JP2023083700A - Track condition estimation method, track condition estimation device, and vehicle - Google Patents

Abstract

To provide a track condition estimation method capable of easily inspecting a track using an operational vehicle.SOLUTION: A track condition estimation method includes an acceleration noise removal step, a tilt angle estimation step, and a track condition calculation step. The acceleration noise removal step eliminates the acceleration noise generated by the vehicle acceleration/deceleration from a three-dimensional acceleration measured by an inertia measuring device installed on a vehicle body or an axle box based on a vehicle travelling speed. The tilt angle estimation step estimates a horizontal tilt angle and a longitudinal tilt angle at the installation position of the inertia measuring device based on the three-dimensional acceleration with eliminated acceleration noise and the three-dimension acceleration measured by the inertia measuring device. The track condition calculation step calculates track condition information representing the track condition based on the estimated horizontal tilt angle and longitudinal tilt angle.SELECTED DRAWING: Figure 11

Description

The present invention relates to a track state estimation method, a track state estimation device, and a vehicle.

One of the causes of derailment of vehicles running on rails is rail irregularity (hereinafter also referred to as "track irregularity") with respect to the original position. Since track irregularity directly affects derailment, it is important to properly inspect and maintain track irregularity to reduce the risk of derailment.

Inspection of track irregularity (hereinafter also referred to as “track inspection”) is performed using, for example, a hand-push type track inspection device. However, if the rail that is the object of track inspection is long, it is difficult to perform inspections at high frequency. Also, track inspection by a hand-push type track inspection device is static measurement in a no-load state, which is different from the situation at the time of derailment. For this reason, it is desired to be able to frequently perform dynamic inspection while the vehicle is running.

As a method for frequently performing dynamic inspections while vehicles are running, it is conceivable to perform track inspections using running vehicles. As a track inspection device using a vehicle, for example, in Patent Document 1, a detector unit composed of various detectors is attached to the bogie frame of the vehicle, and five items of track displacement are measured based on the inertial versine method while the vehicle is running. Techniques for measuring are disclosed.

Japanese Patent No. 3411861 Japanese Patent No. 6674544

However, in the technique described in Patent Document 1, although five items of track displacement can be measured by the detector unit, the detection mechanism by the detector unit is complicated and expensive. In addition, since the displacement is calculated by integrating the acceleration signal twice, when the vehicle travels at a low speed, such as a vehicle used to transport heavy materials in a steelworks, However, the accumulation of integration errors greatly reduces the measurement accuracy.

Therefore, the present invention has been made in view of the above problems, and an object of the present invention is to provide a track condition estimation method and a track condition estimation method that enable easy track inspection using an operating vehicle. An object of the present invention is to provide an estimation device and a vehicle.

In order to solve the above problems, according to one aspect of the present invention, the acceleration of the vehicle is calculated from the three-axis acceleration measured by the inertial measurement device installed in the vehicle body or axle box based on the traveling speed of the vehicle. an acceleration noise removal step of removing acceleration noise generated by deceleration; and a horizontal tilt angle and a horizontal tilt angle at the installation position of the inertial measurement device based on the three-axis acceleration from which the acceleration noise has been removed and the three-axis angular velocity measured by the inertial measurement device. A track state estimation method, comprising: a tilt angle estimation step of estimating a longitudinal tilt angle; and a track state calculation step of calculating track state information representing a track state based on the estimated horizontal tilt angle and longitudinal tilt angle. provided.

In the tilt angle estimation step, the translational acceleration component during vehicle running is extracted by filtering from the three-axis acceleration from which the acceleration noise has been removed, and based on the extracted translational acceleration component, the error covariance matrix of the observation model of the Kalman filter is calculated. , and using a Kalman filter formulated from a state space model composed of an observation model with an error covariance matrix, the acceleration noise is removed from the three-axis acceleration and three-axis angular velocity to install the inertial measurement device. A horizontal inclination angle and a longitudinal inclination angle at the position may be estimated, and in the track state calculation step, an elevation displacement may be calculated as the track state information based on the estimated longitudinal inclination angle.

In the track state calculation step, the calculated elevation displacement may be subjected to high-pass filter processing for removing estimation errors of low-frequency components.

The translational acceleration component may be extracted by band-pass filter processing that removes the dominant frequency component of the track displacement from the lateral acceleration and the longitudinal acceleration of the three-axis acceleration.

The error covariance matrix may be set by adding a value obtained by multiplying the extracted translational acceleration component by a predetermined coefficient to the diagonal component.

In the acceleration noise removal step, the acceleration noise included in the three-axis acceleration is removed by subtracting the acceleration component calculated from the running speed of the vehicle from the longitudinal acceleration component of the three-axis acceleration measured by the inertial measurement device. good.

In order to solve the above problems, according to another aspect of the present invention, based on the running speed of the vehicle, from the three-axis acceleration measured by the inertial measurement device installed in the vehicle body or axle box, Based on the acceleration noise removal processing unit that removes the acceleration noise generated by the acceleration and deceleration of the vehicle, and the three-axis acceleration from which the acceleration noise has been removed and the three-axis angular velocity measured by the inertial measurement device, at the installation position of the inertial measurement device A track, comprising: an inclination angle estimating unit that estimates a horizontal inclination angle and a longitudinal inclination angle; and a track condition calculation unit that calculates track condition information representing a track condition based on the estimated horizontal inclination angle and longitudinal inclination angle. A state estimator is provided.

Furthermore, in order to solve the above problems, according to another aspect of the present invention, there are provided a speed measuring device for measuring the running speed of a vehicle, an inertial measuring device installed on the vehicle body or axle box, and a running speed of the vehicle. Acceleration noise removal unit that removes acceleration noise generated by acceleration and deceleration of the vehicle from the 3-axis acceleration measured by the inertial measurement device based on the velocity, and measurement by the 3-axis acceleration and inertial measurement device from which the acceleration noise is removed. an inclination angle estimating unit that estimates the horizontal inclination angle and the longitudinal inclination angle at the installation position of the inertial measurement device based on the three-axis angular velocities obtained; and a track state estimation device having a track state calculation unit that calculates track state information.

As described above, according to the present invention, it is possible to easily perform track inspection using an operating vehicle.

1 is a schematic diagram showing a schematic configuration of a vehicle according to one embodiment of the present invention; FIG. It is a schematic diagram for explaining elevation displacement. FIG. 5 is a schematic diagram showing elevation displacement calculated when the inertial measurement device is installed on the vehicle body; FIG. 5 is a schematic diagram showing elevation displacement calculated when the inertial measurement device is installed in the axle box; FIG. 4 is an explanatory diagram for explaining measured values by an inertial measurement device; FIG. 4 is a schematic cross-sectional view of a wheelset for explaining measured values by an inertial measurement device; FIG. 5 is a graph for explaining processing for removing acceleration noise of longitudinal acceleration; FIG. 4 is a graph showing a calculation example of an index _Rx representing translational acceleration; 4 is a graph showing a calculation example of an index _Ry representing translational acceleration; It is a functional block diagram which shows the structure of the track state estimation apparatus which concerns on the same embodiment. It is a flowchart which shows an example of the track|orbit state estimation method which concerns on the same embodiment. It is a graph which shows an example of the elevation change before and after application of a high-pass filter. It is a graph which shows the estimation result of the elevation displacement in an Example.

Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings. In the present specification and drawings, constituent elements having substantially the same functional configuration are denoted by the same reference numerals, thereby omitting redundant description.

[1. Vehicle configuration]
First, based on FIG. 1, a schematic configuration of a vehicle 1 according to one embodiment of the present invention will be described. FIG. 1 is a schematic diagram showing a schematic configuration of a vehicle 1 according to this embodiment.

A vehicle 1 according to the present embodiment is configured to be able to travel on a pair of rails 5 extending on sleepers (reference numeral 7 in FIG. 2). As shown in FIG. 1, the vehicle 1 is composed of a vehicle body 10 which is the main structural portion of the vehicle, and a bogie 20 which supports the vehicle body 10 and has a running mechanism. In the following description, the direction of vehicle movement (front-back direction) is the X direction, the width direction (left-right direction) is the Y direction, and the height direction (up-down direction) is the Z direction.

Four wheel pairs are arranged in the traveling direction of the vehicle on the truck portion 20 of the vehicle 1 shown in FIG. Each wheel pair is formed by connecting two wheels 23 that form a pair in the width direction with a wheelset 25 . Both ends of each wheel set 25 are provided with axle boxes 27 which are bearing portions of the wheel set 25 . The axle box 27 is connected to the bogie frame 21 by a coil spring 29 . 1 shows the axle box 27 and the coil spring 29 only for one wheel 23 in order to show the wheelset 25, but all the wheels 23 are provided with the axle box 27 and the coil spring 29.

The vehicle 1 according to this embodiment includes an inertial measurement unit (IMU) 30 in at least one of the vehicle body 10 and the axle boxes 27 of the wheels 23 . In the vehicle 1 shown in FIG. 1, one inertial measurement device 30 is provided in each of the vehicle body 10 and the axle box 27 of one wheel 23, but only one of them may be installed. The inertial measurement device 30 is a device that detects three-dimensional inertial motion (translational motion and rotational motion in orthogonal three-axis directions). The inertial measurement device 30 detects translational motion with an acceleration sensor and detects rotational motion with an angular velocity sensor. The measured values obtained by the inertial measurement device 30 are output to a track state estimation device (track state estimation device 100 in FIG. 10) that estimates the track state of the rail 5 on which the vehicle 1 runs.

Further, the vehicle 1 according to the present embodiment includes a position detection device (not shown) for detecting the absolute position of the vehicle 1 on the rail 5 and a speed measurement device for measuring the running speed of the vehicle 1 (speed measurement device shown in FIG. 10). device 40). 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, with an RFID reader, an RFID tag previously set at the middle position of the pair of rails 5 in the width direction. Note that the method of detecting the absolute position of the vehicle 1 is not limited to this example, and a known technique may be used. For example, as the absolute position of the vehicle 1, position information obtained using radio waves from artificial satellites may be used. The speed measuring device may be, for example, a laser Doppler speedometer installed on the vehicle body 10 , and the relative speed from the ground surface calculated from the laser Doppler speedometer may be used as the traveling speed of the vehicle 1 .

[2. Estimation of orbital state]
Rail track conditions include flatness displacement, alignment displacement, elevation displacement, level displacement, and gauge displacement. A case of estimating an elevation displacement that may indirectly cause a derailment as a track state will be described below. As shown in FIG. 2, the vertical displacement refers to the difference in height of the top surface of the rail 5 in the vehicle traveling direction (front-rear direction). The vertical displacement h is expressed by the vertical distance between a line connecting two points on the top surface of the rail 5 that are separated by a predetermined distance d in the direction of travel of the vehicle and the top surface of the rail 5 at an intermediate position between the two points. .

Elevation displacement is usually defined by a method called the 10m string versine method, which is obtained by stringing two points on a rail 10m apart and measuring the distance from the rail at the midpoint. On the other hand, although the measurement characteristics are different from those of the 10m-string Masaya method, the height difference between the front and rear fulcrums can also be considered as a type of elevation displacement that indicates irregularities in the height direction of the rail. Therefore, in the present embodiment, the height difference in the length of the reference length L according to the vehicle body structure is calculated as the height displacement h. Assuming that the front-rear tilt angle is φ and the distance between the fulcrums is L, the elevation displacement h can be expressed by the following formula (1).

h=L×tanφ (1)

In this embodiment, the elevation displacement is measured using only the measurement values of the inertial measurement device 30 installed on the vehicle body portion 10 or the axle box 27 . As shown in FIG. 3, when the inertial measurement device 30 is installed in the vehicle body 10, the inertial measurement device 30 measures the longitudinal inclination angle φ of the vehicle body 10, and the elevation of the front and rear bogies 20 is calculated as the elevation displacement. Calculate the difference. At this time, the inter-fulcrum distance L is the distance between the front and rear truck portions 20 of the vehicle body portion 10 . Further, when the inertial measurement device 30 is installed on the truck portion 20 (axle box 27) as shown in FIG. A height difference between the front and rear wheelsets 25 is calculated. At this time, the inter-fulcrum distance L is the distance between the front and rear wheelsets 25 of the truck portion 20 .

Error factors in estimating the inclination angle include noise caused by shocking vibrations accompanying vehicle travel, acceleration/deceleration noise of the vehicle, and the like. In particular, when the inertial measurement device 30 is installed in the axle box 27, it is possible to measure the track displacement directly under the wheels, while facilitating the measurement of the above-described noise. Therefore, in the present embodiment, a method of reducing the influence of these noises and estimating the elevation displacement, which is one index representing the track state of the rail, is presented.

As a method of estimating the elevation displacement, for example, Patent Document 2 discloses an inspection device for measuring a track irregularity such as an elevation irregularity of a track on which a vehicle travels. In such an inspection device, when a beam member having a predetermined length is moved on a rail by two rollers, the product of the angular velocity acting on the beam member and the two rollers determines the midpoint of the two contact points. Calculates the height deviation in However, in Patent Literature 2, since a device different from that of an actual vehicle traveling on a rail is used, it is still not possible to easily measure the track condition of the rail. Moreover, the inspection device of Patent Document 2 measures the response of a beam member that is a rigid body, and it is considered that the response is different from that of a vehicle having an elastic body such as a spring.

Details of a track state estimation method for estimating elevation displacement as a track state, a track state estimation device for executing track state estimation processing based on the track state estimation method, and a track state estimation method will be described below.

[2-1. Track state estimation method]
First, a track state estimation method for estimating a track state according to the present embodiment will be described with reference to FIGS. 5 to 9. FIG. FIG. 5 is an explanatory diagram for explaining measured values by the inertial measurement device 30. As shown in FIG. FIG. 6 is a schematic cross-sectional view of the wheelset for explaining the measured values by the inertial measurement device 30. As shown in FIG. FIG. 7 is a graph for explaining the process of removing the acceleration noise of the longitudinal acceleration. The lower graph shows longitudinal acceleration after correction from which acceleration noise has been removed. FIG. 8 is a graph showing a calculation example of the index _Rx representing the translational acceleration. FIG. 9 is a graph showing a calculation example of the index _Ry representing the translational acceleration.

In the following description, the measured values of the three-axis acceleration at the time k measured by the inertial measurement device 30 are a _xk , a _yk , and a _zk , and the measured values of the three-axis angular velocity are ω _xk , ω _yk , and ω _zk . . Also, as shown in FIG. 5, let θ _k be the roll angle, φ _k be the pitch angle, and ψ _k be the yaw angle at time k.

In this embodiment, a Kalman filter is used to estimate the roll angle θx of the wheelset 25 from the measurement value of the inertial measurement device 30 . The Kalman filter is one of the data assimilation methods, and is a method of successively estimating state quantities that minimize errors with observed quantities using Bayesian estimation. A Kalman filter can be formulated from a time update formula based on integration of the roll angle velocity and the pitch angle velocity in the absolute coordinates, with the roll angle estimate value and the pitch angle estimate value obtained from the measured values of the three-axis acceleration as the observed quantities.

The state vector _xk is defined by the following equation (2).

_Further , the observation vector _{yk is} obtained by the following _formula ( 3) can be expressed as follows. In the observation vector _yk , θ _0k is the estimated value of the roll angle at time k, and φ _0k is the estimated value of the pitch angle at time k.

Here, acceleration noise generated by acceleration/deceleration of the vehicle 1 is included in the measured values a _xk , a _yk , and a _zk of the three-axis acceleration. In this embodiment, first, acceleration/deceleration noise is removed from the longitudinal acceleration a xk among the three-axis acceleration measurement values a _xk , a _yk , and a _zk based on the running speed of the vehicle 1 _. In actual vehicle running, the vehicle 1 runs at a constant speed while repeating acceleration and deceleration, and the effects of acceleration and deceleration in the longitudinal direction can be clearly observed from the longitudinal acceleration waveform. Here, since the vehicle speed is measured at a predetermined cycle (for example, once every 0.1 seconds), the longitudinal acceleration waveform has a step shape. Therefore, the acceleration/deceleration noise cannot be removed by filtering the specific frequency.

Therefore, in this embodiment, the acceleration component _{aVi due to the acceleration and deceleration of the vehicle 1 is calculated from the running speed Vi of} the vehicle 1 at the time i measured by the speed measuring device, using the following equation (4).

Here, for waveform smoothing, a low-pass filter may be applied to the traveling speed V of the vehicle 1 measured by the speed measuring device. The cutoff frequency of the low-pass filter may be V _ave /2 (V _ave is the average velocity [m/s]), for example. Then, the longitudinal acceleration _axIMU measured by the inertial measurement device 30 is corrected by the following formula (5) from the acceleration component _aV calculated based on the above formula (4), and the corrected longitudinal acceleration _ax ^* Ask for _{an IMU} .

The upper graph in FIG. 7 shows the longitudinal acceleration _axIMU measured by the inertial measurement device 30 and the acceleration component _aV calculated based on the above equation (4) from the vehicle speed measured by the speed measurement device at this time. ing. If the acceleration component _aV is subtracted from the longitudinal acceleration _axIMU measured by the inertial measurement unit 30 based on the above equation (5), the lower graph in FIG. A _x ^* _IMU can be obtained. In the example of FIG. 7, acceleration noise is generated due to the acceleration of the vehicle 1 near 120 m, and it can be seen from the lower graph in FIG. 7 that the influence thereof has been removed.

In the subsequent processing, among the measured values a _xk , a _yk , _and a _zk ^of the three-axis acceleration, the longitudinal acceleration _{a xk} after correction from which the acceleration noise has been removed is used to determine the track state. can improve the estimation accuracy of In the following description, the measured value of the longitudinal acceleration of the three-axis acceleration is also referred to as a _xk , but in practice, the longitudinal acceleration a _x ^* _IMU after correction by the above equation (5) is used.

The gravity acceleration projection components of the three-axis acceleration measurement values a _xk , a _yk , and a _zk are acceleration low-frequency components, for example, 0 to 1 Hz. For example, a low-pass filter can be used to extract the projected gravitational acceleration component from the three-axis acceleration measurements a _xk , a _yk , and a _zk .

The time update formula is derived from angle integration in the absolute coordinate system and is a nonlinear relational formula for the state vector. Therefore, in this embodiment, an Extended Kalman Filter that performs linearization around the state vector x _k−1 is used. At this time, the system equation is represented by the following formula (6). _vk is the process noise.

Also, when observation noise w _k and observation matrix H _k are assumed, the observation equation is expressed by the following equation (8).

Using the Kalman filter based on the state space model represented by the above equations (6) to (8), the measured values a _xk , a _yk , and a _zk of the three-axis acceleration and the measured values ω _xk , ω _yk , and ω of the three-axis angular velocity The roll angle θ _k and pitch angle φ _k can be estimated from _zk .

Here, in the present embodiment, in order to reduce the error effect due to the translational acceleration component during vehicle running included in the measurement values of the inertial measurement device 30, the measured values a _xk , A translational acceleration component may be extracted from _{ayk and azk} by filtering, and an observation error covariance matrix of the Kalman filter may be set based on the extracted translational acceleration component.

The impact response that occurs when the wheel flange contacts the rail in a sharp curve portion becomes an error factor in the angle estimation value. In the above formula (3) for calculating the angle from the integral of the angular velocity and the above formula (8) for calculating the angle from the acceleration low frequency component (that is, the projection component of the gravitational acceleration), the process noise v _k and the observation noise w _k are Gaussian noise with zero mean is assumed. This noise setting represents the error component of each relationship. Process noise _vk means measurement error and integration error of angular velocity data. The observation noise _wk means the measurement error of the acceleration data and the estimation error of the gravity acceleration projection component extracted from the acceleration.

When the wheel flange contacts the rail on a sharp curve, both the measured angular velocity and the measured acceleration may show a shocking response, but the shocking response is dominant in all frequency bands. There is a feature. Here, the above equation (3) is established regardless of the presence or absence of the impact response, but the observation vector _yk in the above equation (8) is an angle calculated from the gravity acceleration projection component, When acceleration acts, it becomes an error factor. That is, in such a travel section, by setting the observation noise w _k to a large value, the likelihood of the above equation (8) is reduced (in other words, the likelihood of the above equation (6) is increased). to properly represent the actual response.

Therefore, the observation noise _wk is set according to the magnitude of the translational acceleration acting on the inertial measurement device 30 to improve the accuracy of the angle estimation value. Specifically, indexes R _x and R _y representing the translational acceleration are introduced, and the covariance matrix R of the observation noise _wk corresponding to the magnitude of the translational acceleration component is set sequentially.

The calculation accuracy of the roll angle θ _0k and the pitch angle φ _0k of the observation vector _yk is governed by the lateral acceleration _ayk and the longitudinal acceleration _axk , respectively, and errors can occur in either case due to the translational acceleration. For the left-right acceleration _ayk , the impact response when passing through a sharp curve appears as translational acceleration, and for the longitudinal acceleration _axk , the acceleration/deceleration for keeping the vehicle speed constant appears as translational acceleration. Therefore, indices R _x and R _y representing the respective translational accelerations are defined.

The index R _x is a value obtained by extracting only the high frequency component from the longitudinal acceleration measurement value a _x using a band-pass filter, squaring the extracted value, and taking the moving average of a predetermined interval (the number of moving average samples). The index R _y is a value obtained by extracting only the high frequency component from the lateral acceleration measurement value a _y with a bandpass filter, squaring the extracted value, and taking the moving average of a predetermined interval (the number of moving average samples). Since the indexes R _x and R _y are indexes for correcting the low-frequency response (that is, the tilt angle estimated from the gravitational acceleration projection component), they are squared and moving averaged to obtain values excluding high frequencies. to stabilize the estimate. 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.

It can be assumed that the shock response or the response due to acceleration/deceleration is a forced response acting from the outside and is dominant in all frequency components. On the other hand, it is considered that the angle change component is dominant in the low frequency band of less than 1 Hz, and the measurement noise is dominant in the high frequency band of more than 7 Hz, for example. Therefore, a bandpass filter is used to remove the frequency components in these bands.

Specifically, assuming that the vehicle speed is almost constant, the average speed of the vehicle in the measured data to be filtered is V _ave [m/s], and the maximum spatial frequency of the track displacement is F _space [cycle/m]. Then, the cutoff frequency F _time [Hz] of the time history waveform can be expressed by the following equation (9).

_Ftime = _Fspace x _Vave (9)

The high-frequency cutoff frequency F _{time_h} can be expressed as an empirical formula having a general form such as the following formula (10).

F _{time — h} = 7 × F _space × V _ave (10)

The moving average sample number s of the indices R _x and R _y can be expressed by the following formula (11) as an empirical formula from the sampling frequency fs [Hz] of the measured values.

s=fs/(2× _Ftime ) (11)

For example, when the sampling frequency fs=500 Hz and the cutoff frequency F _time =1 Hz, the number of moving average samples s is 250 sample times.

FIG. 8 shows an example of the index _Rx representing the translational acceleration, and FIG. 9 shows an example of the index _Ry representing the translational acceleration. In FIG. 8, the position where the value of the index _Rx indicated by the dashed frame is large indicates that the vehicle is affected by acceleration/deceleration to keep the vehicle speed constant. Also, in FIG. 9, the position indicated by the dashed frame with the large value of the index _Ry is affected by the impact at the time of passing through the sharp curve.

Next, the calculated indices R _x and R _y are used to successively set the observation noise w _k according to the magnitude of the translational acceleration component. 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 component. Specifically, the variance-covariance matrix R(k) of the observation noise w _k at time k is represented by indices R _y (k), R x (k), R _y (k), R _y (k), Using the coefficients K _y and K _x for R _x (k), it is set as shown in Equation (12) below.

Also, the process noise Q may be constant regardless of time, and may be set, for example, by the following equation (13).

Note that the coefficients K _y and K _x are values for setting the noise variance of the observation equation represented by the above formula (8) from the indices R _y (k) and R _x (k), and are theoretically derived you can't. For this reason, the coefficients K _y and K _x are set to optimum values that maximize the estimation accuracy of the elevation displacement. The process noise Q is also appropriately set based on the value that maximizes the estimation accuracy of the elevation displacement.

Thus, in the present embodiment, the variance-covariance matrix R(k) of the observation noise w _k at time k is set as in the above equation (12), and the observation noise of the observation equation shown in the above equation (8) _wk is sequentially set according to the magnitude of the translational acceleration component. As a result, it is possible to reduce the influence of errors due to translational acceleration components during vehicle travel, which are included in the measurement values of the inertial measurement device 30 . As a result, the observation vector y _k at time k, that is, the estimated value θ _0k of the roll angle and the estimated value φ _0k of the pitch angle can be obtained with high accuracy. A Kalman filter based on a state space model represented by equations (6) to (8) and in which the variance-covariance matrix R (k) of the observation noise w _k is set by equation (12), roll angle θ _k and pitch angle Once φ _k is estimated, the elevation displacement h can be calculated using equation (1) above.

[2-2. Track state estimation device]
Based on FIG. 10, the configuration of the track state estimation device 100 for estimating the track state based on the above-described track state estimation method will be described. FIG. 10 is a functional block diagram showing the configuration of the track state estimation device 100 according to this embodiment. The track state estimation device 100 includes an acceleration noise removal section 110, an inclination angle estimation section 120, and a track state calculation section 130, as shown in FIG.

Acceleration noise removal unit 110 removes longitudinal acceleration a _xk (=a _xIMU ) among three-axis accelerations a _xk , a _yk , and a _zk measured by inertial measurement device 30 based on the traveling speed of vehicle 1 . Remove deceleration noise. When the acceleration noise removal unit 110 acquires the traveling speed V from the speed measuring device 40 installed in the vehicle 1, the acceleration component _aV due to acceleration/deceleration of the vehicle 1 is calculated based on the above equation (4). Then, the longitudinal acceleration _axk , which is the measured value of the inertial measurement device 30, is corrected based on the above equation (5). Acceleration noise removal section 110 outputs corrected longitudinal acceleration a _xk (=a _x ^* _IMU ) to inclination angle estimation section 120 .

The tilt angle estimator 120 calculates the horizontal position of the vehicle body 10 or the wheelsets 25 based on the three-axis accelerations a _xk , a _yk , and a _zk and the three-axis angular velocities ω _xk , ω _yk , and ω _zk measured by the inertial measurement device 30 . Estimate the tilt angle (roll angle θ _k ) and the longitudinal tilt angle (pitch angle φ _k ). Note that the corrected longitudinal acceleration a _xk (= _ax ^* _IMU ) corrected by the acceleration noise removing unit 110 is used as the longitudinal acceleration a _xk of the three-axis acceleration.

First, the tilt angle estimating unit 120 extracts the projected gravitational acceleration component and the translational acceleration component from the three-axis acceleration measurement values a _xk , a _yk , and a _zk . Further, the tilt angle estimating unit 120 calculates indexes R _x and R _y representing the translational acceleration from the translational acceleration components extracted from the measured values a _xk , a _yk , and _azk of the three-axis acceleration, and ) to set the variance-covariance matrix R(k) of the observed noise w _k at time k. Then, the tilt angle estimating unit 120 uses the set variance-covariance matrix R(k) in the above equation (8) to calculate the observation vector y _k (roll angle estimated value θ _{0 k} and pitch angle estimated value φ _{0 k} ) is calculated. Furthermore, the tilt angle estimating unit 120 is represented by equations (6) to (8), and the Kalman filter based on the state space model in which the variance-covariance matrix R(k) of the observation noise w _k is set by equation (12) to calculate the roll angle θ _k and the pitch angle φ _k . The inclination angle estimator 120 outputs the calculated roll angle θ _k and pitch angle φ _k to the track state calculator 130 .

The track condition calculator 130 calculates track condition information representing the track condition based on the horizontal inclination angle (roll angle θ _k ) and the longitudinal inclination angle (pitch angle φ _k ) of the wheelset 25 . In this embodiment, an elevation displacement is calculated as the track state information. When obtaining the elevation displacement as the track condition information, the track condition calculator 130 calculates the elevation displacement h using the above equation (1) based on the pitch angle φ _k calculated by the inclination angle estimator 120 .

Here, an estimation error of a low frequency component occurs in the calculated elevation displacement h. Such an estimation error is considered to occur because the horizontal tilt angle and the forward/backward tilt angle calculated by the Kalman filter partially include an integral error of the angular velocity. Therefore, a high-pass filter may be applied to the calculated elevation displacement h. The cutoff frequency of the high-pass filter may be determined according to the wavelength of the elevation change h to be acquired, and may be V _ave /30 (V _ave is the average velocity [m/s]), for example. By applying the high-pass filter, it is possible to suppress deviations in the estimated height displacement h every time the inertial measurement device 30 measures the three-axis acceleration and the three-axis angular velocity, and it is possible to obtain reproducible estimated values. can be obtained.

The track state calculator 130 outputs the calculated elevation displacement h to the output device 200, for example. The output device 200 may be, for example, a display device such as a display.

The track state estimation device 100 according to the present embodiment is composed of various processors such as a CPU (Central Processing Unit) and a DSP (Digital Signal Processor), and the above functions are performed by the processor being operated according to a predetermined program. can be realized.

The track state estimation device 100 according to this embodiment may be installed in the vehicle 1 or may be installed in a separate location. Measured values a _xk , a _yk , and a _zk of the three-axis acceleration and the measured values ω _xk , ω _yk , and ω _zk of the three-axis angular velocity by the inertial measurement device 30 , and the running speed V of the vehicle 1 measured by the speed measurement device 40 . is 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 stores the measured values a _yk , a _zk of the three-axis acceleration, the measured values ω _xk , ω _yk , and ω _zk of the three-axis angular velocity, the traveling speed V, and the absolute value of the vehicle 1 recorded in the storage device. By obtaining the position, the orbital state can be estimated.

[2-3. Track state estimation method]
A track state estimation method performed by the track state estimation device 100 according to the present embodiment will be described with reference to FIG. 11 . FIG. 11 is a flow chart showing an example of a track state estimation method according to this embodiment.

(S10, S20: Acquisition of 3-axis acceleration and 3-axis angular velocity)
First, the inertial measurement device 30 installed in the vehicle body 10 or the axle box 27 of the vehicle 1 measures three-axis accelerations a _xk , a _yk , and _azk and three-axis angular velocities ω _xk , ω _yk , and ω _zk ( S10, S20). The measured values a _xk , a _yk , a _zk , ω _xk , ω _yk , and ω _zk of the three-axis acceleration and three-axis angular velocity measured by the inertial measurement device 30 are obtained by the acceleration noise removal unit 110 of the track state estimation device 100 and the tilt It is output to angle estimator 120 .

(S30: Acquisition of traveling speed of vehicle)
Further, the traveling speed V is measured by the speed measuring device 40 installed in the vehicle 1 (S30). The traveling speed V of the vehicle 1 measured by the speed measuring device 40 is output to the acceleration noise removing section 110 of the track state estimating device 100 .

(S40: acceleration noise removal)
Next, the acceleration _noise removal unit 110 determines _{the longitudinal acceleration a xk ( =} a _xIMU ), acceleration/deceleration noise is removed (S40). The acceleration noise removal unit 110 calculates the acceleration component _aV due to acceleration/deceleration of the vehicle 1 from the running speed V of the vehicle 1 based on the above equation (4). Then, the acceleration noise removal unit 110 corrects the longitudinal acceleration _axk , which is the measured value of the inertial measurement device 30, based on the above equation (5). Acceleration noise removal section 110 outputs corrected longitudinal acceleration a _xk (=a _x ^* _IMU ) to inclination angle estimation section 120 .

(S50: Extraction of gravity acceleration projection component)
Next, the tilt angle estimator 120 extracts the gravity acceleration projection component from the three-axis acceleration measurement values a _xk , a _yk , and a _zk (S50). For the longitudinal acceleration a _xk of the three-axis acceleration, the corrected longitudinal acceleration a _xk (= _ax ^* _IMU ) corrected by the acceleration noise removing unit 110 in step S40 is used. The gravity acceleration projection components of the three-axis acceleration measurement values a _xk , a _yk , and a _zk are acceleration low-frequency components, for example, 0 to 1 Hz. The tilt angle estimator 120 can extract the gravity acceleration projection component from the three-axis acceleration measurement values a _xk , a _yk , and a _zk by using a low-pass filter, for example.

(S60: Extraction of translational acceleration component)
The tilt angle estimator 120 also extracts translational acceleration components from the measured values a _xk , a _yk , and a _zk of the three-axis acceleration (S60). The translational acceleration components of the measured values a _xk , a _yk , and a _zk of the three-axis acceleration are acceleration high-frequency components, for example, components of 1 to 7 Hz. The tilt angle estimating unit 120 can extract translational acceleration components from the measured values a _xk , a _yk , and a _zk of the three-axis acceleration using, for example, a bandpass filter. The tilt angle estimator 120 extracts the translational acceleration component and calculates indexes R _x and R _y representing the translational acceleration.

(S70: Estimation of tilt angle)
Next, the tilt angle estimator 120 estimates the tilt angle of the vehicle body 10 or the wheelset 25 (S70). The tilt angle can be calculated using a Kalman filter based on the state space model expressed by Equations (6)-(8) above. Here, the tilt angle estimating unit 120 sets the variance-covariance matrix R(k) of the observation noise w _k at the time k from the above equation (12) using the indices R _x and R _y calculated in step S60. do. As a result, the observation noise _wk of the observation equation shown in Equation (8) is sequentially set according to the magnitude of the translational acceleration component. As a result, the effect of error due to the translational acceleration component when the vehicle is running, which is included in the measured value, is reduced, and the observation vector y _k (estimated value θ _0k of roll angle and estimated value φ _0k of pitch angle) can be obtained with high accuracy. can.

The tilt angle estimating unit 120 is represented by equations (6) to (8), and is based on a state space model in which the variance-covariance matrix R(k) of the observation noise w _k is set by equation (12). Calculate the roll angle θ _k and the pitch angle φ _k . The inclination angle estimator 120 then outputs the calculated roll angle θ _k and pitch angle φ _k of the vehicle body 10 or wheelset 25 to the track condition calculator 130 .

(S80: Estimation of track state information)
After that, the track condition calculation unit 130 calculates track condition information representing the track condition based on the horizontal inclination angle (roll angle θ _k ) and the longitudinal inclination angle (pitch angle φ _k ) of the vehicle body 10 or the wheelset 25 ( S80). In this embodiment, an elevation displacement is calculated as the track state information. When obtaining the elevation displacement as the track condition information, the track condition calculation _unit 130 calculates the elevation displacement h Calculate

Here, a high-pass filter may be applied to the elevation displacement h calculated in step S80. As described above, the calculated elevation displacement h has an estimation error of the low frequency component. By applying the high-pass filter, it is possible to suppress deviations in the estimated height displacement h every time the inertial measurement device 30 measures the three-axis acceleration and the three-axis angular velocity, and it is possible to obtain reproducible estimated values. can be obtained. The cutoff frequency of the high-pass filter may be determined according to the wavelength of the elevation change h to be acquired, and may be V _ave /30 (V _ave is the average velocity [m/s]), for example.

FIG. 12 shows an example of values of the elevation displacement h calculated in step S80 before and after application of the high-pass filtering process. FIG. 12 shows true values of elevation displacement measured by the 10 m-chord versine method measured by a hand-push type track inspection device. Here, V _ave /30 is set as the cutoff frequency of the high-pass filter. As shown in FIG. 12, by applying the high-pass filtering process, it is possible to reduce the deviation of the calculated elevation displacement h from the true value. The track state calculator 130 outputs the calculated elevation displacement h to the output device 200 .

[3. summary]
The vehicle according to the embodiment of the present invention and the track state estimation device and track state estimation method for estimating the elevation displacement as the track state have been described above. According to this embodiment, an inertial measurement device is installed in the vehicle body or axle box of a vehicle, and the horizontal tilt at the installation position of the inertial measurement device is determined based on the three-axis acceleration and the three-axis angular velocity measured by the inertial measurement device. Estimate angle and fore/aft tilt angle. The horizontal tilt angle and the forward-backward tilt angle can be estimated by a Kalman filter. At this time, after removing acceleration noise from the longitudinal acceleration of the three-axis acceleration, the horizontal tilt angle and the longitudinal tilt angle are estimated. In addition, in order to reduce the effect of errors due to the translational acceleration components when the vehicle is running, which are included in the measured values of the inertial measurement device, the translational acceleration components that are the error factors are extracted from the measured values of the 3-axis acceleration when the vehicle is running by filtering. , sequentially set the observation error covariance matrix of the Kalman filter based on the extracted translational acceleration components. As a result, the horizontal tilt angle and the longitudinal tilt angle of the wheelset can be accurately estimated. By estimating the horizontal inclination angle and the longitudinal inclination angle of the wheelset with high accuracy, the estimation accuracy of the elevation displacement can be improved. Further, since the three-axis acceleration and the three-axis angular velocity can be measured by running the vehicle, the measurement can be easily performed.

In order to verify the effectiveness of the track state estimation method according to the above-described embodiment, estimated values of elevation displacement by the track state estimation method were evaluated. The vehicle used for verification had the configuration shown in FIG. 1. As shown in FIG. 3, an inertial measurement device was provided in the vicinity of the joint between the vehicle body and the bogie. The distance between fulcrums (reference length) L was 6.3 m.

In this verification, the true value was the elevation displacement measured by the 10m-string Masaya method measured by a hand-push type track inspection device. Further, the elevation displacement was estimated using the track state estimation method according to the above embodiment. At this time, the coefficients K _y and K _x were both set to 1.0×10 ⁻⁶ and the variance-covariance matrix R of the above equation (12) was set. The process noise Q is given by the above equation (13). Elevation displacement can be estimated by using the measured value of the inertial measurement device as it is (that is, using the longitudinal acceleration before correction) and using the corrected longitudinal acceleration after removing the acceleration noise from the longitudinal acceleration. followed.

FIG. 13 shows the elevation displacement estimated by using the track state estimation method according to the above embodiment and the elevation displacement by the 10 m-chord versine method. In addition, in FIG. 13, the elevation displacement by the 10 m string versine method shows the result of two paired rails. As shown in FIG. 13, the elevation displacement estimated using the track state estimation method according to the above embodiment approximately follows the true value. In addition, when the longitudinal acceleration after correction is used in estimating elevation displacement, the deviation from the true value is smaller than when the longitudinal acceleration before correction is used, and the error is reduced by a maximum of 5 to 10 mm. . Thus, by applying the track state estimation method according to the above-described embodiment, the effect of errors due to translational acceleration, including the effect of impact response during vehicle travel, can be reduced, and the effect of acceleration noise due to vehicle acceleration/deceleration can be reduced. was reduced, and it was shown that the accuracy of estimating elevation displacement can be improved.

Although the preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings, the present invention is not limited to such examples. It is obvious that a person having ordinary knowledge in the technical field to which the present invention belongs can conceive of various modifications or modifications within the scope of the technical idea described in the claims. It is understood that these also naturally belong to the technical scope of the present invention.

For example, in the above-described embodiment, the acceleration low-frequency component, which is the gravity acceleration projection component of the three-axis acceleration measurement values a _xk , a _yk , and a _zk , is a component of 0 to 1 Hz, and the three-axis acceleration measurement values a _xk , a Although the acceleration high-frequency component, which is the translational acceleration component of _{yk and azk} , is a component of 1 to 7 Hz, the present invention is not limited to such an example. The frequency bands of the acceleration low-frequency component and the acceleration high-frequency component are set assuming, for example, low-speed vehicles used for transporting heavy materials in steelworks. The frequency bands of the acceleration low-frequency component and the acceleration high-frequency component may be appropriately set according to the traveling speed of the vehicle, for example, based on the above equations (9) and (10). The moving average sample number s of the indexes R _x and R _y may also be set based on the above equation (11).

1 Vehicle 5 Rail 10 Car Body Part 20 Bogie Part 21 Bogie Frame 23 Wheel 25 Wheelset 27 Axle Box 29 Coil Spring 30 Inertia Measuring Device 40 Speed Measuring Device 100 Track State Estimating Device 110 Acceleration Noise Removal Part 120 Inclination Estimating Part 130 Track State Calculation Part 200 output device

Claims (8)

an acceleration noise removal step of removing acceleration noise generated by acceleration and deceleration of the vehicle from three-axis acceleration measured by an inertial measurement device installed in the vehicle body or axle box based on the running speed of the vehicle;
a tilt angle estimating step of estimating a horizontal tilt angle and a forward/backward tilt angle at an installation position of the inertial measurement device based on the triaxial acceleration from which the acceleration noise is removed and the triaxial angular velocity measured by the inertial measurement device;
a track state calculation step of calculating track state information representing a track state based on the estimated horizontal inclination angle and the longitudinal inclination angle;
A method for estimating orbital conditions, comprising: In the tilt angle estimation step,
extracting a translational acceleration component during vehicle travel from the three-axis acceleration from which the acceleration noise has been removed, by filtering;
setting an error covariance matrix of the Kalman filter observation model based on the extracted translational acceleration component;
Installation of the inertial measurement device from the three-axis acceleration and the three-axis angular velocity from which the acceleration noise is removed using a Kalman filter formulated from the state space model configured by the observation model to which the error covariance matrix is set. estimating the horizontal tilt angle and the longitudinal tilt angle at the position,
2. The track state estimation method according to claim 1, wherein, in said track state calculation step, an elevation displacement is calculated as said track state information based on said estimated longitudinal inclination angle. 3. The track state estimation method according to claim 2, wherein in said track state calculation step, the calculated elevation displacement is subjected to high-pass filter processing for removing estimation errors of low-frequency components. 4. The track state according to claim 2, wherein said translational acceleration component is extracted by band-pass filter processing for removing dominant frequency components of track displacement from lateral acceleration and longitudinal acceleration among said three-axis acceleration. estimation method. 5. The error covariance matrix according to any one of claims 2 to 4, wherein the error covariance matrix is set by adding a value obtained by multiplying the extracted translational acceleration component by a predetermined coefficient to the diagonal component. Track state estimation method. In the acceleration noise removal step, the acceleration component calculated from the traveling speed of the vehicle is subtracted from the longitudinal acceleration component of the triaxial acceleration measured by the inertial measurement device, thereby removing the acceleration noise included in the triaxial acceleration. The track state estimation method according to any one of claims 1 to 5, wherein the track state estimation method is removed. an acceleration noise removal processing unit that removes acceleration noise generated by acceleration and deceleration of the vehicle from three-axis acceleration measured by an inertial measurement device installed in the vehicle body or axle box based on the running speed of the vehicle;
a tilt angle estimating unit for estimating a horizontal tilt angle and a forward/backward tilt angle at an installation position of the inertial measurement device based on the three-axis acceleration from which the acceleration noise is removed and the three-axis angular velocity measured by the inertial measurement device;
a track state calculation unit that calculates track state information representing a track state based on the estimated horizontal inclination angle and the estimated longitudinal inclination angle;
A track state estimation device. a speed measuring device that measures the running speed of the vehicle;
an inertial measurement device installed in the vehicle body or axle box of the vehicle;
an acceleration noise removal unit that removes acceleration noise generated by acceleration/deceleration of the vehicle from the triaxial acceleration measured by the inertial measurement device based on the traveling speed of the vehicle;
a tilt angle estimating unit for estimating a horizontal tilt angle and a forward/backward tilt angle at an installation position of the inertial measurement device based on the three-axis acceleration from which the acceleration noise is removed and the three-axis angular velocity measured by the inertial measurement device;
a track state estimation device comprising: a track state calculation unit that calculates track state information representing a track state based on the estimated horizontal inclination angle and the longitudinal inclination angle;
a vehicle.

Images