the pressure itself. Therefore, the normalized pressure indicates the pressure loss due to a puncture with higher reliability and does not give false detections due to pressure losses as a result of significant temperature effects, for example.

[0021] In some embodiments, normalizing the real-time tire pressure data includes dividing each pressure value of the real-time tire pressure data by the corresponding temperature value of the real-time tire temperature data.

[0022] In some embodiments, the real-time tire temperature data is obtained from the same TMS as the plurality of TMSs used to obtain the real-time tire pressure data. In a preferred embodiment, the real-time tire temperature data is obtained from the same plurality of TMSs used to obtain the real-time tire pressure data.

[0023] By using the temperature inside the tire obtained from the TMS instead of the external temperature, it becomes less susceptible to the influence of events in the atmosphere, and the accuracy of pressure loss can be improved. Furthermore, by measuring the air pressure and temperature inside the tire with the same TMS, it can be guaranteed that the measured values of temperature and pressure are measured under exactly the same conditions. Therefore, the measured value of pressure can be more accurately normalized based on the measured value of temperature.

[0024] In some embodiments, the pressure change rate of the tire to be evaluated is compared with a pressure change rate threshold, and the pressure change rate of the tire to be evaluated is compared with the pressure change rate of other tires only when the pressure change rate of the tire to be evaluated is below the threshold. In some embodiments, only the normalized pressure change rate is compared with the normalized pressure change rate threshold. In another embodiment, the normalized pressure change rate is compared with the normalized pressure change rate threshold, and the non-normalized pressure change rate is compared with the non-normalized pressure change rate threshold.

[0025] In one embodiment, the pressure change rate threshold is set to approximately 0, and only the negative pressure change rate corresponding to the pressure loss is compared with the pressure change rate of other tires.

[0026] In one embodiment, when comparing both the normalized pressure change rate and the non-normalized pressure change rate with their respective thresholds, the thresholds for both the normalized pressure change rate and the non-normalized pressure change rate may be approximately 0. In a set of embodiments, the normalized pressure change rate threshold is equal to 0.0005, 0.0004, 0.0003, 0.0002, or 0.0001. In a further set of embodiments, the non-normalized pressure change rate threshold is equal to 0.

[0027] By analyzing only the negative pressure change rate, i.e., the pressure loss rate, the required processing amount can be reduced.

[0028] In one embodiment, the value of the average pressure change rate (PCR) is calculated from the PCRs of other tires, and the pressure change rate (PCR) of the tire to be evaluated is compared with the average PCR value to perform a comparison between the tire to be evaluated and other tires. This is known as a "leave-one-out" comparison, and PCR is an abbreviation for "pressure change rate".

[0029] In one embodiment, it further includes calculating the remaining time until the critical pressure value of the tire to be evaluated is reached when it is determined that the tire to be evaluated has an abnormal pressure change rate. In one embodiment, the remaining time is calculated by calculating the difference between the current pressure and the critical pressure and dividing the difference by the current PCR.

[0030] This method may further include displaying the remaining time to the user. In some embodiments, the user can be the driver of the vehicle, and the remaining time can be displayed to the driver via a driver terminal installed in the vehicle or via an application installed on the driver's mobile phone. In additional or alternative embodiments, the user can be a fleet manager, and the remaining time can be displayed to the fleet manager via a fleet manager application installed on the fleet manager's computer.

[0031] By displaying the remaining time to the user, the user can then determine what steps to take next. For example, the user can complete the journey based on the remaining time or determine whether it is necessary to detour to a tire repair shop.

[0032] In one embodiment, the method can make suggestions to the user, such as providing the locations of tire repair shops that can be reached within the remaining time until the tire pressure reaches the critical value.

[0033] In one embodiment, the method includes comparing the remaining time with a remaining time threshold and alerting the user only if the remaining time is lower than the threshold. In such an embodiment, even when the remaining time is greater than the threshold, the remaining time can be left displayed to the user, but no explicit alert is issued. In one embodiment, when the remaining time is greater than the remaining time threshold, the method can spend more time (e.g., 5 hours) to confirm whether the detected pressure loss event is a false detection, and can improve the estimation of the pressure change rate and the remaining time.

[0034] Any of the methods disclosed herein can be a computer-implemented method.

[0035] According to a second aspect of the present invention, there is provided a recording medium storing firmware code that, when executed on a data processor, executes the method of the first aspect.

[0036] According to a third aspect of the present invention, there is provided a system for detecting a pressure loss event, a plurality of tire mount sensors (TMSs) for collecting real-time tire pressure data from a plurality of tires in the same vehicle, and a processor, wherein the processor is configured to obtain real-time tire pressure data in a plurality of tires including the tire to be evaluated from the plurality of tire mount sensors (TMSs) attached to the vehicle, configured to calculate a pressure change rate in a fixed-length time window for each of the plurality of tires, configured to compare the pressure change rate of the tire to be evaluated with one or more pressure change rates of tires other than the tire to be evaluated among the plurality of tires in the vehicle, and Based on the comparison, a system is provided that is configured to determine, based on the comparison, whether the rate of change of pressure of the tire being evaluated has an abnormal rate of change of pressure that may indicate a pressure loss event in the tire.

[0037] The processor may be installed in the vehicle and arranged to directly obtain real-time tire air pressure data from a plurality of tire mount sensors. In some embodiments, the processor may be installed in a remote server and arranged to indirectly obtain real-time tire air pressure data from a plurality of tire mount sensors. A remote server is a server located at a location away from the vehicle.

[0038] In one embodiment, the system includes one or more user output devices. The user output device can include one or more of a driver terminal in the vehicle, a mobile phone, and / or a fleet manager's computer.

[0039] Of course, it should be understood that the term "server" as used herein means a computer or device (e.g., a server device) connected to a network such that the server transmits and / or receives data from other devices (e.g., computers or other devices) on its network. Additionally or alternatively, the server can provide resources and / or services to other devices on the network. The network can be the Internet or other suitable network. The server can be embodied in any suitable server type or server device, such as a file server, an application server, a communication server, a computing server, a web server, a proxy server, etc. For example, the server can be a cloud-based server, i.e., the server function can be divided among a plurality of computing devices and may be located in physically different locations from which server resources can be obtained.

[0040] Therefore, it should be understood that the above-described process executed by the server may be executed by a single computing device, i.e., a single server, or may be executed by a plurality of separate computing devices, i.e., a plurality of servers. For example, all the processing may be executed by a single server that has access to all the necessary information.

[0041] One or more non-limiting examples will be described by way of example only, with reference to the accompanying drawings.

Brief Description of the Drawings

[0042]

Figure 1

Figure 2

Figure 3

Figure 5

Figure 7

Figure 9

Modes for Carrying Out the Invention

[0043] Figure 1 shows a system 1 for detecting a pressure loss event of a vehicle tire 3 attached to a vehicle 2. The system 1 includes a plurality of tire mount sensors (TMSs) 5 each attached to the tire 3, and a network communication device 6 provided in the vehicle 2. In one embodiment, the network communication device can be a dongle plugged into a port such as an OBD port or an FMS port in the vehicle 2. In another embodiment, the network communication device 6 can be a permanently mounted transceiver box. The TMS 5 is configured to communicate with a remote server 7 via the dongle 6 of the vehicle 2. In this example, the TMS 5 communicates with the network communication device 6 via a Bluetooth (registered trademark) connection, but it should be understood that any suitable form of short-range wireless communication or preferred connection can be used. The network communication device 6 is network-connected and communicates with the remote server 7 via a wireless network connection (e.g., a cellular network). The remote server 7 is connected to a user output device 10 via a wireless network. In the illustrated embodiment, a plurality of user output devices 10 are provided, such as a terminal 10a provided in the cabin of the vehicle 2, a user mobile device 10b that can be carried by the user, and a computer 10c of a fleet manager.

[0044] Next, the operation of the system 1 will be described with reference to the flowchart of FIG. 2. In step 101, the TMS 5 acquires pressure and temperature data from inside the tire 3 of the vehicle over a period corresponding to a fixed-length time window, for example, 30 minutes. In this example, the pressure and temperature data are transmitted to the network server 7. In this example, the remote server 7 first executes an algorithm in step 103 to normalize the measured value of the tire air pressure to remove the influence of temperature. The normalized pressure P' can be obtained from the raw pressure P and the internal temperature T according to Equation 1.

Equation

[0045] Figures 3a and 3b respectively plot the measured values of non-normalized pressure and normalized pressure against time over a period of about 32 hours. As is clear from each figure, the normalized pressure graph (Figure 3b) is much flatter than the non-normalized graph (Figure 3a), and the pressure change due to potential punctures can be monitored with higher accuracy because temperature fluctuations only interfere with puncture detection.

[0046] Since the internal temperature is less affected by atmospheric events than the temperature measured outside the tire, the internal temperature is used to normalize the pressure data and improve the accuracy of pressure loss detection.

[0047] Before or after the normalization step 103 in Figure 2, the pressure values taken over a time period can be smoothed or averaged in a suitable way. This can include, for example, applying a sub-window (e.g., 10 minutes) inside a time window of fixed length (e.g., 30 minutes) of the time period. The sub-window is used to generate a rolling average from the pressure values (normalized and / or non-normalized) within the sub-window. This is done to smooth out the oscillations of the pressure values and remove the influence of incorrect values. That is, at each instant, instead of the instantaneous pressure, the average value based on the most recent sub-window (e.g., 10 minutes) is considered.

[0048] In steps 105 and 107, respectively, the algorithm calculates the pressure change rate (PCR) of the non-normalized pressure (non-normalized PCR) and the change rate of the normalized pressure (normalized PCR) over a fixed-length time window (e.g., a 1 / 2-hour window) for all of a plurality of tires. In the illustrated embodiment, the plurality of tires are all mounted on the same axle of the vehicle, but it should be understood that this is an arbitrary feature and the plurality of tires need only be mounted on the same vehicle.

[0049] Normalized PCR is calculated as the slope m of the least squares first-order polynomial fit of the average normalized pressure value within a fixed-length time window. Similarly, non-normalized PCR is calculated as the slope m of the least squares first-order polynomial fit of the average non-normalized pressure value within a fixed-length time window. The algorithm then compares the normalized PCR and non-normalized PCR in step 109 with the normalized PCR threshold and non-normalized PCR threshold, respectively. The thresholds are set near 0, and the comparison is satisfied when the normalized PCR and non-normalized PCR are each below their respective thresholds. That is, substantially positive pressure change rates are excluded. This causes the comparison to be satisfied only for pressure loss events where both the pressure and the normalized pressure decrease between time windows, and increases the likelihood that the comparison will not be satisfied for situations where the pressure is rising. If the pressure is rising, further analysis is not performed because there is no need to further analyze the data to detect a pressure loss event. In some embodiments, the values of the non-normalized PCR threshold and the normalized PCR threshold are set to 0 and 0.0005, respectively.

[0050] If both the normalized PCR and the non-normalized PCR are below their respective thresholds, the algorithm proceeds to step 111. If either or both of the normalized PCR and the non-normalized PCR are above their respective thresholds, the algorithm returns to step 101. In one embodiment, the comparison in step 109 can be omitted, and the algorithm can proceed directly from the calculation of PCR in steps 105 and 107 to the calculation of the difference in step 111.

[0051] In steps 111 and 113, the algorithm uses a statistical method, which will be described in detail below, to identify the significant difference between the normalized PCR of the tire to be evaluated and the normalized PCR of other tires on the same axle, and extract abnormal PCRs that may be related to pressure loss events such as tire punctures. FIGS. 4a, 4b, and 4c show the positions of three different fixed-length time windows comparing the normalized pressures when one of the four tires on the drive axle has a puncture. By using a sliding window with a length of 30 minutes, in FIG. 4a, it can be seen that the pressure change rate (PCR) of the tire with the position "DriveExtR" mainly coincides with the PCR values of the tires in other positions until around 10:26. In FIG. 4b, the time window has shifted forward, and the PCR value of the tire with the position "DriveExtR" deviates from the PCR values of the tires in other positions, with a difference of one digit occurring around 10:34. In FIG. 4c, by around 10:42 when the time window has shifted forward, the difference has further expanded to two digits. By repeating the analysis over subsequent time windows, the algorithm is constantly exploring whether there are abnormalities in the normalized pressure change rate (PCR) of a specific tire compared to other tires on the same axle.

[0052] In this example, to identify abnormal PCRs, a leave-one-out approach is used for coaxial comparison. For the tire to be evaluated, the algorithm calculates the difference between the PCR of the tire and the average PCR in other tire positions on the same axle. Next, abnormalities in the distribution of PCR differences occurring between the average PCRs can be defined using a statistical approach. In one embodiment, outliers in the distribution of PCR differences are explored to define abnormal values. Here, an outlier refers to a PCR difference that is sufficiently far from others, and the sufficiency of the difference in the calculated PCR difference is based on the interquartile range. More specifically, an outlier is an observed value of the distribution that is outside the range. [Q1 - k(Q3 - Q1); Q3 + k(Q3 - Q1)] Equation 2 Here, Q1 and Q3 are the lower quartile value and the upper quartile value respectively, and k is a constant conventionally set to 1.5.

[0053] An abnormal PCR is a PCR in which, among the current fixed-length time-window PCRs, the difference from the average PCR at the position of another tire on the same axle as the PCR is an outlier in the distribution of the coaxial PCR differences.

[0054] To make the distribution of PCR differences wider and more representative, both the coaxial PCR differences calculated in the current fixed-length time window and the coaxial PCR differences calculated the previous day using a time window with the same fixed length can be used.

[0055] If the normalized PCR is not identified as abnormal, an "OK" output is issued to the user at step 112. If an abnormal PCR is identified, together with the tire position where such a difference occurs in the PCR compared to other tire positions on the same axle, at step 115 the algorithm optionally estimates for that tire position and represents the days and / or times until the pressure reaches a critical state.

Number

[0056] In one embodiment, the critical pressure can be defined as 6 bar for TBR (truck bus radial tires). Alternatively, the critical pressure can also be set to 60% of the standard air pressure. The estimated remaining time can define the risk level (speed) of critical / pressure loss.

[0057] In step 116, the remaining time associated with the abnormal PCR is output to the user. In step 117, the remaining time is compared with a remaining time threshold. If the remaining time is less than the threshold, in step 119, an alert is notified to the user. If the remaining time is greater than the remaining time threshold, since the magnitude of the pressure change rate is small, there is a possibility that the pressure loss detection is a false detection. Therefore, in such a scenario, more time (e.g., 5 hours) can be used to re-evaluate the PCR in a plurality of subsequent time windows to improve the estimated value of the remaining time or to verify whether the criticality is a false detection. In this way, the method loops back to the first step 101, and the time frame shifts forward (e.g., by 5 minutes each time). As a result, more pressure and temperature measurement values are obtained, and as shown by arrow 118, the method is repeated for subsequent time windows.

[0058] It should be understood that the described process is naturally carried out multiple times, with each tire 3 on the axle being evaluated in turn as the object of evaluation.

[0059] In the algorithm of the illustrated embodiment, the length of the time window for PCR comparison is set to 30 minutes. The algorithm repeats its evaluation in subsequent time windows shifted 5 minutes forward. In one embodiment, the threshold for the definition of an emergency alert is set to 12 hours.

[0060] The TMS pressure loss detection algorithm described in this specification is developed using experiments conducted on multiple tires, taking into account two main factors of accuracy, namely, the number of false detections and the ratio of the time required to detect a leak to the time required for the pressure to reach the critical value.

[0061] This experiment was conducted on three vehicles that had each traveled more than 75,000 km and had different types of loads. In this experiment, the TMS pressure loss detection algorithm was executed at a frequency of every 5 minutes. Pressure losses due to punctures were observed twice. · In the first puncture, a leak was detected 2 minutes after the puncture occurred, and the pressure had dropped by 0.3 bar (i.e., a relatively "fast" puncture). · In the second puncture, leakage was detected 16 minutes after the puncture occurred, but by that time the pressure had only dropped by a mere 0.15 bar (i.e., a relatively "slow" puncture). There were no false detections, and the detection times were all within 10% of the time to the critical point.

[0062] Figures 5a and 5b are graphs plotting the normalized pressure of the pressure loss events detected by the algorithm against time. The algorithm depends on a 30-minute sliding window during which data is acquired by TMS5. A coaxial comparison was performed on the TMS data acquired during each 30-minute window. This window shifts forward by a fixed amount, for example 5 minutes, each time. Figure 5a shows the 30-minute time window in which an abnormal PCR was first detected. The difference in PCR due to the difference in tire position on the steering axle is obvious, and it can be seen that the tire position SteeringL corresponds to a large pressure change rate. For this severity of PCR, since the remaining time to the critical point is below a fixed threshold (12 hours in the illustrated embodiment), an alert is immediately issued. Figure 5b shows the normalized pressure over a 5-hour window including the first detection. As shown in Figure 5b, the 30-minute detection window corresponds to the start of the tire air leak, and thereafter, the air leak increases in speed and reaches a pressure value of 2 bar in about two and a half hours.

[0063] In the above example, the algorithm has been described as being deployed on the remote server 7 (e.g., its processor). However, in other embodiments, the network communication device 6 may include a processor for executing the algorithm within the vehicle 2. In such embodiments, the network communication device 6 can then bypass the network connection requirements and directly communicate any remaining time estimate or warning to the user terminal 10a provided in the vehicle 2. In such embodiments, the output of the algorithm may still be transmitted to the remote server 7 via the network, and such output may be transmitted from the remote server 7 to the mobile device 10b and / or the fleet manager's computer 10c. The deployment of the algorithm in the network communication device 6 significantly reduces the data transmission to the remote server 7. In other embodiments, the steps executed by the algorithm may be divided between the processor of the network communication device 6 and the processor of the remote server 7 so as to share the data processing load and reduce part of the data transmission load.

[0064] FIG. 6 shows the output of the algorithm that can be displayed on any of the user terminals 10a, 10b, and 10c. For tires where the PCR is not abnormal, an output of "OK" is displayed. For the tires 2L2 and 4R1 with PCR abnormalities, the remaining time until the critical point is displayed. In the example of FIG. 6, the remaining time of the tire 4R1 is 96 hours. Since this exceeds the threshold of 12 hours, no alert is issued for this tire, and the PCR is re-evaluated to confirm or correct the puncture detection. Since the remaining time of the tire 2L2 is only 2.8 hours, an alert will be issued to the user.

[0065] This method is further assumed to include the step of issuing guidance regarding the following steps to the user. For example, the system of the present invention is integrated with a navigation system, and the user can identify a tire repair shop that can be reached within the remaining time. The method can further include automatically making a reservation at the tire repair shop, and in the case of a less serious puncture, a maintenance schedule may be set up.

Claims

1. A method for detecting a pressure loss event in a vehicle tire to be evaluated, comprising: obtaining real-time tire air pressure data from a plurality of tire mount sensors (TMS) respectively attached to one of a plurality of tires including the tire to be evaluated in the same vehicle; calculating a tire pressure change rate for each of the plurality of tires in the vehicle over a fixed-length time window; comparing the pressure change rate of the tire to be evaluated with one or more pressure change rates of other tires other than the tire to be evaluated among the plurality of tires in the vehicle; and determining, based on the comparison, whether the pressure change rate of the tire to be evaluated is an abnormal pressure change rate that may indicate a pressure loss event in the tire. A method comprising the above.

2. The method according to claim 1, wherein the plurality of tires are on the same axle of the vehicle.

3. The method according to claim 1 or 2, further comprising: temporally shifting a fixed-length time window forward to the next fixed-length time window; and recalculating the pressure change rate for each of the plurality of tires over the next fixed-length time window. A method comprising the above.

4. The method according to any one of claims 1 to 3, further comprising obtaining real-time tire temperature data corresponding to the real-time tire air pressure data, and normalizing the real-time tire air pressure data using the real-time tire temperature data to remove the influence of temperature change, wherein the calculated pressure change rate is a normalized pressure change rate.

5. The method according to claim 4, wherein normalizing the real-time tire air pressure data includes dividing each pressure value of the real-time tire air pressure data by the corresponding temperature value of the real-time tire temperature data.

6. The method according to claim 4 or 5, wherein the real-time tire temperature data is obtained from the same TMS as the plurality of TMS used to obtain the real-time tire air pressure data.

7. The method according to any one of claims 1 to 6, wherein the rate of change in pressure of the tire to be evaluated is compared with a rate-of-change-in-pressure threshold value, and the rate of change in pressure of the tire to be evaluated is compared with the rate of change in pressure of the other tire only when the rate of change in pressure of the tire to be evaluated is below the threshold value.

8. The method according to claim 7, wherein the rate-of-change-in-pressure threshold value is set to approximately 0, and the rate of change in pressure of the other tire is compared only for the negative rate of change in pressure corresponding to the pressure loss.

9. The method according to any one of claims 1 to 8, wherein the comparison between the tire to be evaluated and the other tire is performed by calculating the average rate of change in pressure of the rate of change in pressure in the other tire.

10. The method according to any one of claims 1 to 9, further comprising calculating a remaining time until the critical pressure value of the tire to be evaluated is reached after determining that the tire to be evaluated has an abnormal rate of change in pressure, and optionally including displaying the remaining time to the user.

11. The method according to claim 10, further comprising comparing the remaining time with a remaining-time threshold value, and notifying the user of an alert only when the remaining time is lower than the threshold value.

12. A computer-readable storage medium storing firmware code that, when executed on a data processor, executes the method according to any one of claims 1 to 11.

13. A system for detecting a pressure-loss event, a plurality of tire mount sensors (TMSs) for collecting real-time tire air pressure data from a plurality of tires on the same vehicle, and a processor, wherein the processor is configured to obtain real-time tire air pressure data in a plurality of tires including the tire to be evaluated from the plurality of tire mount sensors (TMSs) attached to the vehicle, is configured to calculate a rate of change in pressure over a fixed-length time window for each of the plurality of tires, is configured to compare the rate of change in pressure of the tire to be evaluated with one or more rates of change in pressure of tires other than the tire to be evaluated among the plurality of tires in the vehicle, and Based on the comparison, it is configured to determine, based on the comparison, whether the rate of change in pressure of the tire to be evaluated is an abnormal rate of change in pressure that can indicate a pressure loss event in the tire. System. **Claim 14** The system according to claim 13, wherein the processor is disposed in a server remote from the vehicle. **Claim 15** The system according to claim 13 or 14, configured to execute the method according to any one of claims 1 to 11.

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