Wheel abnormality detection device

Abstract

To provide an abnormality detector capable of detecting an abnormality generated on a wheel properly, on the basis of a signal indicating a wheel rotation speed.SOLUTION: An abnormality detector comprises: a signal acquiring part; a first index calculating part; a spectrum calculating part; and a standardization part; and a second index calculating part. The signal acquiring part acquires, a signal indicating a wheel rotation speed, as a pulse having start. The first index calculating part calculates a first index which indicates, a time variation of start, in each pulse corresponding to one rotation of the wheel. The spectrum calculating part performs frequency analysis of the first index calculated for each pulse, for calculating a frequency spectrum of a rotation order from first to m-th of the first index. The standardization part standardizes a gain of the frequency spectrum, using an average value and a standard deviation of a gain for every rotation order when there is no abnormality. The second index calculation part calculates a second index for determining abnormality presence, on the basis of the standardized gain.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to an abnormality detection device, an abnormality detection method, and an abnormality detection program for detecting an abnormality occurring in a wheel attached to an axle. [Background technology]

[0002] Early detection of wheel abnormalities and the ability to take appropriate measures are important for maintaining proper vehicle operation. Examples of wheel abnormalities include slight wheel wobble and tire damage. Wheel wobble is typically caused by loose wheel nuts. Continuing to drive with loose wheel nuts can cause the nuts to loosen gradually, eventually leading to the wheel falling off. Examples of tire damage include pinch cuts. Pinch cuts occur when a tire undergoes significant deformation upon impact, causing damage to the tire's strength components when the sidewall is pinched between the road surface and the wheel rim flange. Severe pinch cuts can cause sudden tire pressure loss, rendering the tire inoperable. Mild pinch cuts do not cause sudden pressure loss, and the driver may not notice them. However, continuing to drive with such pinch cuts can result in a sudden puncture or tire burst.

[0003] Patent Document 1 discloses a sensor unit that detects loosening of a wheel mounting means that secures a vehicle wheel to a hub based on pulses output from a rotation speed detection device that detects the rotation speed of the wheel. The rotation speed detection device includes a multi-poled disk attached to the wheel and a magnetic field sensor attached to the hub. The multi-poled disk is, for example, a magnetic encoder disk with a predetermined number N of pole areas. When the multi-poled disk rotates with the wheel, the magnetic field sensor detects the magnetic field strength according to the rotational position of the multi-poled disk. The magnetic field sensor outputs measurement pulses having individual pulse durations for each pole area.

[0004] According to Patent Document 1, due to pitch errors that usually occur in the pole areas of a multi-poled disk, the individual pulse durations are not identical to the average pulse duration, which depends on the wheel speed, but are fixed for each pole area. When looseness occurs in the wheel mounting means, an additional periodic change occurs in the individual pulse durations. The frequency of this change corresponds to an integer multiple of the rotational speed of the corresponding wheel multiplied by the number of wheel mounting means.

[0005] In Patent Document 1, the above phenomenon is utilized to detect looseness of the wheel mounting means. More specifically, the frequency spectrum of the periodic change over one wheel rotation is calculated, and if the amplitude of the spectrum reaches or exceeds a predetermined threshold at a wheel frequency equal to or equal to the number of wheel mounting means or an integer multiple thereof, it is determined that the wheel mounting means is loose. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Laid-Open No. 2016-530488 Summary of the Invention [Problem to be solved by the invention]

[0007] According to the inventor's investigations, the extent to which the amplitude of the above-mentioned frequency spectrum changes at the wheel frequency depends on the type of wheel, including the tire, and the type of vehicle. Therefore, even if the wheel mounting means becomes loose, the amplitude does not necessarily exceed a predetermined threshold at a specific wheel frequency. If the spectrum amplitude changes in a decreasing direction or if the change in amplitude is slight, it may not be possible to properly detect looseness of the wheel mounting means. This is true not only when attempting to detect looseness of the wheel mounting means, but also when attempting to detect wheel rattle or tire damage based on a similar frequency spectrum.

[0008] The present invention aims to provide an abnormality detection device, an abnormality detection program, and an abnormality detection method that can more appropriately detect abnormalities occurring in wheels based on a signal representing the rotational speed of the wheels. [Means for solving the problem]

[0009] According to one aspect of the present invention, an anomaly detection device detects an anomaly occurring in a wheel, and includes a signal acquisition unit, a first index calculation unit, a spectrum calculation unit, a standardization unit, and a second index calculation unit. The signal acquisition unit sequentially acquires a signal representing the rotation speed of the wheel as pulses having rising edges. The first index calculation unit calculates a first index representing temporal variation in the rising edges of each of the pulses corresponding to one rotation of the wheel. The spectrum calculation unit calculates a frequency spectrum of rotation orders from 1st to mth (where m≧1) of the first index by frequency analyzing the first index calculated for each of the pulses. The standardization unit standardizes the gain for each rotation order of the frequency spectrum using the average value and standard deviation of the gain for each rotation order when no anomaly is present. The second index calculation unit calculates a second index for determining the presence or absence of the anomaly based on the standardized gain.

[0010] In the above-described abnormality detection device, the abnormality may be a pinch cut occurring in a tire included in the wheel.

[0011] In the above-described abnormality detection device, the abnormality may be loosening of a fixing member that attaches and fixes a wheel included in the wheel assembly to an axle.

[0012] In the anomaly detection device, the second index may be calculated using at least one of a sum and a sum of squares of absolute values ​​of the standardized gains from first to mth orders.

[0013] The anomaly detection device may further include a determination unit that determines the presence or absence of the anomaly based on the calculated second index.

[0014] In the anomaly detection device, the determination unit may set a threshold value for determining the presence or absence of the anomaly based on a probability distribution that the second index follows when the anomaly is not present.

[0015] The abnormality detection device may further include an alarm output unit that outputs an alarm when it is determined that the abnormality exists.

[0016] In the above-mentioned abnormality detection device, the signal representing the rotational speed of the wheel may be a signal output by a rotational speed sensor mounted on a vehicle, and the rotational speed sensor may detect at least one of a magnetic field and light that changes in accordance with the rotation of the wheel.

[0017] An anomaly detection method according to an aspect of the present invention is an anomaly detection method executed by a computer to detect an anomaly occurring in a wheel, and includes the following: Also, an anomaly detection program according to an aspect of the present invention is an anomaly detection program to detect an anomaly occurring in a wheel, and causes a computer to execute the following: The signal representing the rotation speed of the wheel is sequentially acquired as a pulse having a rising edge. Calculating a first index representing the temporal variation in the rise of each of the pulses corresponding to one rotation of the wheel. By performing frequency analysis on the first index calculated for each of the pulses, a frequency spectrum of rotation orders from 1st order to mth order (where m≧1) of the first index is calculated. Standardizing the gain of each rotation order of the frequency spectrum using the average value and standard deviation of the gain of each rotation order when there is no abnormality. Calculating a second index for determining the presence or absence of the abnormality based on the standardized gain. [Effects of the Invention]

[0018] Abnormalities, including wheel rattle and tire damage, even if minor enough to go unnoticed by the driver, appear in the frequency components of the signal representing the wheel rotation speed. More specifically, the abnormality causes fluctuations in the rise time of pulses representing the wheel rotation speed, which have frequency components different from those in normal conditions. According to the present invention, the gain for the rotation order of the frequency spectrum of this variation is standardized using the average value and standard deviation of the gain for the rotation order of the frequency spectrum in normal conditions. This improves sensitivity to changes in the gain for the rotation order, and the presence or absence of an abnormality is determined using a second index calculated based on this, allowing for more appropriate abnormality detection. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a schematic diagram showing an abnormality detection device according to an embodiment of the present invention mounted on a vehicle; [Figure 2] FIG. 2 is a block diagram showing the electrical configuration of the anomaly detection device. [Figure 3] 10 is a flowchart showing the flow of an abnormality detection process. [Figure 4] 10 is a flowchart showing the flow of an abnormality detection process. [Figure 5A] FIG. 4 is a diagram illustrating pulses acquired from a rotation speed sensor in a normal state. [Figure 5B] 5A and 5B are diagrams illustrating pulses acquired from a rotation speed sensor when an abnormality occurs. [Figure 6A] Graph of first index against tooth number (RR ring). [Figure 6B] Graph of first index against tooth number (RL ring). [Figure 7A] Gain of the frequency spectrum of the rotation order of the first index (RR wheel) under normal and abnormal conditions. [Figure 7B] Gain of the frequency spectrum of the rotational order of the first index (RL wheel) under normal and abnormal conditions. [Figure 8] 10 is a histogram of the second index obtained from an experiment conducted by the inventor. [Figure 9A] FIG. 1 is a diagram illustrating the situation of an experiment conducted by the inventor. [Figure 9B] 1 is a cross-sectional view of a square timber used in an experiment conducted by the inventor. [Figure 10] Graph of the second index against time for the FR and FL rings. DETAILED DESCRIPTION OF THE INVENTION

[0020] An anomaly detection device, an anomaly detection program, and an anomaly detection method according to an embodiment of the present invention will be described below with reference to the drawings.

[0021] <1. Overview> FIG. 1 is a schematic diagram showing an anomaly detection system 1 according to this embodiment mounted on a vehicle. The vehicle is a four-wheel vehicle and includes a left front wheel FL, a right front wheel FR, a left rear wheel RL, and a right rear wheel RR. The vehicle includes a front axle 4a and a rear axle 4b. The wheels FL, FR, RL, and RR are attached to hubs 40 fixed to the left and right ends of the front and rear axles 4a and 4b, respectively. Each of the wheels FL, FR, RL, and RR includes a wheel 7b and a tire 7a mounted thereon. The wheel 7b is attached to and fixed to the hub 40 by a fixing member (not shown). The fixing member is typically a plurality of wheel nuts having threads. The fixing member is engaged with a hub bolt (not shown) on the hub 40 and the screw is appropriately tightened, thereby fixing each wheel to its respective hub 40 without loosening.

[0022] The abnormality detection system 1 includes a control unit 2 as an abnormality detection device, and a sensor unit 3 that detects information indicating the rotation speeds of the wheels FL, FR, RL, and RR. The control unit 2 detects the presence or absence of an abnormality occurring in at least one of the wheels FL, FR, RL, and RR based on a signal output from the sensor unit 3, and has the function of alerting the driver to that effect via a display 6 provided in the vehicle if an abnormality is detected.

[0023] Abnormalities occurring in the wheels FL, FR, RL, and RR include at least one of wheel rattle and damage to the tire 7a included therein. Wheel rattle can be caused by damage to the hub bolt, damage to the wheel nut, or loose wheel nut, with loose wheel nuts being a particularly typical example. When the wheel 7b is properly secured to the hub 40, the wheel nut and hub bolt are undamaged and are properly tightened with the appropriate torque. Loosening between the wheel nut and hub bolt creates mechanical play between the wheel 7b and the hub 40, causing the wheel to rattle. If the vehicle continues to travel in this state, the loosening will progress due to vibrations applied to the wheel, eventually causing the wheel nut to come off the hub bolt, potentially resulting in the wheel falling off the hub 40 (wheel derailment). For this reason, it is important to detect wheel rattle early and eliminate its cause.

[0024] On the other hand, typical damage to the tire 7a is pinch cuts that occur while the tire is in motion. Pinch cuts are caused by the tire 7a being significantly deformed by an impact from an uneven road surface or an obstacle, causing the sidewall to be pinched between the wheel rim flange and the road surface or obstacle (pinching), resulting in the cutting of strength members inside the tire 7a. The strength members are typically the carcass cords that make up the carcass inside the tire 7a. Cuts in the carcass cords are irreparable, and the tire 7a itself must be replaced. In severe pinch cuts, the pinching cuts the rubber along with the carcass cords, causing the tire 7a to rapidly depressurize and making the vehicle unable to run.

[0025] On the other hand, a minor pinch cut does not break the rubber and the air pressure is maintained, making it difficult for the driver to notice. However, even a minor pinch cut can cause the tire to suddenly puncture or burst if the vehicle continues to drive, so it is necessary to discover this early and replace the tire 7a. The abnormality detection system 1 is capable of detecting and issuing an alarm for pinch cuts of any severity, but it is more important to detect and issue an alarm for minor pinch cuts, which are more difficult for the driver to notice.

[0026] <2. Anomaly detection system> 2 is a block diagram showing the electrical configuration of the anomaly detection system 1. Each element of the anomaly detection system 1 will be described below.

[0027] [Control unit] The control unit 2 is an in-vehicle computer in terms of hardware, and includes an I / O interface 8, a CPU (Central Processing Unit) 9, a ROM (Read Only Memory) 10, a RAM (Random Access Memory) 11, and a non-volatile rewritable storage device 12. The I / O interface 8 is a communication device for communicating with external devices such as the sensor unit 3 and the display 6. The ROM 10 stores a program 13 for controlling the operation of each part of the vehicle. The program 13 is written to the ROM 10 from a storage medium 14 such as a CD-ROM. The CPU 9 reads and executes the program 13 from the ROM 10, thereby virtually operating as a signal acquisition unit 20, a first index calculation unit 21, a spectrum calculation unit 22, a standardization unit 23, a second index calculation unit 24, a determination unit 25, and an alarm output unit 26. Details of the operation of each unit 20 to 26 will be described later. The storage device 12 is configured with a hard disk, a flash memory, or the like. The program 13 may be stored in the storage device 12 instead of the ROM 10. The RAM 11 and the storage device 12 are used as appropriate for the calculations of the CPU 9.

[0028] [Sensor unit] The sensor unit 3 includes four rotating bodies 31 that rotate together with the wheels FL, FR, RL, and RR, and four sensors 30 that continuously detect physical quantities that are changed by the rotating bodies 31 and output detection signals. The mounting positions of the rotating bodies 31 are not particularly limited as long as they are mounted so as to be rotatable together with the wheels around the rotation axis of each wheel. The sensors 30 are mounted on non-rotating parts of the vehicle body near the corresponding rotating bodies 31. Each sensor 30 is connected to the control unit 2 via a communication line 5.

[0029] In this embodiment, the rotor 31 is a gear made of a magnetic material, although not limited thereto. In this embodiment, the sensor 30 is a magnetic field sensor incorporating a permanent magnet and a coil, and is fixed to the vehicle body so as to face the circumferential surface of the rotor 31, although not limited thereto. The magnetic field generated by the permanent magnet of the sensor 30 changes as the rotor 31 rotates and teeth pass in front of the sensor 30, generating an induced electromotive force in the coil. The waveform of the induced electromotive force is a sine wave with a frequency proportional to the rotational speed of the rotor 31. This sine wave has the same number of periods as the number of teeth on the rotor 31, with one period corresponding to one rotation of the wheel. The sensor 30 outputs a sine wave signal based on the induced electromotive force to the control unit 2 in real time as a signal representing the rotational speed of the wheel.

[0030] [Display] The display 6 can be realized in any manner, such as a liquid crystal display element, a liquid crystal monitor, a plasma display, or an organic EL (Electro-Luminescence) display, as long as it can inform the driver that an abnormality has occurred in at least one wheel. For example, the display 6 can be configured with four lamps corresponding to each of the wheels FL, FR, RL, and RR, arranged in accordance with the actual arrangement of the wheels. The mounting position of the display 6 can be selected appropriately, but it is preferable to install it in a location that is easy for the driver to see, such as on the instrument panel. If the control unit 2 is connected to a car navigation system, the monitor for the car navigation system can also be used as the display 6. The alarm can be output to the display 6 in the form of an icon, text information, or the like. Alternatively, the alarm can be output as a voice or alarm sound via a speaker installed in the vehicle.

[0031] <3. Anomaly detection processing> The following describes an anomaly detection method for detecting an abnormality in the wheels FL, FR, RL, and RR, which is executed by the anomaly detection system 1 according to this embodiment. Figures 3 and 4 are flowcharts showing the flow of the anomaly detection process. The anomaly detection process described below is roughly divided into a learning phase in which a reference value for determining whether or not an abnormality is present is calculated based on a signal from the sensor 30 and stored in the storage device 12 of the control unit 2, and an anomaly detection phase in which the control unit 2 determines whether or not an abnormality is present based on the signal from the sensor 30 and the reference value.

[0032] The learning phase is initiated, for example, when the driver operates the vehicle's initialization switch, and is repeatedly executed until a certain time has elapsed or while the vehicle has traveled a predetermined distance. The learning phase processing can be initiated, for example, after a vehicle maintenance inspection or when the vehicle is new, when no abnormalities have occurred in any of the wheels and they are assumed to be normal, or when tires are replaced. Once the learning phase is completed, the system transitions to the abnormality detection phase. In the abnormality detection phase, processing is repeatedly executed to determine whether or not there is an abnormality in the wheels while the vehicle is running. Each step is explained below.

[0033] In step S1 of Fig. 3, the signal acquisition unit 20 sequentially acquires the sine wave signal output from the sensor 30 for each wheel as a pulse having a rising edge. The signal acquisition unit 20 samples the sine wave signal at a predetermined cycle to convert it into pulses as shown in Fig. 5A, and calculates the transit time t i Calculate the transit time t i corresponds to the time it takes for the tooth (i) with tooth number i of the rotor 31 to pass in front of the sensor 30. This passing time t i can be measured based on a signal called a "timestamp" supplied from a clock module mounted on the sensor 30, for example.

[0034] Here, the gear pitch of the rotating body 31 is not completely uniform, and there is a variation corresponding to the pitch of each tooth in each passing time ti during one rotation of the rotating body 31 (see FIG. 5A). In step S2, the first index calculation unit 21 calculates the passing time t of the pulse for the number of teeth N corresponding to one rotation of the wheel. i (i=1,2,…,N) are calculated by the average transit time t mean The comparison value x to compare against i is calculated according to the following formula: x i =t i / t mean -1

[0035] In the next step S3, the first index calculation unit 21 calculates the comparison value x iis a signal representing

number

number

number

[0036] The estimated x with hat i (k) is the transit time t for tooth (i) i When there is no abnormality in the wheel, the signal contains frequency components corresponding to the pitch of each tooth. i (k) is an example of a first index representing the temporal variation in the rising edges of pulses corresponding to one rotation of the wheel sequentially acquired by the signal acquiring unit 20. Note that the first index is not limited to this, as long as it is an index representing the temporal variation in the rising edges of pulses corresponding to one rotation of the wheel.

[0037] Step S4 is a step that includes a loop of steps S5 to S7. In step S4, the spectrum calculation unit 22 calculates x i Steps S5 to S7, in which the frequency spectrum of (k) is derived and a gain is calculated based on this, are repeated for the first to mth rotation orders. As a result, gains of the frequency spectrum for the first to mth rotation orders are calculated in step S4. Below, the processing executed in steps S5 to S7 will be explained using the first loop as an example.

[0038] In the first loop, the analysis is performed on the first rotation order component, that is, the component that completes one cycle corresponding to one rotation of the wheel. The spectrum calculation unit 22 first calculates the x i (k) is passed through a band-pass filter to extract components near the first rotation order (step S5).

[0039] In step S6, the spectrum calculation unit 22 applies a window function to the rotation order components extracted in step S5. This process is performed to extract a finite interval prior to calculating the gain in the subsequent step S7. The window function to be applied is not particularly limited, and any known window function such as a Hanning window or a Hamming window can be applied. From the viewpoint of rapid attenuation of side lobes, the Blackman window function is preferable.

[0040] In step S7, the spectrum calculation unit 22 calculates the gain of the first rotation order for the signal multiplied by the window function in step S6. Based on Parseval's theorem, the spectrum calculation unit 22 determines the gain for the first rotation order from the signal on the time axis. At this point, the first loop ends and the second loop begins.

[0041] In the second loop, analysis is performed on the second order rotational order component, i.e., the component that completes two periods corresponding to one rotation of the wheel. The spectrum calculation unit 22 changes the frequency band of the band-pass filter in step S5 to a band-pass filter that passes the frequency band around the second order rotational order component. That is, in step S5 of the second loop, the hatched x corresponding to one rotation of the wheel estimated in step S3 is i From (k), components near the second rotation order are extracted. After that, steps S6 to S7 are executed in the same manner as in the first loop.

[0042] As described above, the spectrum calculation unit 22 repeats steps S5 to S7 for the components that complete j cycles corresponding to one rotation of the wheel while changing the pass frequency band of the band pass filter each time the number of loops increases by 1. As a result, when all loops of step S4 are completed, the gains G for the first to mth rotation orders are calculated. j (j=1, 2, ..., m) is calculated. The spectrum calculation unit 22 calculates the calculated gain G j is stored in the RAM 11 or the storage device 12. The processing from steps S1 to S7 is repeated for the signals for one rotation of each wheel that are sequentially acquired by the signal acquisition unit 20. That is, each time the signals for one rotation of each wheel are sequentially input, the gain G j (j=1,2,...,m) are calculated sequentially.

[0043] Here, the maximum order m of the rotation order analysis is not particularly limited and can be an integer equal to or greater than 1. From the viewpoint of improving the reliability of anomaly detection, m is preferably equal to or greater than 10. In this embodiment, m=20.

[0044] 4, in the next step S8, the standardization unit 23 determines whether or not a reference value for calculating a second index for determining the presence or absence of an abnormality has already been identified and stored in the storage device 12 (whether or not it has been learned) (the second index will be described later). In this embodiment, the reference value is the gain G calculated in step S4. j The average value G for each rotation order jmean and standard deviation s j If the reference value has not yet been identified (NO), the process proceeds to a learning loop in step S9. On the other hand, if the reference value has already been identified (YES), the process proceeds to a standardization loop in step S10. That is, steps S1 to S9 correspond to the learning phase described above, and steps S1 to S13 correspond to the anomaly detection phase described above.

[0045] In step S9, the standardization unit 23 calculates the gain G j For each rotation order, the gain G jAverage value of G jmean and standard deviation s j and store them in the storage device 12. That is, in the learning loop of step S9, the gain G j Average value of G jmean and standard deviation s j The process of calculating and storing the average value G is repeated for the first to mth rotation orders. When step S9 is completed, the loop of steps S1 to S3 and S5 to S7 is executed again m times, and then the loop of step S9 is executed again m times, and the average value G is stored in the storage device 12. jmean and standard deviation s j is updated. When the loop of steps S1 to S3, steps S5 to S7, and step S9 are repeated a predetermined number of times or for a predetermined distance, the learning phase is completed and the process moves to the abnormality detection phase. In the abnormality detection phase, after the loop of steps S1 to S3 and steps S5 to S7 is repeated m times, step S10 is executed.

[0046] In step S10, the standardization unit 23 calculates the gain G obtained in step S4. j The average value G jmean and standard deviation s j The standardized value E j This process can be performed according to the following formula: E j =(G j -G jmean ) / s j

[0047] In step S11, the second index calculation unit 24 calculates a second index Y for determining the presence or absence of an abnormality according to the following formula.

number

[0048] Here, the reason why the presence or absence of an abnormality can be determined using the second index Y will be explained. As described above, the signal representing the rotation speed of the vehicle includes the transit time t based on the pitch specific to the tooth (i) of the rotor 31, as shown in FIG. i On the other hand, when an abnormality occurs in the wheel, the passing time t i The variation in the rotational speed increases. This is thought to be due to the weight balance of the wheel relative to the rotation axis being disrupted by wheel rattle or pinch cut, causing the vibration mode to change from normal. It is possible to perform processing on the rotation signal detected by sensor 30 to correct minute error components, such as those due to manufacturing variations, that are superimposed on the rotation signal from sensor 30. However, if such processing is performed, the weight balance component will also be corrected, which may result in the component due to wheel rattle or pinch cut being corrected as well. For this reason, it is preferable not to perform processing to correct minute error components, such as those due to manufacturing variations, that are superimposed on the rotation signal.

[0049] Figures 6A and 6B are graphs showing experimentally confirmed changes in vibration mode due to wheel rattle and pinch cut. In the experiment, a vehicle (Honda Jade) was fitted with wheels including tires (205 / 60R16, TOYO TRAMPATH J62) with normal air pressure, and the vehicle was driven around a test course (Sumitomo Rubber Industries, Ltd. Okayama Test Course, circulating circuit) at speeds of 35 to 45 km / h in both cases where there were no wheel abnormalities (normal) and where all wheel nuts on one wheel were loosened (abnormal). The wheel abnormality was defined as a state in which all wheel nuts on the rear wheel were loosened, causing them to protrude 1 mm beyond their normal state. Figures 6A and 6B plot the first index versus tooth number for these cases, with Figure 6A being the graph for the rear wheel and Figure 6B being the graph for the rear wheel. In Figure 6A, i.e., the example of the rear wheel, the width of the first index widens both vertically when there is an abnormality, and the overall passing time t iOn the other hand, in the example of the RL wheel in Figure 6B, the first index hardly changes between normal conditions (dotted line) and abnormal conditions (solid line), and it is confirmed that a significant change in the first index occurs in the wheel where an abnormality has occurred.

[0050] 7A and 7B are graphs of the gain versus rotation order (here, m = 24) resulting from a rotation order analysis of the first index in FIGS. 6A and 6B, respectively. The dotted line represents the gain during normal operation, and the solid line represents the gain during abnormal operation. In the example of FIG. 7A (RR wheel), depending on the rotation order, the gain may be lower under abnormal conditions than under normal operation, or there may be little change between the normal and abnormal conditions. This confirms that the amount of gain change with respect to the rotation order varies. For this reason, a method that compares the gain value itself with a threshold value may not be able to properly distinguish between normal and abnormal conditions. On the other hand, in the example of FIG. 7B (RL wheel), it was confirmed that the gain trend hardly changes between normal and abnormal conditions.

[0051] However, as mentioned above, the gain G j The average value G jmean and standard deviation s j By standardizing based on this, the gain at each rotation order can be treated as data that follows a normal distribution with a mean of 0 and a standard deviation of 1. This aligns the weights between rotation orders, making it possible to evaluate the gain changes at each rotation order on the same scale.

[0052] More specifically, the mean values ​​G obtained during the learning phase jmean and standard deviation s j is used as the reference value, and the newly acquired gain G j When normalized based on the reference value, the normalized gain E of each order is j can be assumed to follow a normal distribution with a mean of 0 and a standard deviation of 1, just like the reference data. j The second index Y, which is the sum of squares of , can be assumed to follow a chi-squared distribution with m degrees of freedom.

[0053] On the other hand, when an abnormality occurs in the wheel, the newly acquired gain G j When normalized based on the reference value, the normalized gain E of each order is j is considered to have a high probability of deviating from the center of the normal distribution. Therefore, the second index Y has a significantly low probability of appearing. Therefore, if the significance level in the chi-squared distribution with m degrees of freedom is determined in advance, the alarm threshold of the second index Y for determining whether or not an abnormality exists is also automatically determined. In this embodiment, the determination unit 25 sets the alarm threshold in advance based on the predetermined significance level and degrees of freedom.

[0054] Referring again to FIG. 4, in step S12, the determination unit 25 compares the second index Y calculated in step S11 with the warning threshold value to determine whether or not an abnormality exists. This determination is performed for each of the wheels FL, FR, RL, and RR. If the second index Y for all the wheels is less than the threshold value (YES), the determination unit 25 determines that there is no abnormality in any of the wheels. In this case, the process returns to step S1. On the other hand, if there is one or more second indexes Y that are equal to or greater than the threshold value (NO), the determination unit 25 determines that there is an abnormality in the wheel. In this case, the process proceeds to step S13.

[0055] In step S13, the alarm output unit 26 outputs an alarm via the display device 6. At this time, the display device 6 can issue an alarm by distinguishing which wheel has an abnormality, or can issue an alarm that only indicates that one of the wheels has an abnormality. The alarm may include content that indicates specific measures to take regarding the wheel abnormality that is thought to have occurred, such as urging the driver to check the wheel nuts or to replace the tire 7a.

[0056] <4. Features> According to the anomaly detection system 1 of the above embodiment, the gain for each rotation order is standardized when no anomaly occurs, eliminating the need to set a gain threshold for each rotation order, making it simple and applicable to any vehicle and wheel, particularly any type of tire. Furthermore, even if the gain changes in a decreasing direction or if the gain change is small, this can be detected, allowing for more accurate detection of anomalies than when it is determined that an anomaly has occurred simply because the gain exceeds a threshold, or when it is determined that an anomaly has occurred because the sum of the gains for all rotation orders exceeds a threshold.

[0057] According to the anomaly detection system 1 according to the above embodiment, the standardized gain E j The presence or absence of an abnormality is determined based on the second index Y, which is the cumulative sum of the squares of the two. When there is no abnormality in the wheel, the distribution of the second index Y follows a chi-squared distribution with m degrees of freedom. This makes it possible to determine the threshold for determining the presence or absence of an abnormality from a probability density function determined by the degrees of freedom, which is simple and does not require setting the threshold by conducting experiments under various vehicle and wheel conditions.

[0058] <5. Variations> Although one embodiment of the present invention has been described above, the present invention is not limited to the above embodiment, and various modifications are possible without departing from the spirit of the present invention. For example, the following modifications are possible. Furthermore, the gist of the following modifications can be combined as appropriate.

[0059] (1) Although the vehicle in the above embodiment is a four-wheel vehicle, the number of wheels is not particularly limited, and may be less than four wheels, or may be five or more wheels. The type of vehicle on which the anomaly detection system 1 according to the above embodiment is mounted is also not particularly limited, and may be a passenger car, a commercial vehicle, or the like.

[0060] (2) In the above embodiment, the sensor unit 3 is an electromagnetic pickup type sensor unit including a gear, a permanent magnet, and a coil. However, the sensor unit 3 is not particularly limited as long as it can detect a physical quantity that changes in accordance with the rotational speed of the wheel. For example, the rotor 31 may be a permanent magnet in which north and south poles are alternately arranged in a ring shape at a predetermined pitch, and the sensor 30 may be a Hall element sensor, a magnetoresistive (MR) sensor, a magneto-impedance (MI) sensor, or the like that detects a magnetic field. Furthermore, for example, the sensor unit 3 may be an optical sensor unit that detects light as a physical quantity. In this case, the sensor unit 3 may include a light-emitting element, a sensor 30 as a light-receiving element, and a disk-shaped rotor 31 with slits formed at a predetermined pitch around its circumference.

[0061] (3) In the above embodiment, the signal representing the rotation speed is converted into a pulse having a rising edge on the side of the control unit 2. However, the sensor unit 3 may be configured to output a pulse having a rising edge, and the signal acquiring section 20 of the control unit 2 may acquire this pulse.

[0062] (4) The abnormality detection process of the above embodiment is merely an example and can be modified as appropriate. For example, steps S3 and S6 may be omitted. When step S3 is omitted, the comparison value x i can be used as the first index.

[0063] (5) The second index Y is E as in the above embodiment. j Not limited to the sum of squares of E j In other words, the second index Y may be calculated by at least one of the sum of the absolute values ​​and the sum of the squares. [Example]

[0064] Examples of the present invention will be described below. However, the following examples are merely illustrative and the present invention is not limited thereto.

[0065] <Experiment 1> A vehicle (Honda Jade) was fitted with wheels fitted with tires (205 / 60R16, TOYO TRAMPATH J62) of the correct size and with normal air pressure on each axle, and the vehicle was driven around a test course (Sumitomo Rubber Industries, Ltd. Okayama Test Course, Circular Circuit) at a speed of 35 to 45 km / h, and the anomaly detection method according to the above embodiment was performed under various conditions to examine the distribution of the second index. The conditions were as follows: Condition 1: Normal condition with no abnormalities in any of the wheels Condition 2: All wheel nuts on the rear wheel are loose and protrude 1mm beyond their normal position.

[0066] The histogram of the second index calculated under conditions 1 and 2 is shown in Figure 8. The horizontal axis of Figure 8 shows the value of the second index divided into classes of 10, and the vertical axis shows the occurrence rate of each class. As shown in Figure 8, it was confirmed that the distribution of the second index is clearly distinguishable between conditions 1 (hatched bars) and 2 (solid bars) at the alarm threshold (20 degrees of freedom, t-value = 49.5 at a significance level of 0.01%). This confirmed that loose wheel nuts can be detected using the above anomaly detection method.

[0067] <Experiment 2> A vehicle (PSA Sedan 5008, four-wheel vehicle) was fitted with normally inflated tires (CONTINENTAL CrossContact LX Sport) on each axle with the wheel nuts tightly secured, and the vehicle was driven on a test course (CATARC, China). As shown in Figure 9A, a 1-m-long, 12-cm-wide, and 10-cm-high beam was placed on the test course at a 45-degree angle relative to the vehicle's direction of travel. The cross-section of the beam is shown in Figure 9B. While the vehicle was traveling at approximately 50 km / h, the front-wheel-rear wheel was collided with the beam, causing a pinch cut in the tire. The second index was calculated for the front-wheel and front-wheel wheels.

[0068] The calculated second index is shown in Figure 10. Between 32 and 36 seconds, which is thought to be immediately after the front wheel hit the timber, only the second index for the front wheel increased significantly (note that data from 36 seconds onwards was rejected and not used in calculating the second index). This confirmed that the above anomaly detection method is capable of detecting pinch cuts in tires. [Explanation of symbols]

[0069] 1. Anomaly detection system 2. Control unit (anomaly detection device) 3 Sensor Unit 4a front axle 4b rear axle 5. Communication lines 6 Display 13 Programs 20 Signal intensity acquisition unit 21 1st index calculation section 22 Spectrum calculation unit 23 Standardization Department 24 Second index calculation section 25 Judgment section 26 Alarm output section FL left front wheel FR right front wheel RL Left rear wheel RR Right rear wheel

Claims

1. An abnormality detection device that detects abnormalities occurring in a wheel, a signal acquisition unit that sequentially acquires a signal representing the rotation speed of the wheel as pulses having rising edges; a first index calculation unit that calculates a first index representing a temporal variation in the rising edge of each of the pulses corresponding to one rotation of the wheel; a spectrum calculation unit that calculates frequency spectra of rotation orders from 1st order to mth order (where m≧1) of the first index by frequency analyzing the first index calculated for each of the pulses; a standardization unit that standardizes the gain of each rotation order of the frequency spectrum using an average value and a standard deviation of the gain of each rotation order when no abnormality is present; a second index calculation unit that calculates a second index for determining the presence or absence of an abnormality based on the standardized gain; Equipped with Anomaly detection device.

2. The abnormality is a pinch cut occurring in a tire included in the wheel. The anomaly detection device according to claim 1 .

3. The abnormality is loosening of a fixing member that fixes a wheel included in the wheel to an axle. The anomaly detection device according to claim 1 or 2.

4. The second index is calculated by at least one of the sum of absolute values ​​and the sum of squares of the standardized gains from first to mth orders. The anomaly detection device according to any one of claims 1 to 3.

5. a determination unit that determines whether or not the abnormality exists based on the calculated second index; Further provided with The anomaly detection device according to claim 1 .

6. the determination unit sets a threshold for determining the presence or absence of the abnormality based on a probability distribution that the second index follows when the abnormality is not present. The anomaly detection device according to claim 5 .

7. an alarm output unit that outputs an alarm when it is determined that the abnormality exists; Further provided with The anomaly detection device according to any one of claims 1 to 6.

8. the signal representing the rotational speed of the wheel is a signal output by a rotational speed sensor mounted on the vehicle, The rotational speed sensor detects at least one of a magnetic field and light that changes in response to the rotation of the wheel. The anomaly detection device according to any one of claims 1 to 7.

9. An abnormality detection program that detects an abnormality occurring in a wheel attached to an axle, Sequentially acquiring signals representing the rotational speed of the wheels as pulses having rising edges; calculating a first index representing a temporal variation in the rising edge of each of the pulses corresponding to one rotation of the wheel; calculating frequency spectra of rotation orders from 1st order to mth order (where m≧1) of the first index by frequency analyzing the first index calculated for each of the pulses; standardizing the gain of each rotation order of the frequency spectrum using an average value and a standard deviation of the gain of each rotation order when no abnormality is present; calculating a second index for determining the presence or absence of the abnormality based on the standardized gain; to the computer, Anomaly detection program.

10. 1. A computer-implemented anomaly detection method for detecting an abnormality occurring in a wheel attached to an axle, comprising: Sequentially acquiring signals representing the rotational speed of the wheels as pulses having rising edges; calculating a first index representing a temporal variation in the rising edge of each of the pulses corresponding to one rotation of the wheel; calculating frequency spectra of rotation orders from 1st order to mth order (where m≧1) of the first index by frequency analyzing the first index calculated for each of the pulses; standardizing the gain of each rotation order of the frequency spectrum using an average value and a standard deviation of the gain of each rotation order when no abnormality is present; calculating a second index for determining the presence or absence of the abnormality based on the standardized gain; Including, Anomaly detection methods.

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