Track state estimation method, track state estimation device, and vehicle

Abstract

To provide a track state estimation method which can simply and accurately inspect and measure a track using an operational vehicle.SOLUTION: A track state estimation method comprises: an inclination angle estimation step of estimating a horizontally inclined angle and a longitudinally inclined angle of a wheel shaft based on three axial accelerations and three axial angular velocities measured by an inertia measurement device installed in an axel box of a vehicle; and a track state calculation step of calculating track state information indicating a track state based on the horizontally inclined angle and the longitudinally inclined angle of the wheel shaft.SELECTED DRAWING: Figure 8

Description

[Technical field]

[0001] The present invention relates to a track state estimation method, a track state estimation device, and a vehicle. [Background technology]

[0002] One of the causes of derailment of vehicles running on rails is irregularity of the rails relative to their original position (hereinafter referred to as "track irregularity"). Since track irregularity directly affects derailment, it is important to properly inspect and maintain track irregularity in order to reduce the risk of derailment.

[0003] Inspection of track displacement (hereinafter also referred to as "track inspection") is carried out, for example, using a hand-pushed track inspection device. However, when the rail to be inspected becomes long, it becomes difficult to inspect it frequently. In addition, track inspection using a hand-pushed track inspection device is a static inspection performed under no-load conditions, which differs from the conditions during a derailment. For this reason, it is desirable to be able to carry out dynamic inspections frequently while the vehicle is running.

[0004] One method for performing dynamic inspections frequently while a vehicle is running is to use an operating vehicle to perform track inspection. For example, Patent Document 1 discloses a track inspection device that uses a vehicle, in which a detector unit made up of various detectors is attached to the bogie frame of the vehicle, and five track irregularity items are measured based on the inertial sine wave method while the vehicle is running. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Patent No. 3411861 Summary of the Invention [Problem to be solved by the invention]

[0006] However, while the technology described in Patent Document 1 can measure five types of track irregularity using a detector unit, the detector unit measurement mechanism is complex and expensive. In addition, because a large detector unit must be mounted, the measurement position may be far from the wheels depending on the structure of the vehicle, and there is a possibility that rail deformation when the vehicle is loaded may not be fully detected.

[0007] Therefore, the present invention has been made in consideration of the above problems, and an object of the present invention is to provide a track condition estimation method, a track condition estimation device, and a vehicle that are capable of easily and accurately measuring a track by using an operating vehicle. [Means for solving the problem]

[0008] In order to solve the above problems, according to one aspect of the present invention, there is provided a track condition estimation method including: a tilt angle estimation step of estimating a horizontal tilt angle and a longitudinal tilt angle of a wheelset based on a three-axis acceleration and a three-axis angular velocity measured by an inertial measurement unit installed on an axle box of a vehicle; and a track condition calculation step of calculating track condition information representing a track condition based on the horizontal tilt angle and the longitudinal tilt angle of the wheelset.

[0009] In the inclination angle estimation step, a translational acceleration component during vehicle travel may be extracted by filtering processing from the three-axis acceleration measured by the inertial measurement unit, and an error covariance matrix of an observation model of a Kalman filter may be set based on the extracted translational acceleration component. The horizontal inclination angle and longitudinal inclination angle of the wheelset may be estimated from the three-axis acceleration and three-axis angular velocity using a Kalman filter formulated from a state space model configured by the observation model in which the error covariance matrix has been set. In the track state calculation step, a flatness deviation may be calculated as track state information based on the horizontal inclination angle of the wheelset.

[0010] Furthermore, the translational acceleration components may be extracted by band-pass filtering to remove frequency components in which track irregularity dominates, from the lateral acceleration and longitudinal acceleration among the three-axis accelerations.

[0011] The error covariance matrix may be set by adding values ​​obtained by multiplying the extracted translational acceleration components by predetermined coefficients to the diagonal elements.

[0012] In order to solve the above problem, according to another aspect of the present invention, there is provided a track condition estimation device including: a tilt angle estimation unit that estimates a horizontal tilt angle and a longitudinal tilt angle of a wheelset based on a three-axis acceleration and a three-axis angular velocity measured by an inertial measurement unit installed on an axle box of a vehicle; and a track condition calculation unit that calculates track condition information that represents a track condition based on the horizontal tilt angle and the longitudinal tilt angle of the wheelset.

[0013] Furthermore, in order to solve the above-mentioned problems, according to another aspect of the present invention, there is provided a vehicle including an inertial measurement unit installed on an axle box of the vehicle, and a track state estimation device having a tilt angle estimation unit that estimates the horizontal tilt angle and longitudinal tilt angle of the wheel set based on the three-axis acceleration and three-axis angular velocity measured by the inertial measurement unit, and a track state calculation unit that calculates track state information that represents the track state based on the horizontal tilt angle and longitudinal tilt angle of the wheel set. Effect of the Invention

[0014] As described above, according to the present invention, track inspection can be performed easily and accurately by using an operating vehicle. [Brief description of the drawings]

[0015] [Figure 1] 1 is a schematic diagram showing a schematic configuration of a vehicle according to an embodiment of the present invention; [Diagram 2] FIG. 1 is a schematic diagram for explaining a planarity displacement. [Diagram 3] FIG. 2 is an explanatory diagram for explaining measurement values ​​obtained by an inertial measurement unit. [Figure 4] FIG. 2 is a schematic cross-sectional view of a wheelset for explaining values ​​measured by an inertial measurement unit. [Diagram 5] 13 is a graph showing an example of calculation of an index Rx representing translational acceleration. [Figure 6] 13 is a graph showing an example of calculation of an index Ry representing translational acceleration. [Figure 7] FIG. 2 is a functional block diagram showing a configuration of a track state estimation device according to the embodiment. [Figure 8] 4 is a flowchart illustrating an example of a track state estimation method according to the embodiment. [Figure 9] 13 is a graph showing the estimation result of planarity deviation in the example. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0016] Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In this specification and the drawings, components having substantially the same functional configurations are designated by the same reference numerals, and duplicated explanations will be omitted.

[0017] [1. Vehicle configuration] First, a schematic configuration of a vehicle 1 according to an embodiment of the present invention will be described with reference to Fig. 1. Fig. 1 is a schematic diagram showing a schematic configuration of a vehicle 1 according to this embodiment.

[0018] A vehicle 1 according to this embodiment is configured to be capable of running on a pair of extended rails 5. As shown in Fig. 1, the vehicle 1 is configured with a car body section 10 consisting of a body structure which is the main structural part of the vehicle, and a bogie section 20 which supports the car body section 10 and has a running mechanism. In the following, the vehicle travel direction (front-rear direction) is defined as the X direction, the width direction (left-right direction) as the Y direction, and the height direction (up-down direction) as the Z direction.

[0019] Four wheel pairs are arranged in the vehicle travel direction on the bogie section 20 of the vehicle 1 shown in Fig. 1. Each wheel pair is formed by connecting two wheels 23 that are paired in the width direction by a wheel set 25. An axle box 27, which is a bearing portion of the wheel set 25, is provided on both ends of each wheel set 25. The axle box 27 is connected to the bogie frame 21 by a coil spring 29. Note that in Fig. 1, the axle box 27 and the coil spring 29 are shown for only one wheel 23 in order to show the wheel set 25, but all of the wheels 23 are equipped with the axle box 27 and the coil spring 29.

[0020] The vehicle 1 according to this embodiment is equipped with an inertial measurement unit (IMU) 30 on the axle box 27 of at least one wheel 23. The inertial measurement unit 30 is a device that detects three-dimensional inertial motion (translational motion and rotational motion in three orthogonal axial directions). The inertial measurement unit 30 detects translational motion using an acceleration sensor and detects rotational motion using an angular velocity sensor. Measurement values ​​by the inertial measurement unit 30 are output to a track condition estimation device (track condition estimation device 100 in FIG. 3) that estimates the track condition of the rail 5 on which the vehicle 1 travels.

[0021] Moreover, the vehicle 1 according to this embodiment is equipped with a position detection device (not shown) that detects the absolute position of the vehicle 1 on the rails 5. The position detection device may be, for example, an RFID (Radio Frequency Identification) reader. In this case, the absolute position of the vehicle 1 can be detected by reading an RFID tag that is set in advance at the middle position in the width direction of the pair of rails 5 with the RFID reader. Note that the method of detecting the absolute position of the vehicle 1 is not limited to this example, and well-known technology may be used. For example, position information obtained using radio waves from an artificial satellite may be used as the absolute position of the vehicle 1.

[0022] [2. Estimation of orbital state] The track conditions of a rail include rollarity, alignment, elevation, level, and gauge variation. In the following, a case will be described in which rollarity, which contributes greatly to derailment, is estimated as a track condition. Rollarity, as shown in FIG. 2, refers to the state of twist with respect to the plane of the track, and is expressed by the level difference CL(x) between the left and right rails 5 at two points (for example, points P1 and P3, and points P2 and P4) that are separated by a predetermined distance d in the direction of vehicle travel. Specifically, since the fixed wheelbase of a general vehicle is about 5 m, rollarity Tw(x) is expressed by the following formula (1).

[0023] Tw(x) = CL(x) - CL(x - 5) (1)

[0024] Here, assuming that there is no floating of the wheels 23, the vertical displacement of the wheel set 25 can be considered to be equal to the vertical displacement of the track. Therefore, by estimating the roll angle θx of the wheel set 25 based on the measurement value of the inertial measurement unit 30 installed on the axle box 27, the level difference CL(x) can be estimated from the following equation (2). In addition, in equation (2), L represents the standard gauge length.

[0025] CL(x) = Ltanθx (2)

[0026] In this embodiment, the track irregularity immediately below the wheels is measured using only the measurement values ​​of the inertial measurement unit 30 installed on the axle box 27. By installing the inertial measurement unit 30 on the axle box 27, the track irregularity immediately below the wheels can be measured, but noise such as impulsive vibrations caused by vehicle running is also likely to be measured. For this reason, the track irregularity estimated from the measurement values ​​of the inertial measurement unit 30 is likely to include errors due to acceleration components (hereinafter also referred to as "translational acceleration components") including impulsive vibration responses caused by vehicle running, which are measured by the inertial measurement unit 30 translating in the measurement axis direction. Therefore, in this embodiment, a method is presented for estimating runway irregularity by reducing the influence of errors due to the translational acceleration components during vehicle running, which are included in the measurement values ​​of the inertial measurement unit 30.

[0027] Hereinafter, a track state estimation technique for estimating runway deviation as a track state, a track state estimation device that executes a track state estimation process based on the track state estimation technique, and a track state estimation method will be described in detail.

[0028] [2-1. Orbit state estimation method] First, a track state estimation method for estimating track states according to this embodiment will be described with reference to Figs. 3 to 6. Fig. 3 is an explanatory diagram for explaining measured values ​​by the inertial measurement unit 30. Fig. 4 is a schematic cross-sectional diagram of a wheel set for explaining measured values ​​by the inertial measurement unit 30. Fig. 5 is a graph showing an index R representing translational acceleration. x 6 is a graph showing an example of calculation of the index R y 1 is a graph showing an example of calculation of

[0029] In the following description, the measurement value of the three-axis acceleration at time k measured by the inertial measurement unit 30 is a xk , a yk , a zk , the measured values ​​of the three-axis angular velocity are ω xk , ω yk , ω zk As shown in FIG. 3, the roll angle at time k is θ k , pitch angle is φ k , yaw angle is ψ k Let us assume that.

[0030] In this embodiment, a Kalman filter is used to estimate the roll angle θx of the wheel set 25 from the measurement values ​​of the inertial measurement unit 30. The Kalman filter is one of the data assimilation methods, and is a method of sequentially estimating a state quantity that minimizes an error with an observation quantity by using Bayesian estimation. The roll angle estimated value and pitch angle estimated value obtained from the measurement values ​​of the three-axis acceleration are used as the observation quantities, and the Kalman filter can be formulated from a time update equation based on the integration of the roll angular velocity and pitch angular velocity in the absolute coordinates.

[0031] State vector x k is defined by the following equation (3).

[0032]

number

[0033] Also, the observation vector y k is the measured value of the three-axis acceleration shown in Fig. 4. xk , a yk , a zk The low-frequency components of the gravitational acceleration g are extracted and projected onto the three axes, and the observation vector y can be expressed as in the following equation (4). k In this case, θ 0k is the estimated roll angle at time k, φ 0k is the estimated pitch angle at time k.

[0034]

number

[0035] 3-axis acceleration measurement value a xk , a yk , a zk The gravitational acceleration projection component is a low-frequency acceleration component, for example, a component of 0 to 1 Hz. The tilt angle estimation unit 110 uses, for example, a low-pass filter to estimate the three-axis acceleration measurement value a xk , a yk , a zk The gravitational acceleration projected component can be extracted from

[0036] The time update equation is derived from the integral of the angle in the absolute coordinate system, and is a nonlinear relational equation for the state vector. k-1 We use an Extended Kalman Filter that performs linearization around v. In this case, the system equation is expressed by the following equation (5). k is the process noise.

[0037]

number

[0038] In addition, the observation noise w k , the observation matrix H k When , the observation equation is expressed by the following equation (7).

[0039]

number

[0040] The measured value a of the three-axis acceleration is calculated by the Kalman filter based on the state space model expressed by the above equations (5) to (7). xk , a yk , a zk , and the measured value of the three-axis angular velocity ω xk , ω yk , ω zk From the roll angle θ k and pitch angle φ kcan be estimated.

[0041] In this embodiment, in order to reduce the influence of errors due to translational acceleration components during vehicle travel included in the measurement values ​​of the inertial measurement unit 30, the tilt angle estimation unit 110 estimates the measurement value a of the three-axis acceleration during vehicle travel, which is an error factor. xk , a yk , a zk A translational acceleration component is extracted by filtering processing, and an observation error covariance matrix of the Kalman filter is set based on the extracted translational acceleration component.

[0042] The inventors of the present application have investigated the error factors of the angle estimation value and found that the impact response that occurs when the wheel flange comes into contact with the rail at a sharp curve section is the error factor of the angle estimation value. In the above formula (5) that calculates the angle from the integral of the angular velocity and the above formula (7) that calculates the angle from the low-frequency component of the acceleration (i.e., the projected component of the gravitational acceleration), the process noise v k and the observation noise w k is assumed to be Gaussian noise with zero mean. This noise setting represents the error component of each equation. The process noise v k means the measurement error and integration error of the angular velocity data. The observation noise w k means the measurement error of the acceleration data and the estimation error of the gravitational acceleration projection component extracted from the acceleration.

[0043] When the wheel flange comes into contact with the rail on a sharp curve, both the angular velocity measurement and the acceleration measurement may show an impulsive response, but the impulsive response is characterized by being prominent in all frequency bands. Here, the above formula (5) is valid regardless of the presence or absence of an impulsive response, but the observation vector y in the above formula (7) k is an angle calculated from the gravitational acceleration projection component, and becomes an error factor when translational acceleration acts on the inertial measurement unit 30. That is, in such a traveling section, the observation noise w kIt is considered that by setting to a large value, the reliability of the above formula (7) is reduced (in other words, the reliability of the above formula (5) is increased), and the actual response can be appropriately expressed.

[0044] Therefore, in this embodiment, the observation noise w k In order to improve the accuracy of the angle estimation value, the index R representing the translational acceleration is set. x , R y We introduce the observation noise w according to the magnitude of the translational acceleration component. k The covariance matrix R of

[0045] Observation vector y k Roll angle θ 0k and pitch angle φ 0k The calculation accuracy of the left and right acceleration a yk , longitudinal acceleration a xk In both cases, errors can occur due to translational acceleration. yk For the shock response when passing through a sharp curve, the shock response appears as translational acceleration, and the longitudinal acceleration a xk For the two, the acceleration and deceleration required to keep the vehicle speed constant appears as translational acceleration. x , R y Define.

[0046] Index R x is the measured value of the longitudinal acceleration a x Only high frequency components are extracted from the index R using a band pass filter, and the extracted values ​​are squared and then the moving average of a specified interval (number of moving average samples) is taken. y is the measured value of the lateral acceleration a y Only high frequency components are extracted from the index R using a band pass filter, and the extracted values ​​are squared and then the moving average of a specified interval (number of moving average samples) is taken. x , R ySince is an index for correcting low-frequency response (i.e., the tilt angle estimated from the gravitational acceleration projection component), the estimated value is stabilized by squaring it and taking the moving average to remove high frequencies. For example, the high-frequency components to be extracted may be frequency components of 1 to 7 Hz, and the number of moving average samples may be 250 sample times.

[0047] The shock response or the response due to acceleration / deceleration is a forced response acting from the outside, and it can be assumed that it is dominant in all frequency components. On the other hand, it is considered that the components due to the angle change are dominant in the low frequency band, for example, less than 1 Hz, and the measurement noise is dominant in the high frequency band, for example, more than 7 Hz. For this reason, in this embodiment, the frequency components in these bands are removed using a bandpass filter.

[0048] In detail, assuming that the vehicle speed is almost constant, the average vehicle speed in the measurement data to be filtered is V ave [m / s], maximum spatial frequency of track irregularity F space [cycle / m], the cutoff frequency F time [Hz] can be expressed by the following formula (8).

[0049] F time =F space ×V ave (8)

[0050] The characteristic of the planarity deviation is approximately the maximum spatial frequency F space = 1, vehicle speed V ave = 1. Therefore, in the estimation of flatness displacement, the cutoff frequency F time is set to 1Hz.

[0051] High frequency cutoff frequency F time_h can be expressed as an empirical formula with generality as shown in the following equation (9).

[0052] F time_h =7×Fspace ×V ave (9)

[0053] Index R x , R y The number of moving average samples s can be empirically expressed by the following formula (10) using the sampling frequency fs [Hz] of the measurement values.

[0054] s = fs / (2 × F time ) ···(10)

[0055] For example, the sampling frequency fs=500Hz, the cutoff frequency F time = 1Hz, the number of moving average samples s is 250 sample times.

[0056] Figure 5 shows the index R representing the translational acceleration. x Fig. 6 shows an example of the translational acceleration index R y In FIG. 5, the indicator R x In the locations where the value of is large, the vehicle is accelerated or decelerated to keep the vehicle speed constant. y Locations with large values ​​indicate that the train is being affected by the shock caused by passing through a sharp curve.

[0057] Next, the calculated index R x , R y Using the above, the observation noise w according to the magnitude of the translational acceleration component k The error covariance matrix R(k) may be set by adding a value obtained by multiplying the extracted translational acceleration component by a predetermined coefficient to the diagonal components. Specifically, the observation noise w k The variance-covariance matrix R(k) of y (k), R x (k) and index R y (k), R x Coefficient K for (k) y , K x Using this, it is set as shown in the following formula (11).

[0058]

number

[0059] Moreover, the process noise Q may be set constant regardless of time, for example, as shown in the following equation (12).

[0060]

number

[0061] In addition, the coefficient K y , K x is the index R y (k), R x (k) is a value for setting the noise variance of the observation equation expressed by the above formula (7), and cannot be derived theoretically. y , K x The value that maximizes the estimation accuracy of the deviation from planarity is set as the optimal value for the process noise Q. The process noise Q is also appropriately set based on the value that maximizes the estimation accuracy of the deviation from planarity.

[0062] In this manner, in this embodiment, the observed noise w k The variance-covariance matrix R(k) is set as shown in the above equation (11), and the observation noise w k is sequentially set according to the magnitude of the translational acceleration component. This makes it possible to reduce the influence of errors due to the translational acceleration component during vehicle travel, which is included in the measurement value by providing the inertial measurement unit 30 on the axle box 27. As a result, the observation vector y k , i.e., the estimated roll angle θ 0k and the estimated pitch angle φ 0k can be calculated with high accuracy. It is expressed by equations (5) to (7), and the observation noise w k The roll angle θ is calculated by a Kalman filter based on a state space model in which the variance-covariance matrix R(k) of k and pitch angle φ kOnce estimated, the planarity deviation Tw(x) can be calculated using the above equations (1) and (2).

[0063] [2-2. Orbit state estimation device] The configuration of a track state estimation device 100 that estimates a track state based on the above-mentioned track state estimation method will be described with reference to Fig. 7. Fig. 7 is a functional block diagram showing the configuration of the track state estimation device 100 according to this embodiment. As shown in Fig. 7, the track state estimation device 100 has an inclination angle estimation unit 110 and a track state calculation unit 120.

[0064] The inclination angle estimation unit 110 estimates the three-axis acceleration a measured by the inertial measurement unit 30 installed on the axle box 27 of the vehicle 1. xk , a yk , a zk and 3-axis angular velocity ω xk , ω yk , ω zk Based on this, the horizontal inclination angle (roll angle θ k ) and the front-rear tilt angle (pitch angle φ k ) is estimated.

[0065] First, the tilt angle estimation unit 110 estimates the three-axis acceleration measurement value a xk , a yk , a zk The tilt angle estimation unit 110 extracts the gravitational acceleration projection component and the translational acceleration component from the three-axis acceleration measurement value a xk , a yk , a zk The translational acceleration component extracted from the x , R y Calculate the observation noise w at time k using the above formula (11). k Then, the tilt angle estimation unit 110 uses the set variance-covariance matrix R(k) in the above equation (7) to estimate the observation vector y k (Estimated roll angle θ 0k and the estimated pitch angle φ 0k Furthermore, the tilt angle estimation unit 110 calculates the observation noise w kThe roll angle θ is calculated by a Kalman filter based on a state space model in which the variance-covariance matrix R(k) of k and pitch angle φ k The inclination angle estimation unit 110 calculates the roll angle θ of the wheel set 25. k and pitch angle φ k to the orbit state calculation unit 120.

[0066] The track condition calculation unit 120 calculates the horizontal inclination angle (roll angle θ k ) and the front-rear tilt angle (pitch angle φ k In this embodiment, the track condition information is calculated as the track condition information. When calculating the track condition information as the track condition information, the track condition calculation unit 120 calculates the roll angle θ of the wheel set 25 calculated by the tilt angle estimation unit 110. k Based on this, the track state calculation unit 120 calculates the track deviation Tw(x) using the above formulas (1) and (2). The track state calculation unit 120 outputs the calculated track deviation Tw(x) to, for example, the output device 200. The output device 200 may be, for example, a display device such as a display.

[0067] The track state estimation device 100 according to this embodiment is configured with various processors such as a CPU (Central Processing Unit) and a DSP (Digital Signal Processor), and the above functions can be realized by operating the processors in accordance with predetermined programs.

[0068] The track state estimation device 100 according to the present embodiment may be installed in the vehicle 1 or may be installed in a separate location. xk , a yk , a zk and the measured value of the three-axis angular velocity ω xk , ω yk , ω zk are recorded in a storage device (not shown) in association with the absolute position of the vehicle 1 on the rail 5 detected by the position detection device. The track state estimation device 100 uses the measured values ​​a of the three-axis acceleration recorded in the storage device.yk , a zk , 3-axis angular velocity measurement value ω xk , ω yk , ω zk , and by obtaining the absolute position of vehicle 1, the track state can be estimated.

[0069] [2-3. Orbit state estimation method] A track state estimating method implemented by the track state estimating device 100 according to this embodiment will be described with reference to Fig. 8. Fig. 8 is a flowchart showing an example of the track state estimating method according to this embodiment.

[0070] (S10, S20: Acquisition of 3-axis acceleration and 3-axis angular velocity) First, the three-axis acceleration a xk , a yk , a zk and 3-axis angular velocity ω xk , ω yk , ω zk The three-axis acceleration and three-axis angular velocity measurements a are measured by the inertial measurement unit 30 (S10, S20). xk , a yk , a zk , ω xk , ω yk , ω zk is output to the inclination angle estimation unit 110 of the orbit state estimation device 100.

[0071] (S30: Extraction of gravitational acceleration projection component) Next, the tilt angle estimation unit 110 calculates the measured value a of the three-axis acceleration. xk , a yk , a zk The gravitational acceleration projection component is extracted from (S30). xk , a yk , a zk The gravitational acceleration projection component is a low-frequency acceleration component, for example, a component of 0 to 1 Hz. The tilt angle estimation unit 110 uses, for example, a low-pass filter to estimate the three-axis acceleration measurement value a xk , a yk , a zk The gravitational acceleration projected component can be extracted from

[0072] (S40: Extraction of translational acceleration components) In addition, the tilt angle estimation unit 110 estimates the three-axis acceleration measurement value a xk , a yk , a zk The translational acceleration component is extracted from (S40). xk , a yk , a zk The translational acceleration component is a high-frequency acceleration component, for example, a component of 1 to 7 Hz. The tilt angle estimation unit 110 uses, for example, a band-pass filter to filter the measured value a of the three-axis acceleration. xk , a yk , a zk As described above, for the planarity deviation, the translational acceleration component, which is the acceleration component caused by the impact that prevails when the vehicle 1 is traveling on a curve, can be extracted from the estimated value θ of the roll angle. k and the estimated pitch angle φ k It has been found that the inclination angle estimation unit 110 extracts the translational acceleration component and calculates an index R x , R y Calculate.

[0073] (S50: Estimation of inclination angle) Next, the inclination angle estimation unit 110 estimates the inclination angle θ (here, the roll angle θx) of the wheel set 25 (S50). The roll angle θx of the wheel set 25 can be calculated using a Kalman filter based on the state space model expressed by the above equations (5) to (7). Here, the inclination angle estimation unit 110 estimates the index R x , R y Using the above formula (11), the observation noise w k This sets the variance-covariance matrix R(k) of the observation noise w k is sequentially set according to the magnitude of the translational acceleration component. As a result, by providing the inertial measurement unit 30 on the axle box 27, the influence of errors due to the translational acceleration component during vehicle running, which is included in the measurement value, is reduced, and the observation vector y k (Estimated roll angle θ 0kand the estimated pitch angle φ 0k ) can be calculated with high accuracy.

[0074] The tilt angle estimation unit 110 is represented by equations (5) to (7), and the observation noise w k The roll angle θ is calculated by a Kalman filter based on a state space model in which the variance-covariance matrix R(k) of k and pitch angle φ k Then, the tilt angle estimation unit 110 calculates the calculated roll angle θ of the wheel set 25. k and pitch angle φ k to the orbit state calculation unit 120.

[0075] (S60: Estimation of orbital state information) Then, the track state calculation unit 120 calculates the horizontal inclination angle (roll angle θ k ) and the front-rear tilt angle (pitch angle φ k ) to calculate track condition information representing the track condition (S60). In this embodiment, the track condition information is calculated as the track condition information. When calculating the track condition information as the track condition information, the track condition calculation unit 120 calculates the roll angle θ k The track state calculation unit 120 calculates the track deviation Tw(x) using the above formulas (1) and (2) based on the above. The track state calculation unit 120 outputs the calculated track deviation Tw(x) to the output device 200.

[0076] [3. Summary] The vehicle according to one embodiment of the present invention, and the track condition estimation device and the track condition estimation method for estimating the turnability as the track condition have been described above. According to this embodiment, an inertial measurement unit is installed on the axle box of the vehicle, and the horizontal inclination angle and the longitudinal inclination angle of the wheel set are estimated based on the three-axis acceleration and the three-axis angular velocity measured by the inertial measurement unit. The horizontal inclination angle and the longitudinal inclination angle of the wheel set can be estimated by a Kalman filter. In this case, in order to reduce the influence of errors due to the translational acceleration component during vehicle running contained in the measurement value of the inertial measurement unit, the translational acceleration component that is an error factor is extracted from the measurement value of the three-axis acceleration during vehicle running by filtering processing, and the observation error covariance matrix of the Kalman filter is sequentially set based on the extracted translational acceleration component. This makes it possible to accurately estimate the horizontal inclination angle and the longitudinal inclination angle of the wheel set. By estimating the horizontal inclination angle and the longitudinal inclination angle of the wheel set with high accuracy, the estimation accuracy of the turnability can be improved. In addition, the three-axis acceleration and the three-axis angular velocity can be measured by running the vehicle, so that the measurement can be easily performed. EXAMPLES

[0077] In order to verify the effectiveness of the track state estimation method according to the above embodiment, the estimated values ​​of track roughness by the track state estimation method were evaluated. The vehicle used for the verification was a vehicle with the configuration shown in Fig. 1, and an inertial measurement unit was provided on each of the axle boxes of the pair of wheels at the front of the vehicle in the traveling direction (X direction). As the track conditions, the running section was set to a curved section (R = 100 m) with a total length of 200 m and a part of which had a track roughness of up to about 40 mm.

[0078] In this verification, the planarity measurement value measured by a hand-operated track inspection device was taken as the true value. As an example, the planarity measurement value was estimated using the track state estimation method according to the above embodiment. At this time, the coefficient K in the above formula (11) y , K x We searched for the value that would give the highest accuracy in estimating the planarity displacement. y , K x Both are 1.0×10 -6 The coefficient K y , Kx The variance-covariance matrix R was set based on the above. The process noise Q was expressed by the above formula (12).

[0079] As a comparative example, the observation noise w k is a fixed value, and the observation vector y k In other words, in the comparative example, the vehicle is estimated in a state where the acceleration component (translational acceleration component) caused by the impact that is prominent when the vehicle is traveling on a curve remains as noise. In the comparative example, the variance-covariance matrix R and the process noise Q are as follows.

[0080]

number

[0081] Fig. 9 shows the track roughness estimated in the example and the comparative example. As shown in Fig. 9, in the comparative example, the estimated track roughness deviated from the true value, but in the example, the estimated track roughness was almost equal to the true value. This shows that the application of the track state estimation method according to the above embodiment reduces the influence of errors due to translational acceleration, including the influence of shock response during vehicle travel, and improves the estimation accuracy of track roughness.

[0082] Although the preferred embodiment of the present invention has been described in detail above with reference to the accompanying drawings, the present invention is not limited to such an example. It is clear that a person having ordinary knowledge in the technical field to which the present invention pertains can conceive of various modified or altered examples within the scope of the technical ideas described in the claims, and it is understood that these also naturally belong to the technical scope of the present invention.

[0083] For example, in the above embodiment, the measured value a of the three-axis acceleration xk , a yk , a zk The low-frequency components of acceleration, which are the gravitational acceleration projection components of the 0 to 1 Hz components, are used as the measured values ​​of the three-axis acceleration a xk , a yk, a zk The high-frequency acceleration components, which are the translational acceleration components, are set to 1 to 7 Hz, but the present invention is not limited to this example. The frequency bands of the low-frequency acceleration components and high-frequency acceleration components are set assuming a low-speed vehicle used to transport heavy materials in a steelworks, for example. The frequency bands of the low-frequency acceleration components and high-frequency acceleration components may be set appropriately according to the vehicle's traveling speed, for example, based on the above formulas (8) and (9). Index R x , R y The number of moving average samples s may also be set based on the above formula (10). [Explanation of symbols]

[0084] 1 vehicle 5 Rail 10 Body 20 Bogie section 21 Bogie frame 23 wheels 25 wheel set 27 Axle box 29 Coil spring 30 Inertial Measurement Unit 100 Orbit state estimation device 110 Tilt angle estimation part 120 Orbital state calculation unit 200 Output Device

Claims

1. an inclination angle estimating step of estimating a horizontal inclination angle and a longitudinal inclination angle of a wheel set based on a three-axis acceleration and a three-axis angular velocity measured by an inertial measurement device installed on an axle box of the vehicle; a track condition calculation step of calculating track condition information representing a track condition based on the horizontal inclination angle and the longitudinal inclination angle of the wheel set; Including, In the tilt angle estimation step, extracting a translational acceleration component during vehicle travel from the three-axis acceleration measured by the inertial measurement unit through a filtering process; setting an error covariance matrix of an observation model of a Kalman filter based on the extracted translational acceleration component; estimating a horizontal inclination angle and a longitudinal inclination angle of the wheel set from the three-axis acceleration and the three-axis angular velocity using a Kalman filter formulated from a state space model configured by an observation model in which the error covariance matrix is ​​set; A track condition estimating method, wherein in the track condition calculating step, a turn error is calculated as the track condition information based on a horizontal tilt angle of the wheelset.

2. 2. The orbit state estimating method according to claim 1, wherein the translational acceleration components are extracted by band-pass filtering to remove frequency components in which track irregularity predominates from the lateral acceleration and the longitudinal acceleration among the three-axis accelerations.

3. 3. The orbit state estimation method according to claim 1, wherein the error covariance matrix is ​​set by adding values ​​obtained by multiplying the extracted translational acceleration components by predetermined coefficients to diagonal elements.

4. an inclination angle estimation unit that estimates a horizontal inclination angle and a longitudinal inclination angle of a wheel set based on a three-axis acceleration and a three-axis angular velocity measured by an inertial measurement device installed on an axle box of the vehicle; a track condition calculation unit that calculates track condition information representing a track condition based on the horizontal inclination angle and the longitudinal inclination angle of the wheel set; Equipped with The tilt angle estimation unit is extracting a translational acceleration component during vehicle travel from the three-axis acceleration measured by the inertial measurement unit through a filtering process; setting an error covariance matrix of an observation model of a Kalman filter based on the extracted translational acceleration component; estimating a horizontal inclination angle and a longitudinal inclination angle of the wheel set from the three-axis acceleration and the three-axis angular velocity using a Kalman filter formulated from a state space model configured by an observation model in which the error covariance matrix is ​​set; The track state estimation device, wherein the track state calculation unit calculates a turn error as the track state information based on a horizontal tilt angle of the wheelset.

5. An inertial measurement unit installed on an axle box of the vehicle; a tilt angle estimating unit that estimates a horizontal tilt angle and a longitudinal tilt angle of a wheel set based on the three-axis acceleration and the three-axis angular velocity measured by the inertial measurement unit; and a track condition estimating device having a track condition calculating unit that calculates track condition information representing a track condition based on the horizontal inclination angle and the longitudinal inclination angle of the wheelset; Equipped with The orbit state estimation device comprises: The tilt angle estimation unit extracting a translational acceleration component during vehicle travel from the three-axis acceleration measured by the inertial measurement unit through a filtering process; setting an error covariance matrix of an observation model of a Kalman filter based on the extracted translational acceleration component; estimating a horizontal inclination angle and a longitudinal inclination angle of the wheel set from the three-axis acceleration and the three-axis angular velocity using a Kalman filter formulated from a state space model configured by an observation model in which the error covariance matrix is ​​set; a track condition calculation unit calculating a turnability deviation as the track condition information based on a horizontal inclination angle of the wheelset.

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