KR102571608B1 - Tire monitoring system and method for monitoring tire - Google Patents

Abstract

The present invention relates to a tire, a sensor module at least partially disposed in the inner space of the tire and measuring an acoustic signal inside the tire having a frequency spectrum, and the acoustic signal received from the sensor module and preset reference data. It provides a tire monitoring system including a processor for diagnosing the state of the tire by comparing the .

Description

Tire monitoring system and method for monitoring tires {Tire monitoring system and method for monitoring tire}

The present invention relates to a tire monitoring system and a tire monitoring method.

Vehicles driven by users are composed of many parts, and among them, tires have a great influence on the operation of the vehicle, and in particular, can be said to be one of the key parts for securing the user's safety.

The tire-pressure-monitoring system (TPMS) measures the internal pressure of the tire in real time and informs the driver of the pressure condition of the tire. However, since the tire pressure monitoring system generates pressure information, it is difficult to determine the condition of various tires.

Conventionally, a mechanic directly checks tire damage or checks tire damage from the outside of the tire using a measuring device. However, in the case of quality damage to the internal structure of the tire, it is difficult to confirm the damage by observing it from the outside. In addition, since this method needs to check a tire in a stationary state, there is a limit to confirming damage to a tire in a running state.

The foregoing background art is technical information that the inventor possessed for derivation of the present invention or acquired during the derivation process of the present invention, and cannot necessarily be said to be known art disclosed to the general public prior to filing the present invention.

An object of the present invention is to provide a tire monitoring system and a tire monitoring method capable of improving driving safety by accurately and quickly determining the condition of a tire while driving a vehicle.

An embodiment of the present invention relates to a tire, a sensor module at least partially disposed in the inner space of the tire and measuring an acoustic signal inside the tire having a frequency spectrum, and the acoustic signal transmitted from the sensor module. A tire monitoring system including a processor for diagnosing the condition of the tire by comparing the set reference data with the set reference data.

In addition, the processor may extract a plurality of frequency peaks from the sound signal as target frequency peaks, and compare the target frequency peaks with frequency peaks of the reference data.

In addition, the processor may extract a predetermined number of target frequency peaks from the frequency spectrum of the sound signal, and extract the target frequency peaks in order of low frequencies in the frequency spectrum.

In addition, the processor may extract a peak at the lowest frequency among a plurality of frequency peaks of the frequency spectrum of the acoustic signal as a division frequency peak, and compare the division frequency peak with the reference data.

In addition, the processor first compares the segmented frequency peak and the reference data, and when it is determined that the state of the tire is abnormal, extracts a plurality of frequency peaks from the sound signal as target frequency peaks, and compares the target frequency peak and Frequency peaks of the reference data may be compared.

In addition, the reference data is set to different grades according to the condition of the tire based on the frequency peak of the reference data, and the processor continuously matches the acoustic signals received continuously to the plurality of grades of the reference data. and each of the plurality of grades may have a probability value according to the number of matches with the sound signal.

Also, when the highest probability value among the plurality of grades exceeds a predetermined threshold value, the processor may determine the state of the tire measured by the sensor module based on the grade of the probability value.

Also, the reference data may be generated by machine learning acoustic signals of tires having different states.

In addition, the sensor module is mounted on the rim of the tire, and at least a portion of the sensor module is exposed in a space between the tire and the rim to measure a sound pressure in the space.

Another embodiment of the present invention is a tire monitoring method using a frequency spectrum of an acoustic signal measured inside a tire, comprising the steps of measuring tires in different states to obtain reference data, and the acoustic signal inside the tire. It provides a tire monitoring method comprising the steps of measuring , comparing the measured acoustic signal with the reference data based on a frequency spectrum, and displaying the state of the tire.

In addition, in the step of acquiring the reference data, the state of each tire may be graded by machine learning the acoustic signals of the tires having different states.

In the comparing of the measured acoustic signal and the reference data, a plurality of frequency peaks from the acoustic signal may be extracted as target frequency peaks, and the target frequency peak may be compared with a frequency peak of the reference data.

In addition, the step of comparing the measured acoustic signal and the reference data extracts a predetermined number of target frequency peaks from the frequency spectrum of the acoustic signal, and selects the target frequency peaks in order of low frequencies in the frequency spectrum. can be extracted.

In addition, the step of comparing the measured acoustic signal and the reference data may include extracting a peak at the lowest frequency among a plurality of frequency peaks of the frequency spectrum of the acoustic signal as a division frequency peak, and comparing the division frequency peak with the reference data. Reference data can be compared.

In addition, in the comparing of the measured acoustic signal and the reference data, the frequency peaks are first compared with the reference data, and when it is determined that the state of the tire is abnormal, a plurality of frequency peaks are detected in the acoustic signal. A target frequency peak may be extracted, and the target frequency peak may be compared with a frequency peak of the reference data.

The tire monitoring system and method according to the present invention can accurately determine the state of the tire by measuring acoustic signals in the inner space of the tire. Since the sensor module is exposed to the inner space of the tire, external noise of the tire is blocked when measuring the acoustic signal of the inner space of the tire, so that accurate tire information can be determined.

The tire monitoring system and method according to the present invention compares the frequency peak of the measured acoustic signal with reference data to accurately and quickly determine the state of the tire. Since the characteristic of the state of the tire is different from the frequency peak of the acoustic signal, the processor can quickly and accurately determine the state of the tire by comparing at least one of the target frequency peak and the segment frequency peak with the frequency peak of the reference data.

1 is a diagram illustrating a tire monitoring system according to an embodiment of the present invention.
FIG. 2 is a block diagram illustrating the tire monitoring system of FIG. 1;
FIG. 3 is a graph illustrating sound signals processed by the processor of FIG. 2 .
FIG. 4 is a graph in which sound signals processed by the processor of FIG. 2 are matched with reference data.
5 is a flowchart illustrating a tire monitoring method according to another embodiment of the present invention.
FIG. 6 is a flow chart illustrating one embodiment of FIG. 5 .
FIG. 7 is a flow chart illustrating another embodiment of FIG. 5 .

Since the present invention can apply various transformations and have various embodiments, specific embodiments will be illustrated in the drawings and described in detail in the detailed description. However, it should be understood that this is not intended to limit the present invention to specific embodiments, and includes all transformations, equivalents, and substitutes included in the spirit and scope of the present invention. In describing the present invention, if it is determined that a detailed description of related known technologies may obscure the gist of the present invention, the detailed description will be omitted.

Terms such as first and second may be used to describe various components, but the components should not be limited by the terms. Terms are only used to distinguish one component from another.

Terms used in this application are only used to describe specific embodiments, and are not intended to limit the present invention. Singular expressions include plural expressions unless the context clearly dictates otherwise. In addition, in each drawing, components are exaggerated, omitted, or schematically illustrated for convenience and clarity of description, and the size of each component does not entirely reflect the actual size.

In the description of each component, in the case where it is described as being formed on or under, both on and under are formed directly or through other components. Including, the criteria for the upper (on) and lower (under) will be described based on the drawings.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings, and in the description with reference to the accompanying drawings, the same or corresponding components are given the same reference numerals, and overlapping descriptions thereof will be omitted. do.

FIG. 1 is a diagram illustrating a tire monitoring system according to an embodiment of the present invention, and FIG. 2 is a block diagram illustrating the tire monitoring system of FIG. 1 .

Referring to FIGS. 1 and 2 , the tire monitoring system may include a tire 10 , a rim 20 , a sensor module 100 and a processor 200 . The tire monitoring system may measure a sound signal generated in the inner space of the tire 10 and monitor the condition of the tire based on the measurement.

The tire 10 is mounted on the rim 20, and air at a set pressure is stored in an inner space defined by the tire 10 and the rim 20.

Noise is generated in the inner space of the tire 10 while the vehicle is running, and the noise may be determined by the driving condition of the tire 10 or the condition of the tire 10 . That is, noise generated inside the tire 10 according to driving conditions, such as when the tire 10 is driving on a rough road surface, driving on snowy road, or driving on a rain road, has frequency characteristics. can have

In addition, noise generated inside the tire 10 may have frequency characteristics depending on the state of the tire 10, such as when the tire 10 has a defect or has a high mileage. For example, if one component of the tire, such as an inner liner, a belt layer, a cap ply, a body ply, a bead filler, or a bead core, has a defect, the tire may have frequency characteristics according to the defect in the component.

At least a part of the sensor module 100 may be disposed in the inner space of the tire 10 . The sensor module 100 may be mounted on the rim 20 and may be disposed such that at least a portion of the sensor module 100 is exposed to an inner space defined by the tire 10 and the rim 20 .

The sensor module 100 includes a plurality of sensors, and each sensor may measure internal information of the tire 10 . The sensor module 100 may measure a sound signal inside a tire having a frequency spectrum.

The first sensor 110 may measure an acoustic signal inside the tire 10, and the acoustic signal may have a frequency spectrum. The first sensor 110 may measure a change in acoustic pressure of air inside the tire 10 . For example, the first sensor 110 may be provided with a microphone or the like.

In detail, the first sensor 110 may be exposed to the inner space of the tire 10 and sense sound generated in the inner space of the tire 10 while the vehicle is driving. Since the first sensor 110 continuously generates sound signals while the vehicle is driving, a frequency spectrum according to driving time may be generated.

The second sensor 120 may measure the air pressure inside the tire 10 . The second sensor 120 may continuously measure the pressure in the inner space of the tire 10 while the vehicle is driving.

The third sensor 130 may measure the internal temperature of the tire 10 . The third sensor 130 may continuously measure the temperature of the inner space of the tire 10 while the vehicle is driving.

The sensor module 100 may include a valve unit 30 . By adding air to the inner space of the tire 10 through the valve unit 30 , the inner pressure of the tire 10 may be adjusted. That is, the sensor module 100 may include a TPMS sensor and an acoustic sensor in the valve stem of the tire.

The transceiver 140 may transmit data measured by the sensor module 100 to the processor 200 . Also, the transceiver 140 may receive a user input from the processor 200 . The transceiver 140 may have various forms capable of transmitting signals or data generated by the sensor module 100 and the processor 200.

The transceiver 140 and the processor 200 may perform communication using a network. For example, the network may include a Local Area Network (LAN), a Wide Area Network (WAN), a Value Added Network (VAN), a mobile radio communication network, a satellite communication network, and any of these It is a comprehensive data communication network that includes mutual combinations and allows each network constituent entity to communicate smoothly with each other, and may include wired Internet, wireless Internet, and mobile wireless communication network. In addition, wireless communication includes, for example, wireless LAN (Wi-Fi), Bluetooth, Bluetooth low energy (Bluetooth low energy), Zigbee, WFD (Wi-Fi Direct), UWB (ultra wideband), infrared communication (IrDA, infrared Data Association), NFC (Near Field Communication), 5G, etc. may exist, but are not limited thereto.

In the sensor module 100, a plurality of sensors may be integrally formed. The first sensor 110 , the second sensor 120 , and the third sensor 130 are disposed in one case and can simultaneously measure noise, pressure, and temperature of the internal state of the tire 10 .

The first sensor 110 of the sensor module 100 is fixed to the rim 20 and can accurately measure the sound signal of the tire 10 . Since the rim 20 is made of a strong material to support the vehicle, the shape is not deformed during driving, and since the sensor module 100 is mounted on the rim 20, the position of the tire 10 does not change during driving, noise can be minimized.

FIG. 3 is a graph illustrating sound signals processed by the processor of FIG. 2 .

Referring to FIGS. 2 and 3 , the processor 200 may diagnose the state of the tire 10 by comparing the sound signal received from the sensor module 100 with preset reference data.

In one embodiment, the processor 200 may include a first unit 200A disposed on the sensor module 100 and a second unit 200B disposed outside the sensor module 100 .

The first unit 200A may simply process data measured by the sensor module 100 . For example, the first unit 200A may convert the acoustic signal measured by the first sensor 110 into a continuous frequency spectrum according to driving time. In addition, noise may be removed before transmitting the sound signal to the second unit 200B.

The second unit 200B may receive the data processed by the first unit 200A and diagnose the driving condition of the tire 10 . For example, the second unit 200B may receive the sound signal processed by the first unit 200A through the transceiver 140 and compare it with reference data to diagnose the condition of the tire.

In another embodiment, although not shown in the drawings, the processor 200 may be provided as one and may be disposed in the sensor module 100 or disposed outside the sensor module 100 . The processor 200 is composed of one component and can directly compare the acoustic signal received from the sensor module 100 with reference data. The data storage unit may be set according to the location of the processor.

Reference data may be obtained through machine learning of data measured by the sensor module 100 . Hereinafter, a method of obtaining reference data from a normal tire, a tire with a small degree of damage, and a tire with a high degree of damage will be described.

For example, when driving a vehicle equipped with normal T1 tires, the sensor module 100 generates sound signals using noise generated by the T1 tires. The acoustic signal is continuously measured during the driving time, and the processor 200 may generate reference data having a frequency spectrum by continuing machine learning using the measured acoustic signal as an input value. Since the reference data is formed by repeating the measured data through machine learning, the reference data regarding the frequency spectrum of a normal tire can have high reliability.

When a vehicle equipped with a T2 tire with a small degree of damage is driven, the sensor module 100 generates an acoustic signal using noise generated by the T2 tire. The acoustic signal is continuously measured during the driving time, and the processor 200 may generate reference data having a frequency spectrum by continuing machine learning using the measured acoustic signal as an input value. Since the reference data is formed by repeating the measured data through machine learning, reference data regarding the frequency spectrum of a tire with a small degree of damage can have high reliability.

When a vehicle equipped with a T3 tire with a high degree of damage is driven, the sensor module 100 generates an acoustic signal using noise generated by the T3 tire. The acoustic signal is continuously measured during the driving time, and the processor 200 may generate reference data having a frequency spectrum by continuing machine learning using the measured acoustic signal as an input value. Since the reference data is formed by repeating the measured data through machine learning, reference data regarding the frequency spectrum of a tire with a high degree of damage can have high reliability.

Reference data according to the state of each tire is generated based on the acoustic signal measured by the sensor module 100, and can have high reliability through repeated machine learning. The generated reference data may be stored in the data storage unit 300 .

In one embodiment, the reference data may be updated by additionally measured data. For example, the sensor module 100 may obtain an acoustic signal by driving a vehicle equipped with T4 tires of unknown tire condition, and the processor 200 may compare the acoustic signal with reference data. When the processor 200 diagnoses that the T4 tire has a level of damage of the T3 tire, reference data for a tire with a previous high degree of damage may be updated with signal data for the T4 tire.

Since the reference data is continuously updated when the tire monitoring system monitors the condition of the tire, reliability of the data can be increased.

Referring to FIG. 3 , reference data may include a normal tire (solid line), a slightly damaged tire (dotted line), and a heavily damaged tire (dotted chain line). Each reference data has a frequency spectrum and may have a frequency peak (FP).

The processor 200 may extract some of the plurality of frequency peaks (FPs) from the acoustic signal measured by the sensor module 100 as target frequency peaks (TFP). The extracted target frequency peak (TFP) may be compared with a frequency peak in reference data.

The processor 200 compares a target frequency peak (TFP) in the frequency spectrum of the acoustic signal with a target frequency peak (TFP) of reference data corresponding to the target frequency peak (TFP). The processor 200 may determine which data the frequency spectrum of the measured acoustic signal corresponds to among stored reference data by comparing differences in target frequency peaks (TFP).

For example, if the target frequency peak (TFP) extracted by the processor 200 has the highest similarity to the frequency peak in the frequency spectrum of the T2 tire, the driving tire is determined to be a tire with little damage, an alarm signal is generated, It can be delivered to the display device 500.

The processor 200 may compare target frequency peaks (TFPs) to increase calculation processing speed and increase calculation accuracy. Since the frequency peak characteristics are different depending on the condition of the tire, even if the frequency peak of the acoustic signal measured by the sensor module 100 is compared with the frequency peak of the reference data, the calculation accuracy can be improved and the calculation speed can be improved at the same time. .

In one embodiment, the processor 200 extracts a predetermined number of target frequency peaks (TFPs) from the frequency spectrum of the acoustic signal, but may extract the target frequency peaks (TFPs) in order of low frequencies in the frequency spectrum.

For example, the processor 200 extracts five target frequency peaks (TFP1 to TFP5) at 15 f/Hz to 80 f/Hz from the frequency spectrum measured by the sensor module 100, which are low frequency peaks in the frequency spectrum. can be set in the order of

Referring to FIG. 3 , frequency peaks in a low frequency band can be easily distinguished according to the degree of tire damage. The frequency peaks of the T1 tire, T2 tire, and T3 tire are easily distinguished in a low frequency band, but as the frequency band increases, the similarity increases, making it difficult to distinguish them.

The processor 200 selects a frequency peak of a low frequency band as a target frequency peak in the frequency spectrum measured by the sensor module 100 and compares it with the reference data, thereby quickly determining where the tire state corresponds in the reference data. can do.

In one embodiment, the processor 200 extracts a peak at the lowest frequency among a plurality of frequency peaks (FP) of the frequency spectrum of the acoustic signal as a division frequency peak (CFP), and converts the division frequency peak (CFP) into reference data. can be compared with

Based on the same frequency band, the processor 200 may simply and quickly diagnose the state of the driving tire by comparing the frequency peak (CFP) of the measured data and the frequency peak (FP) of the reference data.

Referring to FIG. 3 , the frequency peak (FP) having the lowest frequency shows the largest difference depending on the condition of the tire. In particular, when the degree of damage to the tire is large, the height of the frequency peak increases. Therefore, since the frequency peak (FP) having the lowest frequency has the largest height difference, the condition of the tire can be quickly diagnosed by comparing only the frequency peak (CFP) in the frequency spectrum measured by the sensor module 100. .

In another embodiment, the processor 200 first compares the segment frequency peak (CFP) and the reference data, and when it is determined that the tire is in an abnormal state, the processor 200 extracts a plurality of frequency peaks from the sound signal as target frequency peaks (TFP). and compare the target frequency peak (TFP) with the frequency peak of the reference data.

In detail, the processor 200 may extract a CFP from the frequency spectrum of the acoustic signal and compare it with the frequency spectrum of the reference data. The processor 200 first determines whether the sensed tire is in a normal state or an abnormal state by comparing the segment frequency peak (CFP).

If it is determined that the tire is in an abnormal state, the processor 200 may compare a plurality of target frequency peaks (TFPs) with the frequency spectrum of the reference data as a next step. Thus, the processor 200 may determine the degree of damage to the tire. For example, the processor 200 may determine whether the damage to the tire 10 is small or large by comparing the target frequency peak (TFP).

The processor 200 may increase the calculation speed by first determining whether the tire is in a normal or abnormal state. Since the processor 200 determines the degree of damage to the tire 10 only when the tire is in an abnormal state using the segment frequency peak (CFP), unnecessary calculation for the tire in a normal state is eliminated, thereby increasing the calculation speed of the system. can

FIG. 4 is a graph in which sound signals processed by the processor of FIG. 2 are matched with reference data.

Referring to FIG. 4 , reference data may be set to different grades according to tire conditions based on frequency peaks of the reference data. The vertical axis is the grade in the reference data, and the horizontal axis is the signal data measured by the sensor module 100. In FIG. 4 , the density is proportional to the number of matchings, and the class having the deepest density indicates that the processor has matched the measured acoustic signal the most.

The processor 200 may set each grade to reference data. For example, the processor 200 sets a normal T1 tire as a first grade (L1), sets a T2 tire with a small damage as a second grade (L2), and sets a T3 tire with a large damage as a third grade (L3). It is set, and the T4 tire with very large damage can be set as the fourth grade.

Thereafter, the processor 200 may match the frequency spectrum of the acoustic signal received from the sensor module 100 to each class. The sensor module 100 continuously generates sound signals, and the generated sound signals are input to the processor 200 . Based on the frequency peak of the input acoustic signal, the processor may match the acoustic signal to any one of the first to fourth grades.

Since the sound signal is continuously input according to the driving of the vehicle, the processor 200 may continuously match input values to a plurality of grades while the vehicle is driving. Each of the plurality of grades may have a probability value according to the number of matches with the sound signal.

For example, since the A1 tire matches the first class L1 the highest number of times, the processor 200 determines that the A1 tire is a tire in a normal state. Since the A2 tire is matched with the third grade (L3) the highest number of times, the processor 200 determines the A2 tire to be a tire with the greatest damage. Since the A3 tire is matched with the second grade L2 the highest number of times, the processor 200 determines the A2 tire as a less damaged tire. Since the A4 tire is matched with the fourth grade L4 the highest number of times, the processor 200 determines that the A4 tire is a very damaged tire.

When the highest probability value among a plurality of grades exceeds a predetermined threshold value, the processor 200 may determine the state of the tire measured by the sensor module based on the grade of the probability value.

For example, if the threshold value is set to a%, the processor determines that the tire A1 is normal only when it is determined that the highest probability value of the tire A1 exceeds a%. In addition, the processor determines that the A2 tire has a large damage only when it is determined that the highest probability value of the A2 tire exceeds a%. In addition, the processor determines that the damage of the A3 tire is small only when it is determined that the highest probability value of the A3 tire exceeds a%. In addition, the processor determines that the A4 tire is very damaged only when it is determined that the highest probability value of the A4 tire exceeds a%.

If the probability value calculated by the processor 200 does not exceed the threshold value, the driving of the sensor module 100 continues, and the processor 200 receives input from the sensor module 100 until the probability value exceeds the threshold value. value can be passed.

Since the processor 200 diagnoses the condition of the tire when the matching probability for each class exceeds a threshold value, the accuracy of tire diagnosis can be improved.

Referring back to FIG. 2 , the data storage unit 300 may store reference data and may be connected to the processor 200 to receive the reference data. The data storage unit 300 may transmit reference data to the processor 200 through a wire or a communication network.

The user interface 400 allows a user to input a command. A user may select reference data through the user interface 400 .

Reference data according to the state of various tires is stored in the data storage unit 300, and the user can select one of a plurality of reference data in order to examine whether the current tire of the vehicle corresponds to a specific state.

The processor 200 may compare the selected reference data with the sound signal measured by the sensor module 100 to determine whether the state of the tire corresponds to the state of the reference data.

The display device 500 may display the state of the tire determined by the processor 200 . The display device 500 may display state information determined by the processor 200 . In addition, when the processor 200 determines that the tire has a large defect, an alarm signal is generated and transmitted to the display device 500 to give a warning to the user.

The tire monitoring system according to the present invention can accurately determine the state of the tire by measuring acoustic signals in the inner space of the tire. Since the sensor module 100 is exposed to the inner space of the tire, external noise of the tire is blocked when measuring the sound signal of the inner space of the tire, so that accurate tire information can be determined.

The tire monitoring system according to the present invention compares the frequency peak of the measured acoustic signal with reference data to accurately and quickly determine the state of the tire. Since the characteristic of the state of the tire is different from the frequency peak of the acoustic signal, the processor can quickly and accurately determine the state of the tire by comparing at least one of the target frequency peak and the segment frequency peak with the frequency peak of the reference data.

5 is a flowchart illustrating a tire monitoring method according to another embodiment of the present invention.

Referring to FIG. 5, measuring tires in different states to obtain reference data (S10), measuring the acoustic signal inside the tire (S20), and measuring the measured acoustic signal and It may include comparing the reference data (S30) and displaying the state of the tire (S40).

In step S10 of obtaining reference data by measuring tires in different states, sound signals of each tire may be generated and reference data may be obtained based on the generated sound signals. In addition, the processor 200 classifies the state of each tire by machine learning the acoustic signals of the tires having different states, and reference data may be stored for each rank.

Reference data is obtained by using a machine learning algorithm in a processor for data of each tire, and as data measured by the sensor module 100 is accumulated, the reference data may be updated.

In the step of measuring the sound signal inside the tire ( S20 ), the sensor module 100 may measure the sound signal of the tire during driving and form it into a frequency spectrum.

In the step of comparing the measured acoustic signal and the reference data based on the frequency spectrum (S30), the processor 200 compares the frequency spectrum of the acoustic signal and the frequency spectrum of the reference data, and determines the state of the tire based on this. can do.

In the step of displaying the tire state ( S40 ), the tire state information determined by the processor 200 may be displayed on the display device 500 . If it is determined that the damage to the tire is significant, the processor 200 may transmit an alarm signal to the display device 500 .

FIG. 6 is a flow chart illustrating one embodiment of FIG. 5 .

Referring to FIG. 6 , comparing the measured acoustic signal and the reference data based on the frequency spectrum (S30) includes extracting a target frequency peak from the acoustic signal (S310) and assigning a plurality of grades of the reference data to the acoustic signal. It may include matching signals (S320), setting probability values to a plurality of grades according to the number of matches (S330), and comparing probability values with threshold values (S340).

In the step of extracting the target frequency peak from the sound signal (S310), a plurality of target frequency peaks (TFP) may be extracted from the frequency spectrum of the sound signal.

In one embodiment, the processor 200 extracts a predetermined number of target frequency peaks (TFPs) from the frequency spectrum of the acoustic signal, but may extract the target frequency peaks (TFPs) in order of low frequencies in the frequency spectrum.

In the step of matching the acoustic signal to the plurality of grades of the reference data (S320), the frequency peak of the reference data and the target frequency peak (TFP) of the acoustic signal may be compared.

A plurality of reference data is set to each class according to the characteristics of the frequency peak, and the acoustic signal is matched to the target frequency peak (TFP). Since continuous sound signals from tires being driven are input to the processor 200, the total number of matchings increases as the driving time elapses, and the sound signals may be matched to specific levels of reference data according to the state of the driving tires.

In the step of setting probability values for a plurality of grades according to the number of matches (S330), the number of matches for each level of the reference data is made into statistics, and a probability value for each level is set.

In the step of comparing the probability value and the threshold value (S340), the highest probability value and the threshold value are compared. If the probability value exceeds the threshold value, the processor 200 determines that the driving tire has the highest probability value grade. However, if the probability value is less than or equal to the threshold value, the processor 200 continues to match the acoustic signal of the tire to the class.

Figure 7 is a flow chart illustrating another embodiment of Figure 5;

Referring to FIG. 7 , comparing the measured acoustic signal and the reference data based on the frequency spectrum (S30) includes extracting a division frequency peak and a target frequency peak from the acoustic signal (S310A), the division frequency peak and It may include comparing reference data (S320A) and comparing target frequency peak and reference data (S330A).

In the step of extracting the division frequency peak and the target frequency peak from the acoustic signal (S310A), the processor 200 may extract the division frequency peak (CFP) and the target frequency peak (TFP) from the acoustic signal.

In the step of comparing the segment frequency peak and the reference data (S320A), the processor 200 may compare the segment frequency peak (CFP) of the acoustic signal and the reference data to determine whether the tire is in a normal state or an abnormal state. If it is determined that the tire is in a normal state, the processor 200 does not perform an additional operation, and if it is determined that the tire is in an abnormal state, the processor 200 proceeds with the following steps.

In the step of comparing the target frequency peak and the reference data (S330A), the processor 200 compares the target frequency peak (TFP) of the acoustic signal with the reference data to accurately determine the degree of damage to the tire.

For example, the processor 200 may compare five target frequency peaks (TFPs) with each frequency peak of the reference data to accurately determine the degree of damage to the tire being driven.

The tire monitoring method according to the present invention compares the frequency peak of the measured acoustic signal with reference data, and can accurately and quickly determine the state of the tire. Since the characteristic of the state of the tire is different from the frequency peak of the acoustic signal, the processor can quickly and accurately determine the state of the tire by comparing at least one of the target frequency peak and the segment frequency peak with the frequency peak of the reference data.

Although the above has been described with reference to the embodiments shown in the drawings, this is only exemplary, and those skilled in the art will understand that various modifications and equivalent other embodiments are possible therefrom. Therefore, the true technical scope of protection of the present invention should be determined by the technical spirit of the appended claims.

10: tire
20: rim
100: sensor module
200: processor

Claims (15)

tire;
a sensor module, at least a part of which is disposed in the inner space of the tire, to measure a sound signal inside the tire having a frequency spectrum; and
A processor for diagnosing the condition of the tire by comparing the acoustic signal received from the sensor module with preset reference data;
The processor
Extracting a plurality of frequency peaks from the sound signal as target frequency peaks, comparing the target frequency peak with the frequency peak of the reference data,
A tire monitoring system, wherein a predetermined number of target frequency peaks are extracted from the frequency spectrum of the acoustic signal, and the target frequency peaks are extracted in order of low frequencies in the frequency spectrum. tire;
a sensor module, at least a part of which is disposed in the inner space of the tire, to measure a sound signal inside the tire having a frequency spectrum; and
A processor for diagnosing the condition of the tire by comparing the acoustic signal received from the sensor module with preset reference data;
The processor
A tire monitoring system for extracting a peak at the lowest frequency among a plurality of frequency peaks of the frequency spectrum of the acoustic signal as a division frequency peak and comparing the division frequency peak with the reference data. According to claim 2,
The processor
The segmented frequency peak and the reference data are first compared, and if it is determined that the state of the tire is abnormal, a plurality of frequency peaks are extracted as target frequency peaks from the acoustic signal, and the frequency of the target frequency peak and the reference data is determined. A tire monitoring system that compares peaks. According to claim 1 or 2,
The reference data is set to different grades according to the condition of the tire based on the frequency peak of the reference data,
The processor
Continuously matching the input acoustic signal to a plurality of grades of the reference data,
The tire monitoring system, wherein each of the plurality of ratings has a probability value according to the number of times matched with the acoustic signal. According to claim 4,
The processor
Wherein the tire monitoring system determines the state of the tire measured by the sensor module based on the grade of the probability value when the highest probability value among the plurality of grades exceeds a predetermined threshold value. According to claim 1 or 2,
The reference data is
A tire monitoring system that generates sound signals of tires in different conditions through machine learning. According to claim 1 or 2,
The sensor module
Mounted on the rim of the tire, at least a portion of which is exposed in a space between the tire and the rim to measure the sound pressure of the space. In the tire monitoring method using the frequency spectrum of the acoustic signal measured inside the tire,
obtaining reference data by measuring tires in different states;
measuring the acoustic signal inside the tire;
comparing the measured acoustic signal and the reference data based on a frequency spectrum; and
Including; displaying the state of the tire,
Comparing the measured acoustic signal and the reference data
A tire monitoring method of extracting a plurality of frequency peaks from the acoustic signal as target frequency peaks and comparing the target frequency peak with a frequency peak of the reference data. According to claim 8,
Acquiring the reference data
A tire monitoring method that classifies the condition of each tire by machine learning the acoustic signals of tires with different conditions. According to claim 8,
Comparing the measured acoustic signal and the reference data
Extracting a predetermined number of target frequency peaks from the frequency spectrum of the acoustic signal, wherein the target frequency peaks are extracted in order of low frequencies in the frequency spectrum. According to claim 8,
Comparing the measured acoustic signal and the reference data
Tire monitoring method of extracting a peak at the lowest frequency among a plurality of frequency peaks of the frequency spectrum of the acoustic signal as a segmentation frequency peak and comparing the segmentation frequency peak with the reference data. According to claim 11,
Comparing the measured acoustic signal and the reference data
If the state of the tire is determined to be abnormal by first comparing the segmented frequency peak and the reference data, a plurality of frequency peaks are extracted as target frequency peaks from the acoustic signal, and the frequency of the target frequency peak and the reference data is determined. A tire monitoring method that compares peaks. delete delete delete

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