Tire pressure loss detection

Abstract

A method of detecting a pressure loss event in an evaluated vehicle tire, the method comprising: obtaining real-time tire pressure data from a plurality of tire mounted sensors (TMSs), each mounted in one of a plurality of tires on the same vehicle, the tires including an evaluated tire; calculating a rate of pressure change over a fixed length of time window for each of the plurality of tires on the vehicle; comparing the rate of change in pressure of the evaluated tire to one or more of the rates of change in pressure of other tires of the plurality of tires on the vehicle; it is determined from the comparison whether the evaluated tire has an abnormal rate of pressure change indicative of a pressure loss event in the tire.

Description

Technical Field

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

[0002] Being able to detect pressure loss events in vehicle tires that may be associated with punctures is important to vehicle safety.An accurate measurement of tire pressure can be obtained through the use of sensors mounted inside the tire.

[0003] Sensors mounted inside a tire may be referred to as tire mounted sensors (TMS). TMS are used to monitor some parameters of the tire itself, such as tire pressure and tire temperature, as well as to extract information about the interaction of the tire with its surroundings, such as the road or vehicle.

[0004] Current pressure loss detection methods monitor the pressure in a vehicle's tires and compare the measured pressure to a threshold value to determine if the tire pressure is too low, which could indicate a puncture.

[0005] The problem with this approach is that since the tire's pressure must drop below a fixed threshold before a pressure loss event is detected, it may be too late for the driver to take any proactive action, such as driving to a tire repair shop. Alternatively, an earlier warning can be provided by setting a higher threshold, but doing so increases the likelihood of a false positive, inconvenience to the user.

[0006] There remains a need for an improved method for reliable early detection of tire pressure loss events. Summary of the invention

[0007] According to a first aspect of the present invention, there is provided a method of detecting a pressure loss event in a vehicle tire being evaluated, the method comprising:

[0008] obtaining real-time tire pressure data from a plurality of tire-mounted sensors (TMS) respectively mounted on one of a plurality of tires on the same vehicle, the tires including the tire being evaluated;

[0009] Calculating a rate of change of pressure of each of a plurality of tires on a vehicle over a fixed length time window;

[0010] comparing the rate of change of pressure of the tire being evaluated to one or more of the rates of change of pressure of other tires in the plurality of tires on the vehicle;

[0011] A determination is made based on the comparison whether the tire being evaluated has an abnormal rate of pressure change that may be indicative of a pressure loss event in the tire.

[0012] Those skilled in the art will appreciate that by comparing the rate of change of pressure of the tire being evaluated with the rates of change of pressure of other tires on the same vehicle, a pressure loss event, such as a puncture, can be detected more quickly than by comparing tire pressure or rate of change of pressure to a fixed threshold value. Furthermore, the method reliably achieves this rapid detection, whereas in order to achieve rapid detection by using a fixed threshold method, the threshold would have to be set with a low tolerance, thereby increasing the likelihood of a false positive.

[0013] It will be appreciated that an abnormal rate of pressure change of the tire being evaluated is a rate of pressure change that does not substantially match the rate of pressure change of one or more other tires on the same vehicle. As further described below, this can be determined by repeatedly comparing the rates of pressure change over different fixed-length time windows.

[0014] It will be understood that multiple TMSs are installed in multiple tires, meaning that they are mounted inside the tire, such as attached to the inner liner of the tire. When the TMS is installed inside the tire, it is positioned to directly measure the internal air pressure (and optionally the internal temperature) of the tire. This location provides benefits such as protecting the sensor from damage by curbs and road debris, and providing more accurate tire temperatures, less affected by ambient temperature changes and heat from the braking system.

[0015] In some embodiments, multiple tires are on the same axle of a vehicle. In such embodiments, the rate of change of pressure of the tire being evaluated is compared with one or more of the rate of change of pressure of (one or more) other tires in the multiple tires on the same axle.

[0016] In some embodiments, the method includes moving the fixed length time window forward in time (e.g., a fixed increment, such as five minutes) to the next fixed length time window; and recalculating the pressure change rate over the next fixed length time window for each of the plurality of tires. The method may then repeat the comparing and determining steps to re-evaluate whether the tire being evaluated has an abnormal pressure change rate in the next fixed length time window. These steps may be repeated each time the fixed length time window is moved forward in time, i.e., the fixed length time window is rolled, so that the method continuously determines whether the tire being evaluated has an abnormal pressure change rate. For example, the method may be repeated every five minutes. Repeatedly updating the calculation of the pressure change rate at a relatively high frequency increases the likelihood of quickly detecting any pressure loss event.

[0017] In some embodiments, the fixed increment can be reduced in the event that a pressure loss is detected. For example, the fixed increment can be reduced to two minutes. This reduction in the fixed increment will enable faster recalculation of the rate of change of pressure in order to confirm the presence of a pressure loss event, or to identify a false positive. Additionally, in embodiments that calculate a remaining time, a faster recalculation of the rate of change of pressure can increase the accuracy of the remaining time.

[0018] In some embodiments, the method includes moving the fixed-length time window forward in time by a fixed increment that is less 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 may be 5 minutes.

[0019] In some embodiments, calculating the pressure change rate over the fixed length time window includes evaluating an average of the pressure change rate. The average can be calculated as the total change in pressure data over the fixed length time window divided by the length of the fixed length time window.

[0020] In some embodiments, subwindows are applied to the pressure data within a fixed length time window, and an average pressure is calculated for the subwindows. This can mitigate oscillations in the pressure data and reduce the effects of erroneous measurements. In one embodiment, the length of the subwindow can be 10 minutes. By calculating the average pressure value for each subwindow, the pressure data obtained within the fixed length time window can be converted into a series of average pressure values.

[0021] In some embodiments, the pressure change rate (PCR) can be calculated as the slope m of a least squares first degree 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 a least squares first degree polynomial fit of the average pressure values ​​within the fixed length time window. As described above, the fixed length time window can have a fixed length dt, such as 30 minutes.

[0022] In some embodiments, the method further includes obtaining real-time tire temperature data corresponding to the real-time tire pressure data, and normalizing the real-time tire pressure data using the real-time tire temperature data to remove the effects of any temperature changes, wherein the calculated pressure change rate is a normalized pressure change rate. The real-time tire temperature data can be obtained from any sensor capable of measuring or estimating internal tire temperature.

[0023] In some implementations, both a normalized rate of pressure change and a non-normalized rate of pressure change are calculated for the tire being evaluated.

[0024] Removing the effects of temperature variations has the effect of removing large fluctuations in the pressure itself. Therefore, the normalised pressure is a more reliable indication of pressure loss due to a puncture, rather than giving a false positive due to, for example, a pressure loss caused by a significant drop in temperature.

[0025] In some implementations, normalizing the real-time tire pressure data includes dividing a pressure value of the real-time tire pressure data by a corresponding temperature value of the real-time tire temperature data.

[0026] In some embodiments, real-time tire temperature data is obtained from a plurality of tire mounted sensors (TMS) mounted inside a plurality of tires. In a preferred embodiment, real-time tire temperature data is obtained from the same plurality of TMS used to obtain real-time tire pressure data.

[0027] Using the internal tire temperature retrieved from the TMS instead of the external temperature allows for improved accuracy of pressure loss detection, being less affected by atmospheric events. Additionally, measuring internal tire pressure and temperature using the same TMS ensures that temperature and pressure readings are taken under exactly the same conditions, and therefore the pressure reading can be accurately normalized by the temperature reading.

[0028] In some embodiments, the rate of change of pressure of the tire being evaluated is compared to a pressure rate of change threshold, and only if the rate of change of pressure of the tire being evaluated is the rate of change of pressure of the tire being evaluated compared to the pressure rates of change of the other tires. In some embodiments, only the normalized rate of change of pressure is compared to the normalized rate of change of pressure threshold. In an alternative embodiment, the normalized rate of change of pressure is compared to the normalized rate of change of pressure threshold, and the non-normalized rate of change of pressure is compared to the non-normalized rate of change of pressure threshold.

[0029] In an embodiment, the pressure change rate threshold is approximately zero, so that only negative pressure change rates corresponding to pressure losses are compared to the pressure change rates of the other tires.

[0030] In embodiments where both the normalized pressure rate of change and the non-normalized pressure rate of change are compared to respective thresholds, the normalized pressure rate of change threshold and the non-normalized pressure rate of change threshold may be approximately zero. In one set of embodiments, the normalized pressure rate of change 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 rate of change threshold is equal to 0.

[0031] By analyzing only the negative pressure change rate, ie, the pressure loss rate, the required processing volume is reduced.

[0032] In an embodiment, the comparison between the tire being evaluated and the other tires is performed by calculating an average pressure change rate (PCR) value from the PCR of the other tires and comparing the pressure change rate (PCR) of the tire being evaluated to the average PCR value. This is known as a "leave one out" comparison, where PCR is an abbreviation for "pressure change rate".

[0033] In an embodiment, when it is determined that the evaluated tire has an abnormal pressure change rate, the method further comprises calculating a remaining time before reaching a critical pressure value in the evaluated tire. In an 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.

[0034] The method may also include displaying the remaining time to the user. In some embodiments, the user may be a driver of the vehicle, and the remaining time may be displayed to the driver via a driver terminal located in the vehicle or via an application installed in the driver's mobile phone. In additional or alternative embodiments, the user may be a fleet manager, and the remaining time may be displayed to the fleet manager via a fleet manager application installed on the fleet manager's computer.

[0035] Displaying the remaining time to the user allows the user to make decisions about what steps to take next. For example, based on the remaining time, the user can determine whether they can complete their journey or whether they need to take a detour to a tire repair shop.

[0036] In an embodiment, the method may make recommendations to the user, such as providing the location of a tire repair shop that they can reach within the remaining time before the tire pressure reaches a critical value.

[0037] In an embodiment, the method includes comparing the remaining time to a remaining time threshold and notifying the user with an alert only if the remaining time is below the threshold. In such an embodiment, if the remaining time is greater than the threshold, the remaining time may still be displayed to the user without issuing an explicit alert. In an embodiment, when the remaining time is greater than the remaining time threshold, the method may use more time (e.g., 5 hours) to improve the estimate of the pressure change rate and the remaining time in order to check whether the detected pressure loss event is a false positive.

[0038] Any method disclosed herein may be a computer-implemented method.

[0039] According to a second aspect of the present invention, there is provided a computer-readable storage medium storing firmware code, which, when executed on a data processor, executes the method of the first aspect.

[0040] According to a third aspect of the present invention, there is provided a pressure loss event detection system, comprising:

[0041] Multiple tire mounted sensors (TMS) for collecting real-time tire pressure data from multiple tires on the same vehicle; and

[0042] A processor, wherein the processor is configured to:

[0043] obtaining real-time tire pressure data from a plurality of tire mounted sensors (TMS), including a tire to be evaluated among a plurality of tires on a vehicle;

[0044] calculating a rate of change of pressure over a fixed length time window for each of a plurality of tires;

[0045] comparing the rate of change of pressure of the tire being evaluated to one or more of the rates of change of pressure of other tires in the plurality of tires; and

[0046] A determination is made based on the comparison whether the tire being evaluated has an abnormal rate of pressure change that may be indicative of a pressure loss event in the tire.

[0047] The processor may be located in the vehicle and configured to obtain real-time tire pressure data directly from a plurality of tire-mounted sensors. In some embodiments, the processor is located in a remote server and configured to obtain real-time tire pressure data indirectly from a plurality of tire-mounted sensors, such as by receiving real-time tire pressure data transmitted to the processor by a network communication device installed in the vehicle. The remote server is a server that is remote from the vehicle.

[0048] In an embodiment, the system comprises one or more user output devices. The user output device may comprise one or more of the following: a driver terminal inside the vehicle, a mobile phone and / or a fleet manager's computer.

[0049] It will of course be understood that the term "server" as used herein means a computer or machine (e.g., a server device) connected to a network so that the server sends and / or receives data from other devices (e.g., computers or other machines) on the 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 some 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 website server, a proxy server, etc. The server can be a single computing device, or it can be a distributed system, that is, the server functions can be divided on multiple computing devices. For example, the server can be a cloud-based server, that is, its functions can be split "on demand" on many computers. In this configuration, server resources can be obtained from one or more data centers that can be located in different physical locations.

[0050] It will therefore be understood that the above process performed by the server can be performed by a single computing device, i.e. a single server, or by multiple separate computing devices, i.e. multiple servers. For example, all processes can be performed by a single server that can access all required relevant information. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] One or more non-limiting examples will now be described, by way of example only, and with reference to the accompanying drawings, in which:

[0052] Figure 1 is a schematic diagram of a pressure loss detection system installed on a heavy cargo carrier;

[0053] Figure 2 is a flow chart showing a pressure loss detection method;

[0054] Figure 3a is a graph of non-standardized tire pressure versus time;

[0055] Figure 3b is a graph of temperature-normalized tire pressure versus time;

[0056] Figure 4a is a graph showing normalized pressure versus time for four different tires over a 1 / 2 hour window in one example;

[0057] Figure 4b is a graph showing normalized pressure versus time for four different tires over a later 1 / 2 hour window;

[0058] Figure 4cis a graph showing normalized pressure versus time for four different tires over an even later 1 / 2 hour window;

[0059] Figure 5a is a graph showing normalized pressure versus time for two different tires over a 1 / 2 hour window in another example;

[0060] Figure 5b It is shown in the included Figure 5a Graphs of normalized pressure versus time for two different tires over a 1 / 2 hour window and a 5 hour window as shown;

[0061] Figure 6 An example of output that may be provided to a user is shown. DETAILED DESCRIPTION

[0062] Figure 1 A system 1 for detecting a pressure loss event in a vehicle tire 3 mounted on a vehicle 2 is shown. The system 1 includes a plurality of tire mounted sensors (TMS) 5, each mounted within the tire 3, and a network communication device 6 disposed in the vehicle 2. In an embodiment, the network communication device may be a dongle that is plugged into a port of the vehicle 2, such as an OBD port, an FMS port, or other port. In an alternative embodiment, the network communication device 6 may be a permanently installed transceiver box. The TMS 5 is configured to communicate with a remote server 7 via the dongle 6 in the vehicle 2. In this example, the TMS 5 communicates with the remote server 7 via the dongle 6 in the vehicle 2. The vehicle 2 is connected to a network communication device 6, but it will be understood that any suitable form of short-range wireless communication or wired connection may be used. The network communication device 6 is networked and communicates with a 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: a user terminal 10a provided inside the cabin of the vehicle 2, a user mobile device 10b that can be carried by a user, and a computer 10c of a team manager.

[0063] Now refer to Figure 2 The operation of the system 1 is explained in the flowchart of FIG. 1. In step 101, the TMS 5 acquires pressure and temperature data from the inside of the vehicle tire 3 over a period of time corresponding to a fixed length time window, such as 30 minutes. In this example, the pressure and temperature data are sent to the network communication device 6, which in turn sends the data to the remote server 7. In this example, the remote server 7 runs the following algorithm, which first normalizes the tire pressure reading to remove the effect of temperature in step 103. According to equation 1, the normalized pressure P' is obtained from the raw pressure P and the internal temperature T.

[0064]

[0065] Figure 3a and Figure 3b Some non-standardized pressure and standardized pressure readings are shown, plotted against time, over a period of about 32 hours. Figure 3b ) than the non-standardized pressure curve ( Figure 3a ) is much flatter and can therefore be relied upon more reliably for monitoring pressure changes due to possible punctures, since fluctuations due to temperature will only serve to interfere with the detection of punctures.

[0066] Since the interior temperature is less affected by atmospheric events than the temperature measured at a point outside the tire, the interior temperature is used to normalize the pressure data to improve the accuracy of pressure loss detection.

[0067] exist Figure 2 Before or after the normalization step 103 in , the pressure values ​​obtained over the time period can be smoothed or averaged in a suitable manner. This can include, for example, subwindows (e.g., 10 minutes) within a fixed length time window (e.g., 30 minutes) of the application time period. The subwindows are used to generate a rolling average from the pressure values ​​(normalized and / or unnormalized) within the subwindows. This is done to mitigate oscillations in the pressure values, thereby removing the influence of erroneous values. This means that what is considered at each moment is not the instantaneous pressure, but the average value based on the last subwindow (e.g., 10 minutes).

[0068] The algorithm then calculates the rate of change of pressure (PCR) in non-normalized pressure (non-normalized PCR) and the rate of change in normalized pressure (normalized PCR) for all tires in the plurality of tires over a fixed length time window (e.g., 1 / 2 hour window) in steps 105 and 107, respectively. In the illustrated embodiment, the plurality of tires are all mounted on the same axle of the vehicle, but it will be appreciated that this is an optional feature and the plurality of tires may simply be mounted on the same vehicle.

[0069] The standardized PCR is calculated as the slope m of the least squares first-order polynomial fit of the average standardized pressure value within a fixed-length time window. Similarly, the non-standardized PCR is calculated as the slope m of the least squares first-order polynomial fit of the average non-standardized pressure value within a fixed-length time window. Then, at step 109, the algorithm compares the standardized PCR and the non-standardized PCR with the standardized PCR threshold and the non-standardized PCR threshold, respectively. The thresholds are set to be close to zero, and if the standardized PCR and the non-standardized PCR are below their respective thresholds, that is, excluding any substantially positive pressure change rate, the comparison is satisfied. This increases the possibility that the comparison is satisfied only for pressure loss events (where both pressure and standardized pressure decrease over the time window) and not for pressure increases. If the pressure has increased, there is no need to further analyze the data to detect a pressure loss event, so no further analysis is performed. In some embodiments, the value of the non-standardized PCR threshold and the value of the standardized PCR threshold are set to 0 and 0.005, respectively.

[0070] 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 an embodiment, the comparison of step 109 may be omitted, and the algorithm may proceed directly from the calculation of the PCRs performed at steps 105 and 107 to the difference calculation of step 111.

[0071] At steps 111 and 113, the algorithm uses statistical methods (which are explained in detail below) to identify significant differences between the normalized PCRs of the tire being evaluated and the normalized PCRs of other tires on the same axle, and extract abnormal PCRs that may be associated with pressure loss events such as tire punctures. Figure 4a , Figure 4b and Figure 4c Three different time window positions are shown, which compare the normalized pressures on the four tires of the drive axle when one of them has a puncture. By using a sliding window of 30 minutes length, it is clear that Figure 4a The pressure change rate (PCR) of the tire in position "DriveExtR" is mostly consistent with the PCR values ​​of the tires in other positions until about 10:26, when Figure 4b The time window has moved forward and the PCR of the tire in position "DriveExtR" has diverged from the PCR values ​​of the tires in the other positions by an order of magnitude at a time of about 10:34 and at Figure 4cWhen the time window is moved forward to look at the time up to about 10:42, the difference increases further to two orders of magnitude. By repeating its analysis over subsequent time windows, the algorithm continually looks for any anomalies in the Normalized Rate of Pressure Change (PCR) of a particular tire compared to other tires on the same axle, indicating that a puncture has occurred in that tire.

[0072] To identify outlier PCRs in this example, a "leave one out" approach is used to perform an on-axis comparison. For the tire being evaluated, the algorithm computes the difference between its PCR and the average PCR of other tire locations on the same axle. Statistical means can then be used to define anomalies in the distribution of PCR differences generated between the average PCRs. In one embodiment, anomalies are defined by simply looking for outliers in the distribution of PCR differences. An outlier here is a PCR difference that is sufficiently far away from other outliers, where the sufficiency of the difference between the computed PCR differences is based on the interquartile range. More specifically, an outlier is any observation in the distribution that is outside the following range:

[0073] [Q1-k(Q3-Q1); Q3+k(Q3-Q1)] Equation 2

[0074] where Q1 and Q3 are the lower and upper quartiles, respectively, and k is a constant usually set to 1.5.

[0075] The outlier PCR is the PCR for the current fixed-length time window where the difference between the PCR and the average PCR of other tire positions on the same axle is an outlier in the distribution of on-axle PCR differences.

[0076] To make the distribution of PCR differences broader and more representative, the coaxial PCR differences calculated in the current fixed-length time window and the coaxial PCR differences calculated in the previous window using the same fixed-length size can be used.

[0077] If the normalized PCR is not identified as abnormal, then an output of "OK" is issued to the user at step 112. If an abnormal PCR has been identified, and the tire position that produced such a difference in its PCR when compared to other tire positions on the same axle, then the algorithm optionally estimates at step 115 the remaining time for that tire position before the pressure reaches a critical condition, expressed in days and / or hours.

[0078] In an embodiment, the remaining time is calculated mathematically using the following equation:

[0079]

[0080] Where T 余留is the remaining time, P is the current pressure, P 临界 is the critical pressure, and m is the PCR, which is calculated as the slope of the least squares first-degree polynomial fit of the average pressure values ​​within a fixed-length time window of the time period.

[0081] In an embodiment, for TBR (Truck and Bus Radial) tires, the critical pressure may be defined as 6 bar. Alternatively, the critical pressure may be set to 60% of the standard inflation pressure. The estimated remaining time makes it possible to define the criticality / dangerousness (speed) of the pressure loss.

[0082] At step 116, the remaining time associated with the abnormal PCR is output to the user. At step 117, the remaining time is compared to a remaining time threshold. If the remaining time is below the threshold, the user is notified with an alarm at step 119. If the remaining time is greater than the remaining time threshold, the amplitude of the pressure change rate is low, and therefore the pressure loss detection may be a false positive. Therefore, in this scenario, more time (e.g., 5 hours) is allowed to re-evaluate the PCR in multiple subsequent time windows and improve the estimate of the remaining time or verify whether the criticality is a false positive. In this way, the method loops back to the first step 101 and shifts the time period forward (e.g., 5 minutes). In this way, more pressure and temperature readings are taken, and the method is repeated for subsequent time windows, as shown by arrow 118.

[0083] Of course, it will be appreciated that the process described will be performed a number of times, with each tire 3 on the axle taking turns being the tire being evaluated.

[0084] In the algorithm of the illustrated embodiment, the length of the time window for PCR comparison has been set to 30 minutes. The algorithm repeats its evaluation in a subsequent time window shifted forward by 5 minutes. In an embodiment, the defined threshold for emergency alerts is set to 12 hours.

[0085] The TMS pressure loss detection algorithm described herein has been developed using experiments performed on multiple tires and taking into account two main accuracy factors: the number of false positives and the time required to detect a leak as a percentage of the time required for the pressure to reach a critical value.

[0086] The experiment involved three vehicles 2, each running over 75,000 km with different types of loads. To conduct the experiment, the TMS pressure loss detection algorithm was run at a frequency of once every 5 minutes. Two types of pressure loss due to puncture were observed:

[0087] - In the first puncture, the leak was detected 2 minutes from its occurrence, when the pressure had dropped by 0.3 bar (ie a relatively "fast" puncture).

[0088] - In the second puncture, a leak was detected 16 minutes from its occurrence, when the pressure had dropped by only 0.15 bar (ie a relatively "slow" puncture).

[0089] There were no false positives and the detection time was within 10% of the time to critical in both cases.

[0090] Figure 5a and Figure 5b A graph of normalized pressure versus time for a pressure loss event detected by the algorithm is shown. The algorithm relies on a 30 minute sliding window in which data is taken by TMS 5. It is the TMS data taken during each 30 minute window in which the coaxial comparison is performed. The window moves forward by a fixed amount of minutes each time, for example 5 minutes. Figure 5a The 30 minute time window in which an abnormal PCR is first detected is shown. The differences in PCRs for different tire positions on the steering axis are apparent, with tire position SteeringL being seen to correspond to a large rate of pressure change. For a PCR of this severity, the remaining time to critical is below a fixed threshold (12 hours in the illustrated embodiment), so an alarm will be triggered immediately. Figure 5b Normalized pressure over a five-hour window including the initial detection is shown. Figure 5b As shown, the 30 minute detection window corresponds to the onset of tire air leakage, after which the tire air leakage increases its speed and reaches a pressure value of 2 bar in approximately two and a half hours.

[0091] While in the above example, the algorithm is described as being deployed in the remote server 7 (e.g., in a processor thereof), in other embodiments, the network communication device 6 may include a processor for running the algorithm in the vehicle 2. In such an embodiment, the network communication device 6 may then communicate any remaining time estimates or warnings directly to the user terminal 10a disposed in the vehicle 2, bypassing the requirement for a network connection. In such an embodiment, the output of the algorithm may still be sent to the remote server 7 via the network, so that the output may be sent 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 performed by the algorithm may be split between the processor of the network communication device 6 and the processor of the remote server 7 to share the data processing burden and reduce some of the data transmission burden.

[0092] Figure 6The output of the algorithm is shown which can be displayed on any of the user terminals 10a, 10b, 10c. For tires with non-abnormal PCRs, the output "OK" is displayed. For those tires 2L2 and 4R1 with abnormal PCRs, the remaining time to critical is displayed. Figure 6 In the example shown, the remaining time for tire 4R1 is 96 hours. Since this is above the 12 hour threshold, no alert will be issued for this tire and the PCR will be re-evaluated to confirm or correct the detection of the puncture. The remaining time for tire 2L2 is only 2.8 hours, so an alert will be issued to the user.

[0093] It is contemplated that the method may also include the step of issuing instructions to the user regarding the next step. For example, the system of the present invention may be integrated with a navigation system and may identify a tire repair shop that the user will be able to reach within the remaining time. The method may further include making an automatic reservation at a tire repair shop, or in the case of a less severe puncture, maintenance may be scheduled.

Claims

1. A method for detecting a pressure loss event in a vehicle tire being evaluated, the method comprising: obtaining real-time tire pressure data from a plurality of tire-mounted sensors (TMS) respectively mounted on one of a plurality of tires on the same vehicle, the tires including the tire being evaluated; calculating a rate of change of pressure for each of a plurality of tires on the vehicle over a fixed length time window; comparing the rate of change of pressure of the evaluated tire to one or more of the rates of change of pressure of other tires of the plurality of tires on the vehicle; A determination is made based on the comparison whether the evaluated tire has an abnormal rate of pressure change that may be indicative of a pressure loss event in the tire.

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

3. The method according to claim 1 or claim 2, comprising: Moving the fixed-length time window forward in time to the next fixed-length time window; as well as The rate of change of pressure over the next fixed-length time window is recalculated for each of the plurality of tires.

4. The method according to any one of the preceding claims, wherein the method further comprises obtaining real-time tire temperature data corresponding to the real-time tire pressure data, and normalizing the real-time tire pressure data by using the real-time tire temperature data to remove any effect of temperature changes, wherein the calculated pressure change rate is a normalized pressure change rate. 5 . The method of claim 4 , wherein normalizing the real-time tire pressure data comprises dividing a pressure value of the real-time tire pressure data by a corresponding temperature value of the real-time tire temperature data.

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

7. A method according to any one of the preceding claims, wherein the pressure change rate of the evaluated tire is compared with a pressure change rate threshold, and only if the pressure change rate of the evaluated tire is below the threshold, the pressure change rate of the evaluated tire is compared with the pressure change rates of other tires.

8. The method of claim 7, wherein the pressure change rate threshold is approximately zero such that only negative pressure change rates corresponding to pressure losses are compared to the pressure change rates of other tires.

9. A method according to any one of the preceding claims, wherein the comparison between the evaluated tire and the other tires is performed by calculating an average pressure change rate value for the other tires and comparing the pressure change rate of the evaluated tire with the average pressure change rate value.

10. A method according to any preceding claim, further comprising, after determining that the assessed tyre has an abnormal rate of pressure change, calculating a remaining time before reaching a critical pressure value in the assessed tyre, optionally including displaying the remaining time to a user.

11. The method of claim 10, further comprising comparing the remaining time to a remaining time threshold, and notifying a user with an alarm only if the remaining time is below the threshold.

12. A computer readable storage medium storing firmware code which, when executed on a data processor, performs the method according to any one of the preceding claims.

13. A pressure loss event detection system, comprising: Multiple tire mounted sensors (TMS) for collecting real-time tire pressure data from multiple tires on the same vehicle; and A processor, wherein the processor is configured to: obtaining the real-time tire pressure data from the plurality of tire mounted sensors (TMS), including a tire to be evaluated among a plurality of tires on the vehicle; calculating, for each of the plurality of tires, a rate of change of pressure over a fixed length time window; comparing the rate of change of pressure of the tire being evaluated to one or more of the rates of change of pressure of other tires in the plurality of tires; and A determination is made based on the comparison whether the evaluated tire has an abnormal rate of pressure change that may be indicative of a pressure loss event in the tire.

14. The system of claim 13, wherein the processor is located in a server remote from the vehicle.

15. A system according to claim 13 or claim 14, configured to perform a method according to any one of claims 1 to 11.

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