Anomaly detection device, program, and anomaly detection method

Abstract

An abnormality detection device, program, and method are provided that can determine brake abnormalities using a simple method based on information detectable by TPMS. [Solution] The abnormality determination device (10) includes an acquisition unit (131) that acquires input data including the tire internal temperature detected by a detection device (70) mounted on a vehicle (20), a calculation unit (132) that determines the mounting position of the tire on the vehicle where the tire internal temperature was detected based on the input data and classifies the tires into groups according to their mounting position, and a determination unit (133) that compares the tire internal temperatures of the multiple tires classified into groups and determines that an abnormality in the vehicle's brakes has occurred if a discrepancy of more than a threshold occurs in the tire internal temperatures.

Description

Technical Field

[0001] The present disclosure relates to an abnormality determination device, a program, and an abnormality determination method.

Background Art

[0002] Conventionally, a technology has been proposed that detects the temperature of a vehicle brake (brake temperature) and determines an abnormality of the brake based on the detected brake temperature. The brake abnormality is, for example, that the piston of a wheel cylinder or a brake caliper is stuck due to rust or the like, and causes brake drag in which the braked state continues even when the driver is not depressing the brake pedal. For example, Patent Document 1 discloses an apparatus including a calculation unit that calculates a temperature variation amount of the brake temperature, a timing determination unit that determines a determination timing based on the temperature variation amount, and a determination execution unit that determines occurrence of a drum brake abnormality based on the brake temperature at the determination timing.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Here, the apparatus of Patent Document 1 needs to attach a temperature sensor for detecting the temperature of the drum brake to each of the plurality of drum brakes. Therefore, there is a problem that the cost of countermeasures increases for brake abnormalities that generally occur infrequently. Here, in order to monitor the state of tires mounted on a vehicle, a tire pressure monitoring system (TPMS) is often provided in the vehicle.

[0005] In view of these circumstances, the purpose of this disclosure is to provide an abnormality detection device, program, and abnormality detection method that can determine brake abnormalities using a simple method based on information detectable by a TPMS. [Means for solving the problem]

[0006] (1) An abnormality detection device according to one embodiment of the present disclosure, An acquisition unit that acquires input data including the tire temperature detected by a detection device mounted on the vehicle, A calculation unit that determines the mounting position of the tire on the vehicle based on the input data, and classifies the tire into groups according to the mounting position, The system includes a determination unit that compares the internal temperatures of each of the multiple tires classified into the aforementioned groups, and determines that a brake malfunction has occurred in the vehicle if a discrepancy of the internal tire temperatures exceeds a threshold. This configuration allows for the detection of brake abnormalities using a simple method based on information detectable by the TPMS.

[0007] (2) As one embodiment of the present disclosure, in (1), The aforementioned tires are classified into the groups of front wheels, inner rear wheels, or outer rear wheels. This configuration allows for accurate detection of brake abnormalities, even when dealing with vehicles such as trucks that have two wheels.

[0008] (3) In one embodiment of the present disclosure, in (1) or (2), The aforementioned threshold is 20°C. This configuration reduces misjudgments and allows for accurate detection of brake malfunctions.

[0009] (4) In one embodiment of the present disclosure, in any of (1) to (3), The input data includes information on the vehicle's position or information on the vehicle's speed. The determination unit does not make a determination regarding any abnormality in the vehicle's brakes when the vehicle is stopped or for a certain period of time after it starts moving. This configuration allows for accurate detection of brake abnormalities, excluding periods when brake heat is not generated and noise caused by direct sunlight or road surface temperature.

[0010] (5) A program according to one embodiment of the present disclosure is In the abnormality detection device, This involves acquiring input data, including the tire temperature detected by a detection device mounted on the vehicle, and Based on the input data, the mounting position of the tire whose internal temperature has been detected on the vehicle is determined, and the tire is classified into a group according to the mounting position. The system is configured to compare the internal temperatures of each of the multiple tires classified into the aforementioned groups, and to determine that a brake malfunction has occurred in the vehicle if a discrepancy of more than a threshold value is found in the internal tire temperatures. This configuration allows for the detection of brake abnormalities using a simple method based on information detectable by the TPMS.

[0011] (6) An abnormality determination method according to one embodiment of the present disclosure is An anomaly detection method performed by an anomaly detection device, The abnormality detection device, This involves acquiring input data, including the tire temperature detected by a detection device mounted on the vehicle, and Based on the input data, the mounting position of the tire whose internal temperature has been detected on the vehicle is determined, and the tire is classified into a group according to the mounting position. This includes comparing the internal temperatures of each of the multiple tires classified into the aforementioned groups, and determining that a brake malfunction has occurred in the vehicle if a discrepancy of more than a threshold value occurs in the internal temperatures of the tires. This configuration allows for the detection of brake abnormalities using a simple method based on information detectable by the TPMS.

Effects of the Invention

[0012] According to the present disclosure, it is possible to provide an abnormality determination device, a program, and an abnormality determination method that can determine a brake abnormality by a simple method based on information detectable by a TPMS.

Brief Description of the Drawings

[0013] [Figure 1] Figure 1 is a diagram showing a configuration example of an abnormality determination system including an abnormality determination device according to an embodiment of the present disclosure. [Figure 2] Figure 2 is another diagram showing a configuration example of the abnormality determination system of Figure 1. [Figure 3] Figure 3 is a diagram for explaining classification of tire mounting positions. [Figure 4] Figure 4 is a diagram illustrating grouping using the median value of temperature. [Figure 5A] Figure 5A is a graph showing an image of the distribution of temperatures of other tires relative to the temperature of a reference tire for all tires. [Figure 5B] Figure 5B is a graph showing an image of the distribution of temperatures of other tires relative to the temperature of a reference tire for front wheels. [Figure 5C] Figure 5C is a graph showing an image of the distribution of temperatures of other tires relative to the temperature of a reference tire for rear inner tires. [Figure 5D] Figure 5D is a graph showing an image of the distribution of temperatures of other tires relative to the temperature of a reference tire for rear outer tires. [Figure 6] Figure 6 is a diagram for explaining determination of occurrence of a brake abnormality. [Figure 7] Figure 7 is an example of a flowchart showing processing of the abnormality determination method according to an embodiment of the present disclosure.

Modes for Carrying Out the Invention

[0014] As used herein, “comprise,” “comprising,” “comprises,” “include,” “including,” “includes,” “have,” “has,” “having,” or variations thereof are open-ended and include one or more described features, integers, elements, steps, components, or functions, but do not exclude the presence or addition of one or more other features, integers, elements, steps, components, functions, or groups thereof.

[0015] Well-known functions or configurations may not be described in detail for the sake of brevity and / or clarity. Where the term “or” is used (e.g., A or B), it is intended to mean “A or B, or both.” When “only A or B, but not both” is intended, the term “only A or B but not both” is used. Thus, the use of the term “or” in this specification is inclusive and not exclusive.

[0016] Hereinafter, an abnormality detection device 10 (see Figure 1), program, and abnormality detection method according to one embodiment of the present disclosure will be described with reference to the drawings. In each figure, the same or corresponding parts are denoted by the same reference numerals. In the description of this embodiment, the description of the same or corresponding parts will be omitted or simplified as appropriate.

[0017] Figures 1 and 2 show examples of the configuration of an abnormality detection system. The abnormality detection system is composed of at least an abnormality detection device 10. Figure 1 is a block diagram including an example of the internal configuration of the abnormality detection device 10 and an example of the configuration of the device mounted on the vehicle 20. Figure 2 shows the overall configuration of the abnormality detection system.

[0018] The abnormality detection device 10 according to this embodiment can detect brake abnormalities using a simple method described later. Brake abnormalities include brake drag, in particular, where the brakes remain applied for an extended period. Brake drag can occur, for example, when the piston of the wheel cylinder or brake caliper becomes stuck due to rust or other causes.

[0019] Here, a technology is known in which a TPMS detects high temperatures caused by brake malfunctions and notifies the vehicle manager or driver of a high temperature alarm. However, while a TPMS with a rim band type sensor can detect heat transmitted from the brakes through metal, it is difficult for a TPMS with a patch type sensor or a ball type sensor to detect brake heat. In other words, depending on the type of TPMS, brake malfunctions may not be detected by a high temperature alarm. Furthermore, even with a TPMS with a rim band type sensor, it is difficult to appropriately set a threshold for determining abnormally high temperatures. For example, if the threshold is set to 100°C, abnormally high temperatures will be detected a considerable time after the brake malfunction, which may delay responses such as stopping the vehicle 20. Also, if the threshold is set too low, there is a risk of false detection of abnormally high temperatures when the tires 30 are at a high temperature during normal driving. Moreover, even if the threshold is adjusted so as not to be too low, the temperature of the tires 30 fluctuates depending on the mounting position and load conditions. Therefore, it is difficult to completely eliminate false detections. The method of the malfunction determination device 10 according to this embodiment, which will be described later, can solve these problems of the conventional technology.

[0020] Here, vehicle 20 may be, for example, a passenger car, truck, bus, or construction vehicle, and is not limited to a specific type of mobile body. In this embodiment, vehicle 20 is described as a truck that transports cargo. Also, in order to avoid duplication of illustrations in Figures 1 and 2, only one vehicle 20 is shown, but the abnormality detection system may be composed of multiple vehicles 20.

[0021] The abnormality detection device 10 comprises a communication unit 11, a storage unit 12, and a control unit 13. The control unit 13 comprises an acquisition unit 131, a calculation unit 132, a determination unit 133, and an output unit 134. The abnormality detection device 10 may be, for example, a computer as its hardware configuration. The computer may be a server computer or a portable computer such as a laptop or tablet. Details of the components of the abnormality detection device 10 will be described later. In this embodiment, the abnormality detection device 10 is a computer in a management facility that manages the status of a vehicle 20 (a truck that transports cargo) and tires 30. Here, the abnormality detection device 10 is not a single device, but may consist of multiple devices located in multiple locations that can send and receive data from each other via a network 40. In other words, multiple devices connected by the network 40 may function as the abnormality detection device 10 shown in Figure 1 as a whole. Therefore, for example, the abnormality detection device 10 may consist of a single computer as its hardware configuration, or it may consist of multiple computers connected by a network 40. In the case of a system composed of multiple computers, the storage unit 12 may be a shared memory accessible by each computer.

[0022] The abnormality detection device 10 may constitute an abnormality detection system together with devices mounted on the vehicle 20 connected via the network 40 (detection device 70 and in-vehicle communication device 80). The network 40 is, for example, the internet. The network 40 may also be configured to include, for example, a LAN (Local Area Network) in part. Here, the abnormality detection system may further include a terminal device 50 used by an administrator (the person in charge of the management facility who manages the status of the vehicle 20 and tires 30). The terminal device 50 is, for example, a general-purpose mobile terminal such as a smartphone or tablet terminal, but is not limited to these. The terminal device 50 may function as a display unit that displays the judgment result output by the output unit 134 described later or a warning that a brake abnormality has been detected. The abnormality detection system may also include a storage device 90 (a cloud-based storage device 90) on the network 40 as seen from the perspective of the abnormality detection device 10 and the devices mounted on the vehicle 20. In this embodiment, the storage device 90 includes a database that stores information regarding the state of the tires 30 detected by the detection device 70 mounted on the vehicle 20 as time-series data, linked to the vehicle 20 that is the target of detection. In this embodiment, the information regarding the state of the tires 30 includes at least information on the internal temperature of the tires. In addition, in this embodiment, the abnormality determination device 10 obtains information regarding the state of the tires 30 from the database via the network 40 as input information. Here, the information regarding the state of the tires 30 may include not only direct information about the tires 30 (e.g., internal temperature of the tires) but also indirect information (e.g., information on the position of the vehicle 20 or information on the speed of the vehicle 20).

[0023] In this embodiment, the vehicle 20 is equipped with a detection device 70 and an in-vehicle communication device 80. The detection device 70 is a device or in-vehicle system equipped with sensors and used to generate information regarding the state of the tires 30. In this embodiment, the detection device 70 is configured to include a TPMS (Tire Pressure Monitoring System). In this embodiment, the detection device 70 may be configured to include a GPS (Global Positioning System) device. The GPS device can be any device that identifies the position of the vehicle 20. For example, a car navigation system mounted on the vehicle 20 may be used as the GPS device.

[0024] TPMS monitors the pressure (internal pressure) and temperature (internal tire temperature) of the tires 30 mounted on the vehicle 20. TPMS may be configured to include, for example, a sensor installed inside the tire 30, a processor that calculates and outputs the pressure of the tire 30 based on the sensor's detection values, and a memory that stores the sensor's detection values. The sensor may include a pressure sensor and a temperature sensor.

[0025] The in-vehicle communication device 80 is a device that outputs information regarding the state of the tires 30 detected by the detection device 70, so that it is stored in a database. In this embodiment, the in-vehicle communication device 80 is implemented by the communication function of a digital tachograph, but is not limited to this, and may be, for example, a dedicated communication device. In this embodiment, the in-vehicle communication device 80 outputs the position or speed of the vehicle 20 and the temperature of the tires 30 (internal tire temperature) as information regarding the state of the tires 30. However, the in-vehicle communication device 80 does not have to output the position or speed of the vehicle 20, and may output other information as information regarding the state of the tires 30. The information output from the in-vehicle communication device 80 is stored in a database of the storage device 90 on the cloud. Here, the speed of the vehicle 20 may be calculated from the information of the position of the vehicle 20, or it may be directly detected by the detection device 70. For example, the detection device 70 may include a speed sensor, and the measured speed of the vehicle 20 may be output from the in-vehicle communication device 80.

[0026] The components of the abnormality detection device 10 are described below in detail. The communication unit 11 is composed of one or more communication modules connected to the network 40. The communication unit 11 may include communication modules that support mobile communication standards such as 4G (4th Generation) and 5G (5th Generation). The communication unit 11 may also include communication modules that support wired or wireless LAN standards.

[0027] The storage unit 12 is one or more memories. The memories are, for example, semiconductor memories, magnetic memories, or optical memories, but are not limited to these and can be any memory. The storage unit 12 is, for example, built into the anomaly detection device 10, but it is also possible to configure it so that it can be accessed from the outside by the anomaly detection device 10 via any interface.

[0028] The storage unit 12 stores various data used in various calculations performed by the control unit 13. The storage unit 12 may also store the results and intermediate data of various calculations performed by the control unit 13.

[0029] In this embodiment, the storage unit 12 temporarily stores various information from the database of the storage device 90 on the cloud, which is acquired via the communication unit 11. The storage unit 12 may also store vehicle information, which is information related to the vehicle 20 (for example, axle configuration).

[0030] The control unit 13 is one or more processors. The processors are, for example, general-purpose processors or dedicated processors specialized for specific processing, but are not limited to these and can be any processor. The control unit 13 controls the overall operation of the abnormality detection device 10.

[0031] Here, the abnormality detection device 10 may have the following software configuration. One or more programs used to control the operation of the abnormality detection device 10 are stored in the storage unit 12. When the programs stored in the storage unit 12 are read by the processor of the control unit 13, the control unit 13 is made to function as an acquisition unit 131, a calculation unit 132, a determination unit 133, and an output unit 134.

[0032] The acquisition unit 131 acquires input data, including the tire temperature detected by the detection device 70 mounted on the vehicle 20. The input data may include information on the position of the vehicle 20 or the speed of the vehicle 20. In this embodiment, the input data will be described as including information on the speed of the vehicle 20. In this embodiment, the acquisition unit 131 also acquires vehicle information, including the axle configuration of the vehicle 20.

[0033] The calculation unit 132 determines the mounting position of the tire 30 on the vehicle 20 based on the input data, where the internal tire temperature has been detected. In this embodiment, the internal tire temperature is detected for each of the multiple tires 30 mounted on the vehicle 20, and the calculation unit 132 determines the mounting position of each of the multiple tires 30. The calculation unit 132 then classifies the tires 30 into groups according to their mounting positions.

[0034] Here, Figure 3 is a diagram illustrating the classification (i.e., grouping) of the mounting positions of the tires 30. In this embodiment, the mounting positions of the tires 30 are classified into front wheels, inner rear wheels, and outer rear wheels. In other words, the tires 30 are classified into the front wheel, inner rear wheel, or outer rear wheel group by the calculation unit 132. Such grouping makes it possible to accurately determine brake abnormalities even when targeting vehicles 20 such as trucks that have two wheels. Figure 3 shows a bird's-eye view of a 12-wheeled (left) and a 10-wheeled (right) vehicle 20. The tires 30 at mounting positions P1, P2, P7, and P8 are front wheels and are classified as front wheels. The tires 30 at mounting positions P3-P6 and P9-P12 are rear wheels. Of the rear wheels, the tires 30 at mounting positions P5, P6, P11, and P12 are classified as inner rear wheels. Furthermore, the rear tires 30 at mounting positions P3, P4, P9, and P10 are classified as being on the outer rear wheel. The mounting position of the tires 30 does not need to be an individual position, but rather should be divided into the front wheels, inner rear wheels, and outer rear wheels, where the magnitude of the load on the tires 30 differs. As described above, the internal temperature of the tires fluctuates depending on the mounting position and load conditions, but the temperature of tires 30 belonging to the same group fluctuates in the same way. Therefore, if there are tires 30 within the same group that show different internal temperature fluctuations, it is possible to estimate that the high temperature is due to a brake malfunction. The details of the group classification will be explained below with reference to Figures 4, 5A, 5B, 5C, and 5D.

[0035] The calculation unit 132 estimates the front tire group by grouping them using, for example, the median temperature of the tires 30, and verifies the estimated front tire group. Similarly, the calculation unit 132 estimates the rear inner tire group, verifies the estimated rear inner tire group, and designates the remaining tires 30 as the rear outer tire group. Here, we explain assuming that the vehicle 20 has 12 wheels, but the same method can be used to determine if it has 10 wheels. The fact that the target vehicle 20 has 12 wheels can be determined, for example, from vehicle information stored in the memory unit 12.

[0036] Figure 4 is a one-dimensional graph showing the median internal temperatures of each tire 30, arranged from right to left in descending order of temperature. The calculation unit 132 calculates the median temperature for a certain period (e.g., 2 days, 3 days, or one week) from the temperature data of each tire 30 obtained as input data. The calculation unit 132 ranks the tires 30 from the highest median temperature. It is known that during vehicle 20 operation, the internal temperatures of tires 30 within the same group are approximately equal. The calculation unit 132 tentatively estimates the four tires 30 with the highest median temperatures as the front wheel tire group (group G1). The calculation unit 132 may also tentatively estimate the four tires 30 with the highest median temperatures among the remaining eight tires 30 as the rear inner wheel tire group (group G2-2). The calculation unit 132 may also tentatively estimate the other four tires 30 as the rear outer wheel tire group (group G2-1).

[0037] The calculation unit 132 uses the tire with the highest median temperature among the four tires 30 as the reference for each of the three groups described above. In other words, the calculation unit 132 determines the reference front tire, the reference inner rear tire, and the reference outer rear tire. The calculation unit 132 plots the temperature of the reference tire 30 on the horizontal axis (X axis) and the temperature of the other non-reference tires 30 on the vertical axis (Y axis) at the same time (see Figures 5A to 5D). The calculation unit 132 uses the straight line Y=X as the reference line and verifies whether there are any differences in the slope and intercept of the temperature distribution within the group that are greater than a certain amount (i.e., significantly different). If there are no significantly different tires, the calculation unit 132 confirms the provisionally determined group; if there are significantly different tires, it swaps them with the next temperature order of tires 30 and verifies again.

[0038] The verification process will be explained in detail with reference to Figures 5A to 5D. Figure 5A shows all tires 30 plotted as described above, with the reference front tire used as the reference tire 30. There are three groups with different slopes. Figure 5B shows the provisional front tire group (group G1) with the reference front tire plotted on the horizontal axis. The tires 30 in group G1 are distributed almost linearly within a narrow range. In the example of Figure 5B, the calculation unit 132 determines the provisionally defined front tire group (group G1). Figure 5C shows the provisional rear inner tire group (group G2-2) with the reference rear inner tire plotted on the horizontal axis. Figure 5D shows the provisional rear outer tire group (group G2-1) with the reference rear outer tire plotted on the horizontal axis. The calculation unit 132 performs the same verification on the provisional rear inner tire group as on the front tire group. Once the provisionally determined group of inner rear wheel tires (group G2-2) is confirmed through verification, the determination of whether tire 30 is mounted on the front wheel, inner rear wheel, or outer rear wheel will be completed.

[0039] In this way, the calculation unit 132 estimates the group of tires belonging to one of the categories (front wheels, inner rear wheels, outer rear wheels) based on the ranking of the median temperatures of the tires 30 over a certain period. Then, the calculation unit 132 uses one of the estimated tire groups as a reference and performs verification based on the temperature distribution of the other tires 30 relative to the temperature of the reference tire 30. For example, accurate grouping of tires 30 can be performed even without input of individual tire positions by the user.

[0040] The determination unit 133 compares the internal tire temperatures of multiple tires 30 classified by each group after grouping by the calculation unit 132, and determines that a brake malfunction has occurred in the vehicle 20 if a deviation of more than a threshold occurs in the internal tire temperatures. Figure 6 is a diagram illustrating the determination of brake malfunction. For each group, the determination unit 133 acquires information on the internal tire temperatures of multiple tires 30 belonging to the group via the acquisition unit 131 and compares them with each other. In the example in Figure 6, the internal tire temperature of one tire 30 in one group (for example, the front wheel group) is shown by a dashed line, and the internal tire temperatures of other tires 30 that are closest to the internal tire temperature of the dashed tire 30 are shown by solid lines. In the example in Figure 6, at time t1, the internal tire temperature of the tire 30 shown by the dashed line deviates from the internal tire temperatures of the other tires 30 by more than a threshold. As described above, the internal tire temperature fluctuates depending on the mounting position and load state, but the temperatures of tires 30 belonging to the same group fluctuate in the same way. Therefore, if there are tires 30 within the same group that show different fluctuations in internal tire temperature, it can be estimated that high temperature is occurring due to a brake malfunction. Accordingly, in the example in Figure 6, the determination unit 133 determines that a brake malfunction has occurred because a deviation exceeding a threshold value in internal tire temperature occurred at time t1. The internal tire temperature data used in this determination method is information detectable by the TPMS and does not require, for example, a dedicated temperature sensor for detecting the temperature of drum brakes. In other words, the malfunction determination method according to this embodiment can determine brake malfunctions using a simple method based on information detectable by the TPMS.

[0041] Here, the threshold can be 20°C as an example. That is, if the internal temperature of one tire 30 belonging to the same group is 20°C or more different from the internal temperatures of all other tires 30 belonging to that group (compared to any other tire 30), then a brake malfunction may be determined. 20°C is a temperature difference that cannot occur under normal driving conditions. Therefore, by setting the threshold to 20°C, false positives can be reduced, and brake malfunctions can be accurately determined.

[0042] Furthermore, the determination unit 133 does not need to make a determination regarding brake abnormalities of the vehicle 20 when the vehicle 20 is stopped and for a certain period of time after it starts moving, based on the vehicle speed information of the vehicle 20 included in the input data. In the example in Figure 6, period P0 corresponds to the period during which the determination unit 133 does not make a determination. First, when the vehicle 20 is stopped, no brake heat is generated, so it is not possible to determine if there is a brake abnormality. Also, when the vehicle 20 is stopped, a temperature difference may occur between one tire 30 belonging to the same group and the other tires 30 due to differences in direct sunlight or road surface temperature. Therefore, for a certain period of time after the start of driving (for example, 10 minutes), the determination may become inaccurate due to the influence of noise from direct sunlight or road surface temperature. By not making a determination when the vehicle 20 is stopped and for a certain period of time after it starts moving, the determination unit 133 can accurately determine brake abnormalities by excluding the period during which no brake heat is generated and noise from direct sunlight or road surface temperature.

[0043] The output unit 134 may output the determination result from the determination unit 133 (for example, whether or not a brake malfunction has occurred in the vehicle 20) to a terminal device 50 used by the manager of the tire 30. The determination result may include a warning that a brake malfunction has been detected. The output unit 134 may also output a warning to the terminal device 50 only if a malfunction is detected. For example, a manager who sees the warning displayed on the terminal device 50 may contact the driver of the vehicle 20 to stop immediately in order to prevent a vehicle fire. The output unit 134 may also output a warning directly to the driver of the vehicle 20 via the network 40.

[0044] The abnormality detection device 10 according to this embodiment may perform the following abnormality detection method processing. Figure 7 is an example flowchart showing the abnormality detection method processing according to this embodiment.

[0045] First, the acquisition unit 131 acquires input data, including the tire temperature detected by the detection device 70 mounted on the vehicle 20 (step S1).

[0046] Next, the calculation unit 132 determines the mounting position of the tire 30 on the vehicle 20 based on the input data, and classifies the tire 30 into groups according to its mounting position (step S2).

[0047] The determination unit 133 then compares the internal temperatures of each of the multiple tires 30 classified into groups (step S3). If the tires 30 are classified into front wheel, rear inner, or rear outer groups by the calculation unit 132, a comparison is performed for the tires 30 belonging to the front wheels, a comparison is performed for the tires 30 belonging to the rear inner wheels, and a comparison is performed for the tires 30 belonging to the rear outer wheels. In other words, a comparison of the internal temperatures of the tires 30 belonging to each of the three groups is performed (calculation of the discrepancy, i.e., the temperature difference).

[0048] The determination unit 133 determines that a brake malfunction has occurred (step S5) if a deviation of more than a threshold occurs in the internal tire temperature of the tire 30 belonging to any group (Yes in step S4).

[0049] Furthermore, if no deviation exceeding a threshold occurs in any group (No in step S4), the process returns to step S1. In other words, the input data, including the latest tire internal temperature, is acquired, and the brake abnormality detection process continues. Here, if the brake abnormality detection process continues, the tire 30 has already been classified into groups, so the process in step S2 may be omitted. That is, step S3 may be executed immediately following step S1.

[0050] Here, the result of the determination in step S4 may be output to the terminal device 50 or the like by the output unit 134.

[0051] As described above, the abnormality detection device 10, program, and abnormality detection method according to this embodiment can determine brake abnormalities using a simple method based on information detectable by the TPMS.

[0052] In the above descriptions of various embodiments of the concepts of this disclosure, it should be understood that the technical terms used herein are for the sole purpose of describing specific embodiments and are not intended to limit the concepts of this disclosure. Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as those generally understood by those skilled in the art to which the concepts of this disclosure belong. Terms as defined in commonly used dictionaries should be interpreted as having the meaning consistent with their meaning in the context of this specification and related art, and should not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0053] Many variations and modifications can be made to the embodiments without substantially departing from the principles of the concepts of this disclosure. All such variations and modifications are intended to be included herein within the scope of the concepts of this disclosure. Accordingly, the subject matter disclosed above is to be considered illustrative and not restrictive, and the examples of embodiments are intended to encompass all such modifications, extensions, and other embodiments that fall within the spirit and scope of the concepts of this disclosure. Accordingly, to the maximum extent permitted by law, the scope of the concepts of this disclosure should be determined by the broadest acceptable interpretation of this disclosure, including the following claims and their equivalents, and not limited or restricted by the detailed description above.

[0054] For example, in the above embodiment, the abnormality detection device 10 is an external device of the vehicle 20 that is configured to send and receive data via the network 40. As another example, the abnormality detection device 10 may be implemented as an in-vehicle device mounted on the vehicle 20. As yet another example, the abnormality detection system may be implemented inside the vehicle 20. In this case, the network 40 may be an in-vehicle LAN or the like.

[0055] For example, in the above embodiment, the mounting positions of the tires 30 are divided into front wheels, inner rear wheels, and outer rear wheels. As another example, a simple grouping method may be used where the same axle is treated as the same group. For example, in the case of a 2-4D (2 front-4 rear drive) truck, a total of two groups may be used, with the two front wheels forming one group and the four rear wheels forming another group. In other words, grouping is possible without distinguishing between the inner and outer sides of the double tires. However, since there is a temperature difference between the inner and outer sides of the double tires, it is preferable to distinguish between the inner and outer sides of the double tires as in the above embodiment from the viewpoint of achieving highly accurate early determination.

[0056] Exemplary embodiments are described herein with reference to block diagrams and / or flowchart illustrations of computer implementation methods, apparatus (systems and / or devices), and / or computer program products. It is understood that the blocks in the block diagrams and / or flowchart illustrations, and combinations of blocks in the block diagrams and / or flowchart illustrations, can be implemented by computer program instructions executed by one or more computer circuits. These computer program instructions are provided to processor circuits of general-purpose computer circuits, dedicated computer circuits, and / or other programmable data processing circuits, so that instructions executed via the processor of a computer and / or other programmable data processing device can generate a machine that translates and controls transistors, values ​​stored in memory locations, and other hardware components in such circuits to implement the functions / operations specified in the block diagrams and / or flowchart blocks, thereby creating means (functionality) and / or structures for implementing the functions / operations specified in the block diagrams and / or flowchart blocks. The computer program product may be provided as one or more modules of a software product encoded in electrical signals, optical signals, or electromagnetic signals for transmission to a receiver device suitable for execution by a data processing device, for example, downloadable via the Internet.

[0057] It should also be noted that in some alternative implementations, the functions / operations described in a block may be performed in a different order than that shown in the flowchart. For example, two blocks shown consecutively may actually be executed substantially simultaneously, or blocks may sometimes be executed in reverse order depending on the functionality / operations they contain. Furthermore, the functionality of a given block in a flowchart and / or block diagram may be separated into multiple blocks, and / or the functionality of two or more blocks in a flowchart and / or block diagram may be integrated, at least partially. Finally, without departing from the scope of the concepts of this disclosure, other blocks may be added / inserted between illustrated blocks, and / or blocks / operations may be omitted. Furthermore, while some of the diagrams include arrows on the communication path to indicate the main direction of communication, it should be understood that communication may occur in the opposite direction to the depicted arrows.

[0058] Computer program instructions may also be stored in non-temporary, tangible, computer-readable media that can instruct a computer or other programmable data processing device to function in a particular way, causing the instructions stored in the computer-readable media to generate a product containing instructions that implement functions / operations specified in block diagrams and / or flowchart blocks. The term “non-temporary, tangible, computer-readable media” includes semiconductor memory devices, e.g., ROM (read-only memory), EPROM (electrically erasable programmable read-only memory), EEPROM (electrically erasable programmable read-only memory), and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks, and CD-ROM and DVD-ROM disks. The term "non-temporary tangible computer-readable medium" is also intended to encompass types of storage devices that do not necessarily store information permanently, including, for example, RAM (random-access memory), SRAM (static random-access memory), and DRAM (dynamic random-access memory).

[0059] Accordingly, embodiments of the concepts of this disclosure may be embodied in hardware and / or software (including firmware, resident software, microcode, etc.) operating on a processor (also referred to as a controller), such as a digital signal processor, which may collectively be called “circuits,” “modules,” or variations thereof. These terms, as well as terms such as “components,” “engines,” “systems,” “devices,” and “interfaces,” are generally intended to refer to computer-related entities that are hardware (computing device elements), a combination of hardware and software, or software alone. For example, a component may be, but is not limited to, a process, processor, object, executable file, execution thread, program, and / or computer running on a processor. For illustrative purposes, the term “component” may refer to an application running on a controller, or a combination of an application and the controller itself. One or more components may reside within a process and / or execution thread, components may be localized on one computer, and / or distributed across two or more computers. Software may be any programming language.

[0060] Embodiments of the subject matter described herein can be implemented in a computing system having one or more device components in one or more corresponding locations. The device components may include data servers (or other backend components) and may include one or more user devices (e.g., client computers) having a user interface (e.g., a graphical user interface, GUI) that allows a user to interact with the computer system. If the device components of the system are in different locations, they may be interconnected by any form of digital data communication network, and unless otherwise indicated, their collective functionality may be achieved by partitioning the computing process among the device components of the computer system in any way. Examples of wired communication networks include wired local area networks ("LANs") and wired wide area networks ("WANs"), such as the Internet, which do not need to be jointly owned with the rest of the computer system. Examples of wireless communication networks include, for instance, wireless LANs (including Wi-Fi (Wireless Fidelity)), and their communication standards include LTE (Long Term Evolution), 4G (4th generation), or 5G (5th generation). In addition, there are communication standards that enable low-power, wide-area communication, such as LPWA (Low Power Wide Area) and BLE (Bluetooth Low Energy), which includes Bluetooth, a low-power technology suitable for short distances. Client computers may include mobile devices (laptops, tablets, personal digital assistants (PDAs), and mobile phones, etc.).

[0061] In some cases, a computing system can include clients (e.g., client computers) and servers. Clients and servers may be remote from each other and typically interact via a communication network. Clients and servers may run software that defines the client-server relationship.

[0062] A computer system “configured” to perform a specific operation or action means that the system has installed software, firmware, hardware, or a combination thereof that causes the system to perform the operation or action while it is running. One or more computer programs “configured” to perform a specific operation or action means that one or more programs contain instructions that cause the data processing device to perform the operation or action when executed by the device.

[0063] The term "data processing device" refers to data processing hardware and encompasses all types of devices, machines, and equipment for processing data, including, for example, programmable processors, computers, or multiple processors or computers. A device may also be, or further include, a dedicated logic circuit configuration, such as an FPGA (field programmable gate array) or ASIC (application-specific integrated circuit). Optionally, in addition to hardware, a device may include code that constitutes an execution environment for computer programs, such as processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of these.

[0064] The processes and logic flows described herein can be implemented by one or more programmable processors that execute one or more computer programs to perform their functions by acting on input data and producing outputs. The processes and logic flows can also be implemented as dedicated logic circuit configurations, such as FPGAs (field programmable gate arrays) or ASICs (application-specific integrated circuits).

[0065] Computers suitable for running computer programs include, for example, a general-purpose or dedicated microprocessor, or both, or any other type of central processing unit. Generally, the central processing unit receives instructions and data from read-only memory or random-access memory, or both. The elements of a computer are a central processing unit for executing or running instructions, and one or more memory devices for storing instructions and data. Generally, a computer also includes one or more mass storage devices for storing data, such as magnetic, magneto-optical disks, or optical disks, or is operablely coupled to them to receive data from them, transfer data to them, or both. However, a computer is not required to have such devices. Furthermore, a computer can be incorporated into other devices, for example, a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device, such as a Universal Serial Bus (USB) flash drive.

[0066] To provide user interaction, embodiments of the subject matter described herein can be implemented on a computer having a display device for displaying information to the user, such as a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, and a keyboard and pointing device, such as a mouse or trackball, on which the user can provide input to the computer. Other types of devices can also be used to provide user interaction; for example, the feedback provided to the user can be any form of sensory feedback, such as visual feedback, auditory feedback, or tactile feedback, and input from the user can be received in any form, including acoustic, voice, or tactile input. Furthermore, the computer can interact with the user by sending documents to and receiving documents from devices used by the user, for example, by sending a web page to a web browser on the user's client device in response to a request received from a web browser. Input may also be provided by a connection from an external input device instead of an input device such as a keyboard. Any connection method can be used, such as USB (Universal Serial Bus), HDMI (High-Definition Multimedia Interface), or Bluetooth. The embodiments may also include input / output or I / O devices or user interfaces not included herein, which may be connected directly or indirectly to the system.

[0067] The term "machine learning" refers to a process (an automated process, meaning a process with virtually no human intervention except for their arbitrary initiation) in which an algorithm, defined by multiple variable numerical parameters (e.g., thousands, millions, or billions of numerical parameters), is trained to perform a desired function by iteratively modifying the numerical parameters to reduce the discrepancy (e.g., as measured by the loss function) between multiple training input datasets to the algorithm and the corresponding desired output of the algorithm, for example, by a process such as backpropagation.

[0068] In one example, the algorithm is a "neural network" typically defined by a sequence of one or more processing layers, where the output of each processing layer (except the last processing layer) is the input to the next processing layer in the sequence. This is called a "feedforward" network. Each processing layer consists of one or more "neurons" (or "neural units") that receive all or a subset of the input to the corresponding processing layer. Each neuron performs a function of the received data, specifically a function defined by a corresponding subset of numerical parameters (e.g., a nonlinear function) (e.g., a nonlinear function of a weighted sum of the input values ​​of a subset of the input, where the weights are defined by a corresponding subset of numerical parameters). The outputs of the neurons in a given layer collectively constitute the output of the layer. Many types of neural network layers are known, such as transformer layers, recursive layers, and convolutional layers.

[0069] Several algorithms trained by machine learning process data collected from the real world by sensors (e.g., cameras (still or video cameras), microphones, temperature sensors, pressure sensors, strain sensors, accelerometers, etc.) and / or generate data to encode images, audio signals, or control data for electromechanical devices such as robots, vehicles, or vehicle components. For example, a trained algorithm performs a classification task, in which received data (e.g., images or audio signals) is assigned to one of a predetermined set of categories; for example, in the case of images, each category could be an image depicting (a part of) a tire. Contribution to the United Nations-led Sustainable Development Goals (SDGs)

[0070] The SDGs have been proposed to realize a sustainable society. One embodiment of this disclosure is considered to be a technology that can contribute to "No. 9 Industry, Innovation and Infrastructure" and other goals. [Explanation of Symbols]

[0071] 10 Abnormality determination device 11 Communications Department 12 Storage section 13 Control Unit 20 vehicles 30 tires 40 Networks 50 Terminal devices 70 Detection device 80 In-vehicle communication device 90 Storage device 131 Acquisition Department 132 Calculation Section 133 Judgment section 134 Output section

Claims

1. An acquisition unit that acquires input data including the tire temperature detected by a detection device mounted on the vehicle, A calculation unit that determines the mounting position of the tire on the vehicle based on the input data, and classifies the tire into groups according to the mounting position, An abnormality detection device comprising: a determination unit that compares the internal temperatures of each of the multiple tires classified according to the aforementioned group, and determines that an abnormality has occurred in the vehicle's brakes if a deviation of more than a threshold occurs in the internal temperatures of the tires.

2. The abnormality detection device according to claim 1, wherein the tires are classified into the groups of front wheels, inner rear wheels, or outer rear wheels.

3. The abnormality detection device according to claim 1 or 2, wherein the threshold temperature is 20°C.

4. The input data includes information on the vehicle's position or information on the vehicle's speed. The abnormality determination device according to claim 1 or 2, wherein the determination unit does not make a determination regarding abnormalities in the brakes of the vehicle when the vehicle is stopped and for a certain period of time after the vehicle starts moving.

5. In the abnormality detection device, This involves acquiring input data, including the tire temperature detected by a detection device mounted on the vehicle, and Based on the input data, the mounting position of the tire whose internal temperature has been detected on the vehicle is determined, and the tire is classified into a group according to the mounting position. A program that performs the following actions: compares the internal temperatures of multiple tires classified into the aforementioned groups, and determines that a brake malfunction has occurred in the vehicle if a discrepancy of the internal tire temperatures exceeds a threshold.

6. An anomaly detection method performed by an anomaly detection device, The abnormality detection device, This involves acquiring input data, including the tire temperature detected by a detection device mounted on the vehicle, and Based on the input data, the mounting position of the tire whose internal temperature has been detected on the vehicle is determined, and the tire is classified into a group according to the mounting position. An abnormality detection method, which includes comparing the internal temperatures of each of the multiple tires classified into the aforementioned groups, and determining that an abnormality has occurred in the vehicle's brakes if a discrepancy of more than a threshold value occurs in the internal temperatures of the tires.

Images