JP2022131020A - Measurement method, measurement device, measurement system, and measurement program - Google Patents

Abstract

To provide a measurement method that can generate measurement data with reduced drift noise without the need to prepare in advance information for reducing the drift noise.SOLUTION: A measurement method includes: a low-pass filter processing step of performing low-pass filter processing on object data to generate vibration component reduced data; a high-pass filter processing step of performing high-pass filter processing on the vibration component reduced data to generate drift noise reduced data; a correction data estimation step of estimating, on the basis of, the drift noise reduced data, correction data corresponding to a difference between data obtained by excluding drift noise from the vibration component reduced data and the drift noise reduced data; a vibration component data generation step of subtracting the vibration component reduced data from the object data to generate vibration component data; and a measurement data generation step of adding up the drift noise reduced data, the correction data, and the vibration component data to generate measurement data.SELECTED DRAWING: Figure 31

Description

The present invention relates to a measuring method, a measuring device, a measuring system and a measuring program.

In Patent Document 1, a static component storage unit that stores a time series of a static component that is a component that does not depend on the motion of the railway vehicle in the time series of the displacement of the girder of the bridge due to the passage of the railway vehicle, and a measurement object A displacement detection unit that detects the time series of displacement of the girder based on at least one of the acceleration measurement value or velocity measurement value of the girder of the bridge to be measured due to the passage of the railway vehicle, and the displacement detected by the displacement detection unit A dynamic component extraction unit that extracts the time series of dynamic components, which are the components remaining after excluding static components that may contain errors, from the time series of , and obtains the time series of static components from the static component storage unit. and a synthesis unit for synthesizing the time series of the dynamic component extracted by the dynamic component extraction unit and the time series of the static component acquired by the static component acquisition unit. An acquisition device is described.

According to the displacement acquisition device described in Patent Document 1, static components that may contain errors are removed from the time series of detected displacements of the digits, and replaced with stored static components to obtain displacements without errors. can be obtained.

JP 2009-237805 A

However, in the displacement acquisition device described in Patent Document 1, the similarity between the static component included in the time series of the detected displacement of the girder and the stored static component determines the accuracy of the time series of the obtained displacement. If the accuracy of the approximation is not sufficient, the accuracy of the displacement time series may decrease. Further, in the displacement acquisition device described in Patent Document 1, when the static component included in the time series of displacement at the time of measurement changes due to changes in the environment or the like, the static component and the stored static component There is no means for recognizing the deviation from the component, and it is impossible to know that there is a problem with the accuracy of the displacement. In addition, in the displacement acquisition device described in Patent Document 1, data of static components for each classification of railway vehicles and each classification of bridges must be stored, and it is necessary to acquire and update the data. becomes complicated, and cost reduction is difficult. Therefore, there is a demand for a method of reducing errors without preparing in advance information for reducing errors such as static component data.

One aspect of the measuring method according to the present invention is
a low-pass filtering step of low-pass filtering target data including drift noise and vibration components to generate vibration component reduction data in which the vibration components are reduced;
a high-pass filtering step of performing high-pass filtering on the vibration component-reduced data to generate drift noise-reduced data in which the drift noise is reduced;
a correction data estimation step of estimating correction data corresponding to a difference between data obtained by removing the drift noise from the vibration component reduction data and the drift noise reduction data, based on the drift noise reduction data;
a vibration component data generation step of generating vibration component data including the vibration component by subtracting the vibration component reduction data from the target data;
and a measurement data generation step of adding the drift noise reduction data, the correction data, and the vibration component data to generate measurement data.

Another aspect of the measuring method according to the present invention is
a low-pass filtering step of low-pass filtering target data including drift noise and vibration components to generate vibration component reduction data in which the vibration components are reduced;
a high-pass filtering step of performing high-pass filtering on the vibration component-reduced data to generate drift noise-reduced data in which the drift noise is reduced;
Calculate the first peak and the second peak of the drift noise reduction data, the first section before the first peak, the second section between the first peak and the second peak, and the second peak a section identifying step of identifying a subsequent third section;
a correction data estimating step of estimating correction data corresponding to a difference between the data obtained by removing the drift noise from the vibration component reduction data and the drift noise reduction data in the second section based on the drift noise reduction data; ,
a vibration component data generation step of generating vibration component data including the vibration component by subtracting the vibration component reduction data from the target data;
Measurement is performed using the first section as the vibration component reduction data, the drift noise reduction data, the correction data, and the vibration component data in the second section, and the third section as the vibration component reduction data. and a measurement data generating step for generating data.

One aspect of the measuring device according to the present invention is
a low-pass filter processing unit that performs low-pass filtering on target data containing drift noise and vibration components to generate vibration component reduction data in which the vibration components are reduced;
a high-pass filter processing unit that performs high-pass filtering on the vibration component reduction data to generate drift noise reduction data in which the drift noise is reduced;
a correction data estimation unit for estimating correction data corresponding to a difference between data obtained by removing the drift noise from the vibration component reduction data and the drift noise reduction data, based on the drift noise reduction data;
a vibration component data generation unit that generates vibration component data including the vibration component by subtracting the vibration component reduction data from the target data;
a measurement data generation unit that adds the drift noise reduction data, the correction data, and the vibration component data to generate measurement data.

One aspect of the measurement system according to the present invention is
An aspect of the measuring device;
and an observation device that observes the observation point,
The target data is data based on observation data by the observation device.

One aspect of the measurement program according to the present invention is
a low-pass filtering step of low-pass filtering target data including drift noise and vibration components to generate vibration component reduction data in which the vibration components are reduced;
a high-pass filtering step of performing high-pass filtering on the vibration component-reduced data to generate drift noise-reduced data in which the drift noise is reduced;
a correction data estimation step of estimating correction data corresponding to a difference between data obtained by removing the drift noise from the vibration component reduction data and the drift noise reduction data, based on the drift noise reduction data;
a vibration component data generation step of generating vibration component data including the vibration component by subtracting the vibration component reduction data from the target data;
and a measurement data generation step of adding the drift noise reduction data, the correction data, and the vibration component data to generate measurement data.

The figure which shows the structural example of a measurement system. FIG. 2 is a cross-sectional view of the upper structure of FIG. 1 taken along line AA; Explanatory drawing of the acceleration which an acceleration sensor detects. FIG. 4 is a diagram showing the relationship between frequency characteristics F{M _d (k)}, F{M(k)}, F{e(k)}; FIG. 4 is a diagram showing the relationship between frequency characteristics F{M _s (k)}, F{M(k)}, F{e(k)}; The figure which shows the frequency characteristic F{MV(k)}. FIG. 4 is a diagram showing the relationship between frequency characteristics F{M _s (k)}, F{f _HP (M _s (k))}, and F {f _LP (M _s (k))}; FIG. 4 is a diagram showing the relationship between frequency characteristics F{M _s ′(k)}, F{f _HP (M _s (k))}, and F{A _LP (f _HP (M _s (k)))}; FIG. 4 is a diagram showing the relationship between frequency characteristics F{M _d '(k)}, F{M _s '(k)}, and F{M _V (k)}; The figure which shows the data _Ms (k) which is a unit pulse waveform. The figure which shows the data _fLP ( _Ms (k)) which processed the data _Ms (k) with the low-pass filter. FIG. 4 is a diagram showing data f _HP (M _s (k)) obtained by high-pass filtering the data M _s (k); The figure which shows an example of the object data U (k). FIG. 4 is a diagram showing the power spectrum density of target data U(k); The figure which shows an example of displacement data _Ms (k). The figure which shows the power spectrum density of displacement data _Ms (k). The figure which shows an example of vibration component data U _OSC (k). The figure which shows an example of displacement data MU(k). FIG. 4 is a diagram showing an example of first section correction data M _CC1 (k) and third section correction data M _CC3 (k); The figure which shows an example of 2nd area 1st correction data M1 _CC2 (k). The figure which shows an example of straight line _LC (k). The figure which shows an example of 2nd area 2nd correction|amendment data M2 _CC2 (k). The figure which shows an example of 2nd area correction data M _CC2 (k). The figure which shows an example of correction data M _CC (k). The figure which shows an example of displacement data RU(k). The figure which shows an example of the measurement data U' (k). FIG. 4 is a diagram showing an example of displacement waveform UO(k) and drift noise D(k); The figure which shows an example of the object data U (k). The figure which shows the measurement data U' (k). FIG. 4 is a diagram showing measurement data U′(k) and a displacement waveform UO(k) superimposed; The flowchart figure which shows an example of the procedure of the measuring method of 1st Embodiment. FIG. 4 is a flow chart diagram showing an example of the procedure of a correction data estimation process in the first embodiment; The figure which shows the structural example of a sensor, a measuring device, and a monitoring device. The flowchart figure which shows an example of the procedure of the measuring method of 2nd Embodiment. FIG. 11 is a flow chart diagram showing an example of the procedure of a correction data estimation step in the second embodiment; The figure which shows the structural example of the measuring device in 2nd Embodiment. The figure which shows the other structural example of a measurement system. The figure which shows the other structural example of a measurement system. The figure which shows the other structural example of a measurement system. FIG. 40 is a cross-sectional view of the upper structure of FIG. 39 taken along line AA;

Preferred embodiments of the present invention will be described in detail below with reference to the drawings. It should be noted that the embodiments described below do not unduly limit the content of the present invention described in the claims. Moreover, not all the configurations described below are essential constituent elements of the present invention.

1. First Embodiment 1-1. Configuration of Measurement System A measurement system for realizing the measurement method of the present embodiment will be described below, taking as an example a case where the structure is a superstructure of a bridge and the moving object is a railroad vehicle.

FIG. 1 is a diagram showing an example of a measurement system according to this embodiment. As shown in FIG. 1 , a measurement system 10 according to this embodiment includes a measurement device 1 and at least one sensor 2 provided on a superstructure 7 of a bridge 5 . Moreover, the measurement system 10 may include a monitoring device 3 .

The bridge 5 consists of a superstructure 7 and a substructure 8 . FIG. 2 is a cross-sectional view of the upper structure 7 taken along line AA of FIG. As shown in FIGS. 1 and 2, the superstructure 7 includes a floor plate F, a main girder G, a bridge floor 7a composed of cross girders (not shown), bearings 7b, rails 7c, sleepers 7d, and ballast 7e. ,including. Also, as shown in FIG. 1, the substructure 8 includes a bridge pier 8a and an abutment 8b. The superstructure 7 is a structure spanning any one of adjacent abutments 8b and piers 8a, two adjacent abutments 8b, or two adjacent piers 8a. Both ends of the superstructure 7 are located at adjacent abutments 8b and piers 8a, at two adjacent abutments 8b, or at two adjacent piers 8a.

The measuring device 1 and each sensor 2 are connected by, for example, a cable (not shown) and communicate via a communication network such as CAN. CAN is an abbreviation for Controller Area Network. Alternatively, the measuring device 1 and each sensor 2 may communicate via a wireless network.

For example, each sensor 2 outputs data for calculating the displacement of the superstructure 7 due to the movement of the railway vehicle 6, which is a moving object. In this embodiment, each sensor 2 is an acceleration sensor, and may be, for example, a crystal acceleration sensor or a MEMS acceleration sensor. MEMS is an abbreviation for Micro Electro Mechanical Systems.

In this embodiment, each sensor 2 is installed in the longitudinal central portion of the superstructure 7, specifically, in the longitudinal central portion of the main girder G. As shown in FIG. However, each sensor 2 only needs to be able to detect the acceleration for calculating the displacement of the upper structure 7 , and its installation position is not limited to the central portion of the upper structure 7 . If each sensor 2 is provided on the floor plate F of the superstructure 7, it may be destroyed by running of the railroad vehicle 6, and the measurement accuracy may be affected by local deformation of the bridge floor 7a. 1 and 2 each sensor 2 is provided on the main girder G of the superstructure 7 .

The floor plate F, the main girder G, and the like of the superstructure 7 are vertically bent by the load of the railway vehicle 6 running on the superstructure 7 . Each sensor 2 detects the acceleration of bending of the floor plate F and the main girder G due to the load of the railway vehicle 6 running on the superstructure 7 .

Based on the acceleration data output from each sensor 2 , the measuring device 1 calculates the deflection displacement of the superstructure 7 due to the running of the railroad vehicle 6 . The measuring device 1 is installed, for example, on the abutment 8b.

The measuring device 1 and the monitoring device 3 can communicate with each other via a communication network 4 such as a mobile phone wireless network and the Internet. The measuring device 1 transmits to the monitoring device 3 information on the displacement of the superstructure 7 due to the running of the railroad vehicle 6 . The monitoring device 3 may store the information in a storage device (not shown), and perform processes such as monitoring the railcar 6 and determining abnormality of the upper structure 7 based on the information.

In addition, in this embodiment, the bridge 5 is a railway bridge, and is, for example, a steel bridge, a girder bridge, an RC bridge, or the like. RC is an abbreviation for Reinforced-Concrete.

As shown in FIG. 2, an observation point R is set in association with the sensor 2 in this embodiment. In the example of FIG. 2, the observation point R is set at a position on the surface of the superstructure 7 vertically above the sensor 2 provided on the main girder G. That is, the sensor 2 is an observation device that observes the observation point R. The sensor 2 that observes the observation point R may be provided at a position that can detect the acceleration generated at the observation point R due to the running of the railroad vehicle 6, but is preferably provided at a position close to the observation point R.

The number and installation positions of the sensors 2 are not limited to the examples shown in FIGS. 1 and 2, and various modifications are possible.

Based on the acceleration data output from the sensor 2, the measuring device 1 acquires the acceleration in the direction intersecting with the surface of the upper structure 7 on which the railway vehicle 6 moves. The plane of the superstructure 7 on which the railcar 6 moves is the direction in which the railcar 6 moves, i.e. the longitudinal direction of the superstructure 7, the X direction, and the direction perpendicular to the direction in which the railcar 6 moves, i.e. the superstructure 7 and the Y direction, which is the width direction of the . As the railroad vehicle 6 runs, the observation point R bends in the direction perpendicular to the X and Y directions. , that is, the acceleration in the Z direction, which is the normal direction of the floor plate F, is preferably obtained.

FIG. 3 is a diagram illustrating acceleration detected by the sensor 2. FIG. The sensor 2 is an acceleration sensor that detects acceleration occurring in each of three axial directions orthogonal to each other.

In order to detect the acceleration of the bending of the observation point R due to the running of the railroad vehicle 6, the sensor 2 has three detection axes, i.e., the x-axis, the y-axis, and the z-axis. It is installed so that it is in the direction of In FIGS. 1 and 2, the sensor 2 is installed such that one axis intersects the X direction and the Y direction. Since the observation point R bends in a direction perpendicular to the X and Y directions, ideally, the sensor 2 should have one axis perpendicular to the X and Y directions in order to accurately detect the acceleration of the bending. It is installed in accordance with the Z direction, that is, the normal direction of the floor board F.

However, when the sensor 2 is installed on the upper structure 7, the installation location may be tilted. In the measurement device 1, even if one of the three detection axes of the sensor 2 is not installed in the normal direction of the floor plate F, the error is small and negligible because it is generally oriented in the normal direction. In addition, even if one of the three detection axes of the sensor 2 is not aligned with the normal direction of the floor plate F, the measuring device 1 can perform three-axis synthesis by synthesizing the acceleration of the x-axis, the y-axis, and the z-axis. Acceleration can be used to correct detection errors due to tilting of the sensor 2 . Moreover, the sensor 2 may be a uniaxial acceleration sensor that detects at least the acceleration generated in a direction substantially parallel to the vertical direction or the acceleration in the normal direction of the floor plate F.

Below, first, the basic concept of the measurement method of the present embodiment executed by the measurement apparatus 1 will be described, and then the details will be described.

1-2. Basic concept of the measurement method First, let M _d (k) be the target data to be processed, which is obtained based on the acceleration data output from the sensor 2, and the target data M _d ( k) contains a significant signal M(k) containing an oscillating component and drift noise e(k). Assuming that the number of samples included in the target data M _d (k) is N, k is an integer from 0 to N−1.

Drift noise e(k) is mainly not the signal input to the sensor 2, but an error signal generated inside the sensor 2 such as zero-point error, drift due to temperature change, drift due to nonlinear sensitivity. . The drift noise e(k) is a long-period variation compared to the signal input to the sensor 2, and its energy is distributed in the low frequency range. FIG. 4 shows the frequency characteristics F{M _d (k)} of the target data M _d (k), the frequency characteristics F{M(k)} of the signal M(k), and the frequency characteristics F{ e(k)}.

The vibration components contained in the signal M(k) are, for example, the signal component of the fundamental frequency generated by the natural vibration of the bridge 5 and its harmonic components, and the energy is generally distributed in a frequency range higher than the drift noise e(k). . Therefore, by subjecting the target data M _d (k) to low-pass filter processing as in Equation (2), data M _s (k) with reduced vibration components can be obtained.

The low-pass filtering process for reducing the vibration component is a process of moving-averaging the target data M _d (k) with a period corresponding to the fundamental frequency calculated based on the frequency characteristic F {M _d (k)}. Alternatively, FIR filter processing that attenuates signal components with frequencies equal to or higher than the fundamental frequency may be used. FIR is an abbreviation for Finite Impulse Response. FIG. 5 shows the frequency characteristics F{M _s (k)} of data M _s (k) obtained by moving average processing of target data M _d (k), and the frequency characteristics F {M ( k)} and the frequency characteristic F{e(k)} of the drift noise e(k).

Further, by subtracting the data M _s (k) from the target data M _d (k) as in Equation (3), the data M _V (k) containing the vibration component is obtained. FIG. 6 shows the frequency characteristic F{MV(k)} of the data M _V (k) containing the vibration component.

Let f _HP (M _s (k)) be the data obtained by subjecting the data M _s (k) to high-pass filtering, and let f _LP (M _s (k)) be the data obtained by subjecting the data M _s (k) to low-pass filtering. The relationship among M _s (k), data f _HP (M _s (k)), and data f _LP (M _s (k)) is given by Equation (4).

Further, the frequency characteristic F {M _s (k)} of the data M _s (k), the frequency characteristic F {f _HP (M _s (k))} of the data f _HP (M _s (k)), and the data f _LP ( The relationship of the frequency characteristic F{f _LP (M _s (k))} of M _s (k)) is given by Equation (5). FIG. 7 shows the relationship between the frequency characteristics F{M _s (k)}, F{f _HP (M _s (k))}, and F {f _LP (M _s (k))}.

Since the drift noise e(k) is observed like an offset error, high-pass filtering for attenuating signals in the low frequency range is effective in removing the drift noise e(k). When the data M _s (k) is subjected to high-pass filtering, the drift noise e(k) whose energy is distributed in the low frequency range is sufficiently suppressed, and the data f _HP (M _S (k)) is assumed to be approximately equal to the high-pass filtered data f _HP (M(k)) of the signal M(k).

Since the signal components in the low-frequency range of the signal M(k) are also lost by high-pass filtering, data f _HP (M _s (k)) obtained by high-pass filtering the data M _s (k) is used to compensate for these signal components. , the data f _LP (M(k)) obtained by low-pass filtering the signal M(k) is estimated. As shown in equation (7), data f _LP (M(k)) obtained by low-pass filtering the signal M(k) is data f _HP (M _s (k)) obtained by high-pass filtering the data M _s (k). , assume that the signal M(k) is approximately equal to the low-pass filtered data f _LP (M(k)) and the estimated data A _LP (f _HP (M _s (k))).

As shown in equation (8), data obtained by removing drift noise e(k) from data M _s (k) is data f _HP (M _s (k)) obtained by high-pass filtering data M _s (k) and signal Assuming that M(k) is equal to the sum of the low-pass filtered data f _LP (M(k)), equations (6), (7) and (8) yield equation (9).

From the equation (9), the frequency characteristic F{M _s '(k)} of the data M _s '(k) and the frequency characteristic F{f _HP (M _s (k)) of the data f _HP (M _s (k)) } and the frequency characteristic F{A _LP (f _HP (M _s (k)))} of the data A _LP (f _HP (M _s (k))) is given by Equation (10). FIG. 8 shows the relationship between the frequency characteristics F{M _s ′(k)}, F{f _HP (M _s (k))}, and F{A _LP (f _HP (M _s (k)))}.

As shown in _Equation (11), data _{M d} '(k) is obtained. FIG. 9 shows the relationship between the frequency characteristics F{M _d ′(k)}, F{M _s ′(k)} and F{M _V (k)}.

By high-pass filtering the data M _s (k), data f _HP (M _s (k)) with reduced drift noise e(k) is obtained, so that this data f _HP (M _s (k)) data f _LP (M(k)) obtained by low-pass filtering the signal M(k) is estimated, and data f _HP (M _s (k)), the estimated data, and data M _V (k ), a signal M(k) with reduced drift noise e(k) can be obtained.

In the following, taking the case where the data M _s (k) is displacement data as an example, the signal M(k) is obtained from the data f _HP (M _s (k)) obtained by subjecting the data M _s (k) to high-pass filtering. A procedure for estimating the low-pass filtered data f _LP (M(k)) will now be described.

First, as shown in Equation (12), a unit pulse waveform obtained by simplifying the deflection displacement of the superstructure 7 of the bridge 5 when the railway vehicle 6 passes is assumed as the data M _s (k). In formula (12), k is each integer of 0 or more. FIG. 10 shows the data M _s (k), which is the unit pulse waveform represented by Equation (12).

The relationship between the data M _s (k), the data f _HP (M _s (k)) obtained by high-pass filtering the data M _s (k), and the data f _LP (M _s (k)) obtained by performing low-pass filtering on the data M s (k) is given by Equation (13). ).

For example, if the low-pass filter processing is moving average processing, Equation (14) is obtained from Equation (13). At this time, the data k is positioned at the center of the moving average section 2p+1.

In equation (14), p is an integer of 1 or more, and since it is desired to provide a flat portion in data f _LP (M _s (k)) obtained by performing low-pass filtering on data M _s (k), p<(k _a − k _b )/2. FIG. 11 shows data f _LP (M _s (k)) obtained by low-pass filtering the data M _s (k), which is the unit pulse waveform represented by Equation (12), using a moving average. FIG. 12 shows data f _HP (M _s (k)) obtained by high-pass filtering the data M _s (k), which is the unit pulse waveform represented by Equation (12).

Using FIGS. 11 and 12, data f _HP (M _s (k)) obtained by performing high-pass filtering and data f _LP (M _s (k)) obtained by performing low-pass filtering on data M _s (k), which is a unit pulse waveform. Compare with

As shown in FIG. 11, the slope b of the section from k _a −p to k _a +p of the data f _LP (M _s (k)) obtained by low-pass filtering the data M _s (k) is given by equation (15). Calculated.

Also, the slope of the section from k _b −p to k _b +p of the data f _LP (M _s (k)) is −b, and the amplitude B of the section from k _a +p to k _b −p is −1. .

On the other hand, as shown in FIG. 12, the gradient a of the interval from k _a −p to k _a of the data f _HP (M _s (k)) obtained by high-pass filtering the data M _s (k) is given by equation (16) calculated by

Also, the slope of the section from k _b to k _b +p of the data f _HP (M _s (k)) is −a, and the amplitude A at k=k _a −1 is calculated by equation (17).

Substituting equation (12) above into equation (17), the amplitude A is calculated as in equation (18).

From equation (18), if p is sufficiently large, the amplitude A becomes 1/2.

Here, the unit pulse waveform shown by Equation (12) assumed as data M _s (k) does not contain drift noise e(k). Therefore, the data f _LP (M _s (k)) obtained by low-pass filtering the data M _s (k) is equal to the data f _LP (M(k)) obtained by low-pass filtering the signal M(k). Therefore, the comparison of the data f _HP (M _s (k)) and the data f _LP (M _s (k)) can be performed by comparing the data f _HP (M _s (k)) with the data f _LP (M(k)). It is a comparison, and by measuring the slope a and the amplitude A of the data f _HP (M _s (k)), it is possible to estimate the data f _LP (M(k)) obtained by low-pass filtering the signal M(k). can.

1-3. Details of Measurement Method In practice, the target data U(k), which is the displacement data of the deflection of the superstructure 7 of the bridge 5 when the railroad vehicle 6 passes, is convex in the positive or negative direction, which is different from the unit pulse waveform. _{Data f LP (} M( k)) can be estimated. For example, a positively or negatively convex waveform is a rectangular waveform, a trapezoidal waveform, or a half-sine waveform.

First, the measuring device 1 generates velocity data V _s (k) by integrating the acceleration data A _s (k) output from the acceleration sensor as shown in Equation (19), and further, as shown in Equation (20). , the target data U(k) is generated by integrating the velocity data V _s (k). In equations (19) and (20), ΔT is the data time interval. FIG. 13 shows an example of target data U(k).

Next, the measurement apparatus 1 performs displacement data _{M s} ( k).

Specifically, first, the measuring device 1 performs fast Fourier transform processing on the target data U( _k ) to calculate the power spectrum density, and calculates the peak of the power spectrum density as the fundamental frequency Ff. FIG. 14 shows the power spectrum density obtained by subjecting the target data U(k) of FIG. 13 to fast Fourier transform processing. In the example of FIG. 14, the fundamental frequency _Ff is calculated as approximately 3 Hz. Then, the measuring apparatus 1 calculates the fundamental period T _f from the fundamental frequency F _f by Equation (21), and adjusts the time resolution of the data by dividing the fundamental period T _f by ΔT as shown in Equation (22). Then, the moving average interval _kmf is calculated. The fundamental period T _f is the period corresponding to the fundamental frequency F _f and T _f >2ΔT.

Then, as the low-pass filter processing, the measuring device 1 performs moving average processing on the target data U(k) at the fundamental period T _f according to Equation (23) to reduce the vibration component included in the signal M(k). Displacement data M _s (k) is generated as vibration component reduction data. This moving average processing not only requires a small amount of calculation, but also provides displacement data _{M s} ( k) is obtained. FIG. 15 shows an example of displacement data M _s (k). Also, FIG. 16 shows the power spectrum density of the displacement data M _s (k). As shown in FIGS. 15 and 16, the displacement data M _s (k) are obtained from which most of the vibration components contained in the target data U(k) are removed.

Note that the measurement apparatus 1 generates displacement data M _s (k) by performing FIR filtering for attenuating signal components having a frequency equal to or greater than the fundamental period T _f of the target data U(k) as low-pass filtering. may FIR is an abbreviation for Finite Impulse Response. This FIR filter processing requires a larger amount of calculation than the moving average processing, but can attenuate all signal components of frequencies equal to or higher than the fundamental frequency _Ff .

Next, the measuring device 1 subtracts the displacement data M _s (k) with the vibration component reduced from the target data U(k) according to the equation (24) to obtain the vibration component data U _OSC (k) containing the vibration component. ). FIG. 17 shows an example of the vibration component data U _OSC (k).

In addition, the measuring apparatus 1 generates displacement data MU(k) by high-pass filtering the displacement data M _s (k) in order to reduce drift noise, as in Equation (25). FIG. 18 shows an example of the displacement data MU(k).

Next, based on the displacement data MU(k), the measurement apparatus 1 converts data f _LP (M(k)) obtained by low-pass filtering the signal M(k), that is, drift noise from the displacement data M _s (k). Correction data M _CC (k) corresponding to the difference between the data excluding the displacement data MU(k) and the displacement data MU(k) is estimated.

As shown in FIG. 18, in the present embodiment, the measuring device 1 identifies the first section T1, the second section T2, and the third section T3 based on the displacement data MU(k), and corrects the correction data M _CC ( k) is generated by dividing it into these three intervals. In order to specify the first section T1, the second section T2 and the third section T3, the measuring device 1 detects the first peak p ₁ =(k ₁ , mu ₁ ) and the second peak p ₂ = (k ₂ , mu ₂ ). As shown in FIG. 18, the _first peak p1 is the leading peak near the time when the railcar 6 entered the superstructure 7, and the _second peak p2 is the peak when the railcar 6 entered from the superstructure 7. This is the last peak near the time. The first section T1 is a section before the _first peak p1, that is, _a section of k≦k1. A _second section T2 is a section between the _first peak p1 and the _second peak p2, that is, a section of k1< _k <k2. A third section T3 is a section after the _second peak p2, that is, a section satisfying k ₂ ≦k.

As shown in equation (26), the correction data M _CC (k) is composed of the first section correction data M _CC1 (k), which is the correction data for the first section T1, and the second section correction data, which is the correction data for the second section T2. It is obtained as the sum of the correction data M _CC2 (k) and the third section correction data M _CC3 (k) which is the correction data for the third section T3.

The first section correction data M _CC1 (k) is obtained by Equation (27) using data MU′(k) obtained by inverting the sign of displacement data MU(k). Similarly, the third section correction data M _CC3 (k) is obtained by Equation (28) using data MU′(k) obtained by inverting the sign of displacement data MU(k). FIG. 19 shows an example of the first section correction data M _CC1 (k) and the third section correction data M _CC3 (k).

The second section correction data M _CC2 (k) is obtained as follows. First, in a section of k≦(k ₁ +k ₂ )/2 which is before a predetermined time in the second section T2, the displacement data MU(k) before the first peak p ₁ are processed in reverse order from the first peak p ₁ onwards. The permuted data are MU(2k ₁ -k). Further, in the section of (k ₁ +k ₂ )/2≦k, which is after the predetermined time of the second section T2, the displacement data MU(k) after the _second peak p2 are transferred in reverse order to before the _second peak p2. The permuted data produces MU(2k ₂ -k). Here, the predetermined time is the time corresponding to k=k ₁ +k ₂ , but it may be other time.

Then, using the data MU(2k ₁ −k) and the data MU(2k ₂ −k), the second section first correction data M1 _CC2 (k) is obtained according to equation (29). FIG. 20 shows an example of the second section first correction data M1 _CC2 (k).

A straight line L _C (k) passing through the first peak p ₁ =(k ₁ , mu ₁ ) and the second peak p2=(k ₂ , mu ₂ ) is obtained by equation (30). FIG. 21 shows an example of the straight line L _C (k).

The second section second correction data M2 _CC2 (k) is obtained by using the straight line data −2L _C (k) obtained by multiplying the straight line L _C (k) by −2 according to the equation (31). FIG. 22 shows an example of the second section second correction data M2 _CC2 (k).

As shown in equation (32), the second section correction data M _CC2 (k) is obtained as the sum of the second section first correction data M1 _CC2 (k) and the second section second correction data M2 _CC2 (k). be done. FIG. 23 shows an example of the second section correction data M _CC2 (k).

Correction data M _CC (k) is obtained by formula (33) by substituting formula (27), formula (28) and formula (32) into formula (26). FIG. 24 shows an example of correction data M _CC (k).

Then, as in equation (34), the displacement data MU(k) and the correction data M _CC (k) are added to obtain the displacement data RU(k) with reduced vibration component and drift noise.

By substituting equation (33) into equation (34), equation (35) is obtained.

Equation (35) is transformed into Equation (36).

From equation (36), the displacement data RU(k) is 0 in the _first section T1 where k≦k1 and the _second section T2 where k2≦k, and the vibration component and the drift noise are The removed displacement data RU(k) are obtained. FIG. 25 shows an example of displacement data RU(k).

Then, as in equation (37), the displacement data RU(k) and the vibration component data U _OSC (k) are added to obtain measurement data U′(k), which is displacement data with reduced drift noise. be done. FIG. 26 shows an example of measurement data U'(k).

Substituting equation (36) into equation (37) yields equation (38).

In order to confirm the effect of removing drift noise by the measurement method of the present embodiment, drift noise D(k) was added to the displacement waveform UO(k) as shown in Equation (39) as the target data U(k). Use waveforms. FIG. 27 shows an example of displacement waveform UO(k) and drift noise D(k). Also, FIG. 28 shows an example of the target data U(k).

For the object data U(k), the measurement data U'(k) obtained by equations (21) to (38) are compared with the displacement waveform UO(k). FIG. 29 shows the measurement data U'(k). Also, FIG. 30 shows the measurement data U'(k) and the displacement waveform UO(k) superimposed. As shown in FIGS. 29 and 30, it can be confirmed that the measurement data U′(k) in which the drift noise is removed and the displacement waveform is restored is obtained by the measurement method of the present embodiment.

1-4. Procedure of Measurement Method FIG. 31 is a flow chart showing an example of the procedure of the measurement method according to the first embodiment for measuring the displacement of the superstructure 7 of the bridge 5 . In this embodiment, the measuring device 1 performs the procedure shown in FIG.

As shown in FIG. 31, first, in a target data generation step S1, the measuring device 1 acquires acceleration data A _s (k), which is observation data, and generates target data U(k). Therefore, the target data U(k) is data based on the acceleration data A _s (k), which is observation data by the sensor 2, which is an observation device. Specifically, the measuring device 1 generates the target data U(k) by performing the calculations of the above equations (19) and (20). In the present embodiment, the target data U(k) to be processed is data on displacement of the superstructure 7 by the railroad vehicle 6, which is a moving object that moves the superstructure 7, which is a structure. This data is obtained by integrating twice the acceleration in the direction intersecting with the surface of the upper structure 7 . Therefore, the target data U(k) includes data of a convex waveform in the positive or negative direction, specifically rectangular waveform, trapezoidal waveform, or half-sine waveform. Rectangular waveforms include not only exact rectangular waveforms but also waveforms that approximate rectangular waveforms. Similarly, trapezoidal waveforms include waveforms that approximate trapezoidal waveforms as well as exact trapezoidal waveforms. Similarly, half-sine waveforms include exact half-sine waveforms as well as waveforms that approximate half-sine waveforms.

Next, in the low-pass filter processing step S2, the measuring device 1 performs low-pass filtering on the target data U(k) including the drift noise and the vibration component generated in the step S1, and converts the vibration component into reduced vibration component data. to generate displacement data M _s (k) of . For example, the measurement device 1 performs fast Fourier transform processing on the target data U(k) to calculate the fundamental frequency F _f , and performs low-pass filtering to correspond to the fundamental frequency F _f as shown in the above equation (23). The displacement data M _s (k) may be generated by performing moving average processing on the target data U(k) at the fundamental period T _f . Further, for example, the measuring apparatus 1 performs fast Fourier transform processing on the target data U( _k ) to calculate the fundamental frequency Ff, and performs low-pass filter processing on the target data U( _k ) at frequencies equal to or higher than the fundamental frequency Ff. Displacement data M _s (k) may be generated by FIR filtering that attenuates frequency signal components.

Next, in the high-pass filtering step S3, the measuring device 1 performs high-pass filtering on the displacement data M _s (k) including the drift noise generated in the step S2 as in the above equation (25) to is generated as drift noise reduction data MU(k). The high-pass filtering process of the displacement data M _s (k) is a process of subtracting data obtained by performing low-pass filtering of the displacement data M _s (k) from the displacement data M _s (k), as in the above equation (14). may be The low-pass filtering may be moving average or FIR filtering. That is, the high-pass filtering of the displacement data M _s (k) may be a process of subtracting data obtained by subjecting the displacement data M _s (k) to moving average processing or FIR filtering from the displacement data M _s (k). .

Next, in the correction data estimating step S4, the measuring device 1 removes the drift noise from the displacement data M _s (k) based on the displacement data MU(k) generated in the step S3 and the displacement data MU(k). ) to estimate the correction data M _CC (k). Specifically, the measuring device 1 performs the calculations of the above-described formulas (26) to (33) to generate the correction data M _CC (k).

Further, in the vibration component data generation step S5, the measuring device 1 converts the target data U(k) generated in step S1 to the displacement data M _s (k) generated in step S2, as shown in Equation (24) above. is subtracted to generate the vibration component data U _OSC (k) containing the vibration component. In this embodiment, the frequency of drift noise included in the target data U(k) is lower than the minimum value of the natural vibration frequency of the upper structure 7 . The minimum value of the natural vibration frequency of the upper structure 7 is, for example, the frequency of the primary vibration mode in the longitudinal direction of the upper structure 7 . By setting the cut-off frequency of the low-pass filtering process in step S2 and the cut-off frequency of the high-pass filtering process in step S3 higher than the drift noise frequency of the upper structure 7 and lower than the minimum value of the natural vibration frequency. , in the vibration component data U _OSC (k) generated in step S5, drift noise is reduced without reducing the signal component of the natural vibration frequency of the upper structure 7 and its harmonic component. For example, the frequency of drift noise may be less than 1 Hz, and the cutoff frequency for low-pass filtering and the cutoff frequency for high-pass filtering may be 1 Hz or higher.

Next, in the measurement data generation step S6, the measuring device 1 generates the displacement data MU(k) generated in the step S3 and the correction data generated in the step S4 as shown in the above equations (34) and (37). M _CC (k) and the vibration component data U _OSC (k) generated in step S5 are added to generate measurement data U′(k).

Next, in a measurement data output step S<b>7 , the measurement device 1 outputs the measurement data U′(k) generated in step S<b>6 to the monitoring device 3 . Specifically, the measuring device 1 transmits the measurement data U′(k) to the monitoring device 3 via the communication network 4 .

Then, in step S8, the measuring device 1 repeats the processes of steps S1 to S7 until the measurement of the displacement of the superstructure 7 of the bridge 5 is completed.

FIG. 32 is a flow chart showing an example of the correction data estimation step S4 of FIG.

As shown in FIG. 32, first, in the section specifying step S41, the measuring device 1 sets the first peak p ₁ =(k ₁ , mu ₁ ) and the second peak p ₂ =(k ₂ ) of the displacement data MU(k). , mu ₂ ), the first section T1 before the _first peak p1, the second section T2 between the _first peak p1 and the _second peak p2, and the second section T2 after the _second peak p2. 3 section T3 is specified. That is, the _first section T1 is a section of k≦k1, the _second section T2 is a section of k1< _k <k2, and the _third section T3 is a section of k2≦k. In this embodiment, the _first peak p1 is the leading peak near the time when the railcar 6 enters the superstructure 7, and the _second peak p2 is near the time when the railcar 6 leaves the superstructure 7. is the last peak of .

Next, in the first section correction data generating step S42, the measuring device 1 inverts the sign of the displacement data MU(k) in the first section T1 to generate the first section Generate correction data M _CC1 (k).

Next, in the second section correction data generation step S43, the measuring device ₁ generates the displacement data MU -2 is added to the data MU (2k ₁ −k) obtained by rearranging (k) in reverse order from the first peak p ₁ onwards, and the straight line L _C (k) passing through the first peak p ₁ and the second peak p ₂ Add the multiplied linear data −2L _C (k), and rearrange the displacement data MU(k) after the second peak p ₂ in reverse order to before the second peak p ₂ after a predetermined time in the second section T2 The obtained data MU(2k ₂ −k) and the straight line data −2L _C (k) are added to generate the second section correction data M _CC2 (k).

Next, in the third section correction data generating step S44, the measuring device 1 inverts the sign of the displacement data MU(k) in the third section T3 to generate the third section Generate correction data M _CC3 (k).

Finally, in the correction data generation step S45, the measuring device 1 generates the first section correction data M _CC1 (k) generated in step S42 and the second section The correction data M _CC2 (k) and the third section correction data M _CC3 (k) generated in step S44 are added to generate the correction data M _CC (k).

1-5. Configurations of Observation Device, Measurement Device, and Monitoring Device FIG. 33 is a diagram showing a configuration example of the sensor 2, the measurement device 1, and the monitoring device 3, which are observation devices.

As shown in FIG. 33, the sensor 2 includes a communication section 21, an acceleration sensor 22, a processor 23, and a storage section 24.

The storage unit 24 is a memory that stores various programs and data for the processor 23 to perform calculation processing and control processing. The storage unit 24 also stores programs, data, and the like for the processor 23 to implement predetermined application functions.

The acceleration sensor 22 detects acceleration occurring in each of three axial directions.

The processor 23 executes an observation program 241 stored in the storage unit 24 to control the acceleration sensor 22, generate observation data 242 based on the acceleration detected by the acceleration sensor 22, and convert the generated observation data 242 into Store in the storage unit 24 . In this embodiment, the observation data 242 is acceleration data A _s (k).

The communication unit 21 transmits observation data 242 stored in the storage unit 24 to the measuring device 1 under the control of the processor 23 .

As shown in FIG. 33, the measuring device 1 includes a first communication section 11, a second communication section 12, a processor 13, and a storage section .

The first communication unit 11 receives observation data 242 from the sensor 2 and outputs the received observation data 242 to the processor 13 . As described above, the observed data 242 is the acceleration data A _s (k).

The storage unit 14 is a memory that stores programs, data, and the like for the processor 13 to perform calculation processing and control processing. The storage unit 14 also stores various programs, data, and the like for the processor 13 to implement predetermined application functions. Also, the processor 13 may receive various programs, data, etc. via the communication network 4 and store them in the storage unit 14 .

The processor 13 acquires the observation data 242 received by the first communication unit 11 and stores it in the storage unit 14 as the observation data 142 . Then, the processor 13 generates measurement data 143 based on the observation data 142 stored in the storage unit 14 and causes the storage unit 14 to store the generated measurement data 143 . In this embodiment, the measurement data 143 is the measurement data U'(k).

In this embodiment, the processor 13 executes the measurement program 141 stored in the storage unit 14 to generate a target data generation unit 131, a low-pass filter processing unit 132, a high-pass filter processing unit 133, a correction data estimation unit 134, a vibration It functions as a component data generation unit 135 , a measurement data generation unit 136 and a measurement data output unit 137 . That is, the processor 13 includes a target data generation unit 131, a low-pass filter processing unit 132, a high-pass filter processing unit 133, a correction data estimation unit 134, a vibration component data generation unit 135, a measurement data generation unit 136, and a measurement data output unit 137. .

The target data generation unit 131 reads the observation data 142 stored in the storage unit 14 and generates target data U(k) based on the acceleration data A _s (k) which is the observation data 142 . Specifically, the target data generation unit 131 generates the target data U(k) by performing the calculations of the aforementioned formulas (19) and (20). That is, the target data generation unit 131 performs the processing of the target data generation step S1 in FIG.

The low-pass filter processing unit 132 performs low-pass filter processing on the target data U(k) including the drift noise and the vibration component generated by the target data generation unit ₁₃₁ to reduce the vibration component. (k) is generated. That is, the low-pass filter processing unit 132 performs the low-pass filter processing step S2 in FIG.

The high-pass filter processing unit 133 performs high-pass filter processing on the displacement data M _s (k) generated by the low-pass filter processing unit 132 to reduce the drift noise as shown in the above equation (25). to generate displacement data MU(k). That is, the high-pass filter processing unit 133 performs the processing of the high-pass filter processing step S3 in FIG.

Based on the displacement data MU(k) generated by the high-pass filter processing unit 133, the correction data estimation unit 134 calculates the difference between the displacement data M _s (k) from which drift noise has been removed and the displacement data MU(k). Generate corresponding correction data M _CC (k). Correction data estimating section 134 performs the calculations of formulas (26) to (33) above to generate correction data M _CC (k).

Specifically, first, the correction data estimation unit 134 calculates the first peak p ₁ =(k ₁ , mu ₁ ) and the second peak p ₂ =(k ₂ , mu ₂ ) of the displacement data MU(k). and the first section T1 before the _first peak p1, the second section T2 between the _first peak p1 and the _second peak p2, and the third section T3 after the _second peak p2. Identify. That is, the correction data estimating unit 134 performs the processing of the section identifying step S41 in FIG.

Next, the correction data estimator 134 inverts the sign of the displacement data MU(k) in the first section T1 to obtain the first section correction data M _CC1 (k) as in the above equation (27). Generate. That is, the correction data estimation unit 134 performs the process of the first section correction data generation step S42 in FIG.

Next, the correction data estimator 134 converts the displacement data MU(k) before the first peak p1 to the _first peak p before a predetermined time in the second section T2, as in the above equation (32). _{Linear data −2L C ( _} k) is added, and after the predetermined time in the second interval T2, the displacement data MU(k) after the _second peak p2 are rearranged in reverse order to before the _{second peak p2 Data MU(2k 2 −k} ) and the straight line data −2L _C (k) to generate the second section correction data M _CC2 (k). That is, the correction data estimation unit 134 performs the process of the second section correction data generation step S43 in FIG.

Next, the correction data estimator 134 inverts the sign of the displacement data MU(k) in the third section T3 to obtain the third section correction data M _CC3 (k) as in the above equation (28). Generate. That is, the correction data estimation unit 134 performs the process of the third segment correction data generation step S44 in FIG.

Finally, the correction data estimating unit 134 calculates the first section correction data M _CC1 (k), the second section correction data M _CC2 (k), and the third section correction data M _CC3 as shown in Equation (26) above. (k) to generate correction data M _CC (k). That is, the correction data estimator 134 performs the correction data generation step S45 in FIG.

In this manner, the correction data estimating section 134 performs the processing of the correction data estimating step S4 in FIG. 31, specifically, the processing of steps S41 to S45 in FIG.

The vibration component data generator 135 converts the displacement data M s (k) generated by the low-pass filter processor 132 from the target data U(k) generated by the target data generator 131 to the displacement data M _s (k) generated by the target data generator 131, as shown in Equation (24) above. Vibration component data U _OSC (k) containing the vibration component is generated by subtraction. That is, the vibration component data generation unit 135 performs the vibration component data generation step S5 in FIG. 31 .

The measurement data generation unit 136 extracts the displacement data MU(k) generated by the high-pass filter processing unit 133 and the correction data M _CC (k) and the vibration component data U _OSC (k) generated by the vibration component data generation unit 135 are added to generate measurement data U′(k). That is, the measurement data generation unit 136 performs the measurement data generation step S6 in FIG. 31 . The measurement data U′ generated by the measurement data generation unit 136 is stored in the storage unit 14 as the measurement data 143 .

The measurement data output unit 137 reads the measurement data 143 stored in the storage unit 14 and outputs the measurement data 143 to the monitoring device 3 . Then, the second communication unit 12 transmits the measurement data 143 stored in the storage unit 14 to the monitoring device 3 via the communication network 4 under the control of the measurement data output unit 137 . That is, the measurement data output unit 137 performs the measurement data output step S7 in FIG.

Thus, the measurement program 141 is a program that causes the measurement device 1, which is a computer, to execute each procedure of the flowchart shown in FIG.

As shown in FIG. 33 , the monitoring device 3 includes a communication section 31 , a processor 32 , a display section 33 , an operation section 34 and a storage section 35 .

The communication unit 31 receives measurement data 143 from the measurement device 1 and outputs the received measurement data 143 to the processor 32 . As described above, the measurement data 143 is the measurement data U'(k).

The display unit 33 displays various information under the control of the processor 32 . The display unit 33 may be, for example, a liquid crystal display or an organic EL display. EL is an abbreviation for Electro Luminescence.

The operation unit 34 outputs operation data corresponding to user's operation to the processor 32 . The operation unit 34 may be, for example, an input device such as a mouse, keyboard, or microphone.

The storage unit 35 is a memory that stores various programs, data, and the like for the processor 32 to perform calculation processing and control processing. The storage unit 35 also stores programs, data, and the like for the processor 32 to implement predetermined application functions.

The processor 32 acquires the measurement data 143 received by the communication unit 31, evaluates the temporal change in the displacement of the superstructure 7 based on the acquired measurement data 143, generates evaluation information, and uses the generated evaluation information. Displayed on the display unit 33 .

In this embodiment, the processor 32 functions as a measurement data acquisition section 321 and a monitoring section 322 by executing a monitoring program 351 stored in the storage section 35 . That is, the processor 32 includes a measurement data acquisition section 321 and a monitoring section 322 .

The measurement data acquisition unit 321 acquires the measurement data 143 received by the communication unit 31 and adds the acquired measurement data 143 to the measurement data string 352 stored in the storage unit 35 .

The monitoring unit 322 statistically evaluates temporal changes in displacement of the superstructure 7 based on the measurement data string 352 stored in the storage unit 35 . Then, the monitoring unit 322 generates evaluation information indicating the evaluation result, and causes the display unit 33 to display the generated evaluation information. A user can monitor the state of the superstructure 7 based on the evaluation information displayed on the display unit 33 .

The monitoring unit 322 may perform processing such as monitoring of the railcar 6 and abnormality determination of the superstructure 7 based on the measurement data string 352 stored in the storage unit 35 .

In addition, the processor 32 transmits information for adjusting the operating conditions of the measuring device 1 and the sensor 2 to the measuring device 1 via the communication section 31 based on the operation data output from the operating section 34 . The operation status of the measuring device 1 is adjusted based on the information received via the second communication unit 12 . In addition, the measuring device 1 transmits information for adjusting the operation status of the sensor 2 received via the second communication section 12 to the sensor 2 via the first communication section 11 . The operating conditions of the sensor 2 are adjusted based on the information received via the communication unit 21 .

In the processors 13, 23, and 32, for example, the function of each section may be realized by separate hardware, or the function of each section may be realized by integrated hardware. For example, the processors 13, 23, 32 may include hardware, which may include circuitry for processing digital signals and/or circuitry for processing analog signals. The processors 13, 23, 32 may be CPUs, GPUs, DSPs, or the like. CPU is an abbreviation for Central Processing Unit, GPU is an abbreviation for Graphics Processing Unit, and DSP is an abbreviation for Digital Signal Processor. Further, the processors 13, 23, and 32 may be configured as custom ICs such as ASICs to realize the functions of the respective units, or the functions of the respective units may be realized by the CPU and ASIC. ASIC is an abbreviation for Application Specific Integrated Circuit, and IC is an abbreviation for Integrated Circuit.

The storage units 14, 24, and 35 are configured by, for example, various IC memories such as ROM, flash ROM, and RAM, and recording media such as hard disks and memory cards. ROM is an abbreviation for Read Only Memory, RAM is an abbreviation for Random Access Memory, and IC is an abbreviation for Integrated Circuit. The storage units 14, 24, and 35 include nonvolatile information storage devices that are computer-readable devices or media, and various programs, data, and the like may be stored in the information storage devices. The information storage device may be an optical disk such as an optical disk DVD or a CD, a hard disk drive, or various types of memory such as a card type memory or a ROM.

Although only one sensor 2 is illustrated in FIG. 33 , a plurality of sensors 2 may each generate observation data 242 and transmit it to the measuring device 1 . In this case, the measuring device 1 receives a plurality of observation data 242 transmitted from a plurality of sensors 2 , generates a plurality of measurement data 143 , and transmits the generated measurement data 143 to the monitoring device 3 . The monitoring device 3 also receives a plurality of measurement data 143 transmitted from the measuring device 1 and monitors the states of the plurality of superstructures 7 based on the received plurality of measurement data 143 .

1-6. Effect In the measurement method of the first embodiment described above, the measurement device 1 uses the target data U(k) to be processed to reduce the vibration component displacement data M _s (k) and the vibration component is generated, displacement data _MU (k) is generated by reducing drift noise from the displacement data M _s (k), and correction data M is generated based on the displacement data MU(k). Estimate _CC (k). Since the vibration component of the displacement data MU(k) has been reduced, highly accurate estimated correction data M _CC (k) can be obtained. The corrected data M _CC (k) corresponds to the difference between the displacement data M _s (k) from which the drift noise has been removed and the displacement data MU(k). contains ingredients. Therefore, according to the measurement method of the first embodiment, the measurement device 1 adds the displacement data MU(k), the correction data M _CC (k), and the vibration component data U _OSC (k) to obtain the target data It is possible to generate measurement data U'(k) in which drift noise is reduced with respect to U(k). Further, according to the measurement method of the first embodiment, the measurement apparatus 1 uses the target data U(k) to be processed, the displacement data MU(k), the correction data M _CC (k), and the vibration component data. U _OSC (k), and adding the displacement data MU(k), the correction data M _CC (k), and the vibration component data U _OSC (k), information for reducing drift noise is obtained in advance. Measurement data U'(k) with reduced drift noise can be generated without preparation. Therefore, by using the measurement method of the first embodiment, accurate measurement data U'(k) can be obtained regardless of changes in the environment, and cost can be reduced.

In particular, according to the measurement method of the first embodiment, the measurement device 1 performs the first section based on the characteristics of the displacement data MU(k) obtained by reducing the drift noise and vibration components with respect to the target data U(k). T1, the second section T2 and the third section T3 are specified, and appropriate first section correction data M _CC1 (k), second section correction data M _CC2 (k) and third section correction data M _CC3 (k) are obtained. Therefore, it is possible to improve the estimation accuracy of the correction data M _CC (k) generated by adding these.

Further, according to the measurement method of the first embodiment, the measurement apparatus 1 performs moving average processing on the target data U(k) at the cycle _Tf corresponding to the fundamental frequency _Ff , so that the required amount of calculation is small. However, since the attenuation of the signal component of the fundamental frequency F _f and its harmonic components is very large, the displacement data M _s (k) in which the vibration component is effectively reduced can be obtained. can be eliminated to improve the estimation accuracy of the correction data M _CC (k). Alternatively, the measuring apparatus 1 generates displacement data M _s (k) by performing FIR filter processing for attenuating signal components of frequencies equal to or higher than the fundamental frequency F _f of the target data U(k), thereby obtaining moving average Although the amount of calculation becomes larger than the processing, since all signal components with frequencies equal to or higher than the fundamental frequency F _f can be attenuated, the influence of the vibration components equal to or higher than the fundamental frequency F _f can be eliminated to eliminate the correction data M _CC (k). can improve the estimation accuracy of

Further, according to the measurement method of the first embodiment, the measurement apparatus 1 converts the displacement data M _s (k) from the displacement data M _s (k) to the moving average of the displacement data M _s (k) as a high-pass filter process for the displacement data M s (k). The high-pass filter process can be easily performed by performing the process of subtracting the processed or FIR filtered data. Furthermore, since the group delay of each signal component included in the displacement data M _s (k) is constant in moving average processing or FIR filter processing, correction data M _CC (k) can be accurately estimated.

In addition, in the measurement method of the first embodiment, the target data U(k) to be processed is data of the displacement of the superstructure 7 caused by the railway vehicle 6 moving on the superstructure 7 of the bridge 5 . Therefore, according to the measurement method of the first embodiment, the measurement device 1 generates the measurement data U′(k), which is the displacement data of the upper structure 7 due to the movement of the railway vehicle 6, with reduced drift noise. , the displacement of the superstructure 7 of the bridge 5 can be measured with high accuracy.

Further, according to the measurement method of the first embodiment, the measurement device 1 integrates twice the acceleration in the direction intersecting the plane of the upper structure 7 detected by the sensor 2 installed on the upper structure 7, and processes the acceleration. Since certain target data U(k) is generated, the displacement of the upper structure 7 can be measured with high accuracy.

In addition, in the measurement method of the first embodiment, since the frequency of the drift noise included in the target data U(k) is lower than the minimum value of the natural vibration frequency of the upper structure 7, the low-pass noise for the target data U(k) The cut-off frequency of filtering and high-pass filtering can be set higher than the frequency of the drift noise of the superstructure 7 and lower than the minimum value of the natural vibration frequency. Therefore, according to the measurement method of the first embodiment, in the generated measurement data U′(k), the drift noise is reduced without reducing the signal component of the natural vibration frequency of the upper structure 7 and its harmonic components. can be made

In addition, in the measurement method of the first embodiment, the target data U(k) to be processed includes data of a convex waveform in the positive or negative direction, such as a rectangular waveform, a trapezoidal waveform, or a half-sine waveform. , the measuring apparatus 1 can generate more appropriate correction data M _CC (k) based on these waveform features, so that the estimation accuracy of the generated correction data M _CC (k) can be improved. .

2. Second Embodiment Hereinafter, in the second embodiment, the same components as those in the first embodiment are denoted by the same reference numerals, and descriptions overlapping with those in the first embodiment are omitted or simplified. Different contents are explained.

In the measuring method of the first embodiment, the measuring device 1 adds the first section correction data M _CC1 (k), the second section correction data M _CC2 (k), and the third section correction data M _CC3 (k). to generate correction data M _CC (k), and add displacement data MU(k), correction data M _CC (k), and vibration component data U _OSC (k) to generate measurement data U′(k). . On the other hand, the measurement data _{U ′} ( k) always becomes the vibration component data U _OSC (k) in the first section T1 and the third section T3. Therefore, in the measurement method of the second embodiment, the measuring apparatus 1 does not generate the first section correction data M _CC1 (k) and the third section correction data M _CC3 (k), and corrects the correction data in the second section T2. Generate M _CC2 (k). Then, as shown in equation (40), the measuring device 1 calculates the vibration component data U _OSC (k) in the section of k≦k1 that is the _first section T1 and the section of k ₂ ≦k that is the third section T3. Then, the displacement data MU(k), the correction data M _CC2 (k), and the vibration component data U _OSC (k) are added in the second section T2, i.e., k ₁ <k<k ₂ , to obtain the measurement data U ' Generate (k).

In the above equation (38), the corrected data M _CC (k) matches the second segment corrected data M _CC2 (k) in the interval k ₁ <k<k ₂ , so the calculation result of equation (40) is , agrees with the calculation result by equation (38).

FIG. 34 is a flow chart showing an example of the procedure of the measurement method according to the second embodiment for measuring the displacement of the superstructure 7 of the bridge 5. As shown in FIG. In this embodiment, the measuring device 1 executes the procedure shown in FIG.

As shown in FIG. 34, first, in a target data generation step S110, the measuring device 1 acquires acceleration data A _s (k), which is observation data, and generates target data U(k). Specifically, the measuring device 1 generates the target data U(k) by performing the calculations of the above equations (19) and (20). The processing of the target data generation step S110 is the same as the processing of the target data generation step S1 in FIG.

Next, in the low-pass filter processing step S120, the measuring device 1 performs low-pass filter processing on the target data U(k) including the drift noise and the vibration component generated in the step S110, and converts the vibration component into reduced vibration component data. to generate displacement data M _s (k) of . The processing of the low-pass filter processing step S120 is the same as the processing of the low-pass filter processing step S2 of FIG.

Next, in the high-pass filtering step S130, the measuring device 1 performs high-pass filtering on the displacement data M _s (k) containing the drift noise generated in the step S120 to obtain the drift noise is generated as drift noise reduction data MU(k). The processing of the high-pass filtering step S130 is the same as the processing of the high-pass filtering step S3 in FIG.

Next, in the section specifying step S140, the measuring device 1 sets the first peak p ₁ =(k ₁ , mu ₁ ) and the second peak p ₂ =(k ₂ , mu ₂ ), the first section T1 before the _first peak p1, the second section T2 between the _first peak p1 and the _second peak p2, and the third section T2 after the _second peak p2 Interval T3 is specified. That is, the _first section T1 is a section of k≦k1, the _second section T2 is a section of k1< _k <k2, and the _third section T3 is a section of k2≦k. The processing of the section identifying step S140 is the same as the processing of the section identifying step S41 in FIG.

Next, in the correction data estimation step S150, the measuring device 1 removes the drift noise from the displacement data M _s (k) in the second interval T2 based on the displacement data MU(k) generated in the step S130. and the displacement data _MU (k). Specifically, the measuring device 1 performs the calculations of the above-described formulas (29) to (32) to generate the correction data M _CC2 (k).

In addition, in the vibration component data generation step S160, the measuring device 1 generates the displacement data M _s (k) generated in step S120 from the target data U(k) generated in step S110, as in the above equation (24). is subtracted to generate the vibration component data U _OSC (k) containing the vibration component. The processing of the vibration component data generation step S160 is the same as the processing of the vibration component data generation step S5 of FIG.

Next, in the measurement data generating step S170, the measuring device 1 sets the first section T1 to the vibration component data U _OSC (k) generated in the step S160, and the second section T2 as shown in the above equation (40). , the displacement data MU(k) generated in step S130, the correction data M _CC2 (k) generated in step S150, and the vibration component data U _OSC (k) are added to obtain the vibration component data U _OSC (k), the measurement data U'(k) is generated.

Next, in a measurement data output step S<b>180 , the measurement device 1 outputs the measurement data U′(k) generated in step S<b>170 to the monitoring device 3 . Specifically, the measuring device 1 transmits the measurement data U′(k) to the monitoring device 3 via the communication network 4 . The processing of the measurement data output step S180 is the same as the processing of the measurement data output step S7 in FIG.

Then, in step S190, the measuring device 1 repeats the processes of steps S110 to S180 until the measurement of the displacement of the superstructure 7 of the bridge 5 is completed.

FIG. 35 is a flow chart diagram showing an example of the procedure of the correction data estimation step S150 of FIG.

As shown in FIG. 35, in step S151, the measuring device ₁ , as in the above equation (32), before a predetermined time in the second section T2, the displacement data MU(k ) is rearranged in reverse order from the first peak p ₁ onwards, and the straight line L _C (k) passing through the _first peak p ₁ and the second peak p ₂ is multiplied by -2 Data obtained by adding the linear data −2L _C (k) and rearranging the displacement data MU(k) after the second peak p ₂ in reverse order to before the second peak p ₂ after a predetermined time in the second section T2 MU(2k ₂ −k) and the straight line data −2L _C (k) are added to generate correction data M _CC2 (k).

FIG. 36 is a diagram showing a configuration example of the measuring device 1 according to the second embodiment. As shown in FIG. 36, the measuring device 1 according to the second embodiment includes a first communication unit 11, a second communication unit 12, a processor 13, and a storage unit 14, as in the first embodiment. there is The functions of the first communication unit 11, the second communication unit 12, and the storage unit 14 are the same as those of the first embodiment, and thus description thereof will be omitted.

In this embodiment, the processor 13 executes the measurement program 141 stored in the storage unit 14 to generate a target data generation unit 131, a low-pass filter processing unit 132, a high-pass filter processing unit 133, a correction data estimation unit 134, a vibration It functions as a component data generation unit 135 , a measurement data generation unit 136 , a measurement data output unit 137 and a section identification unit 138 . That is, the processor 13 includes a target data generation unit 131, a low-pass filter processing unit 132, a high-pass filter processing unit 133, a correction data estimation unit 134, a vibration component data generation unit 135, a measurement data generation unit 136, a measurement data output unit 137, and an interval A specifying part 138 is included.

The functions of the target data generation unit 131, the low-pass filter processing unit 132, the high-pass filter processing unit 133, the vibration component data generation unit 135, and the measurement data output unit 137 are the same as those in the first embodiment, so description thereof will be omitted. Note that the target data generation unit 131 performs the target data generation step S110 in FIG. 34 . Also, the low-pass filter processing unit 132 performs the processing of the low-pass filter processing step S120 in FIG. Also, the high-pass filter processing unit 133 performs the processing of the high-pass filter processing step S130 in FIG. Further, the vibration component data generation unit 135 performs the vibration component data generation step S160 in FIG. 34 . Also, the measurement data output unit 137 performs the processing of the measurement data output step S180 in FIG.

The section specifying unit 138 calculates the first peak p ₁ =(k ₁ , mu ₁ ) and the second peak p ₂ =(k ₂ , mu ₂ ) of the displacement data MU(k) generated by the high-pass filtering unit 133. and the first section T1 before the _first peak p1, the second section T2 between the _first peak p1 and the _second peak p2, and the third section T3 after the _second peak p2. Identify. That is, the section identification unit 138 performs the section identification step S140 in FIG.

Based on the displacement data MU(k) generated by the high-pass filter processing unit 133, the correction data estimating unit 134 extracts data obtained by removing drift noise from the displacement data M _s (k) and the displacement data MU( Correction data M _CC2 (k) corresponding to the difference from k) is generated. The correction data estimator 134 performs the calculations of formulas (29) to (32) above to generate correction data M _CC2 (k). Specifically, the correction data estimating unit 134 converts the displacement data MU(k) before the _first peak p1 to the first Linear data −2L obtained by multiplying data MU (2k ₁ −k) rearranged in reverse order after peak p ₁ and straight line L _C (k) passing through first peak p ₁ and second peak p ₂ by −2 _{C (} k) is added, and data MU ₍ 2k ₂ −k) and the straight line data −2L _C (k) to generate correction data M _CC2 (k).

In this manner, the correction data estimation unit 134 performs the processing of the correction data estimation step S150 in FIG. 34, specifically, the processing of step S151 in FIG.

The measurement data generation unit 136 sets the vibration component data U OSC (k) generated by the vibration component data generation unit 135 to the first section T1 as in the above equation (40), and applies the high-pass filter U _OSC (k) to the second section T2. The displacement data MU(k) generated by the processing unit 133, the correction data M _CC2 (k) generated by the correction data estimating unit 134, and the vibration component data U _OSC (k) are added, and the third section T3 is obtained as vibration component data Measured data U′(k) is generated as U _OSC (k). That is, the measurement data generation unit 136 performs the processing of the measurement data generation step S170 in FIG. The measurement data U′(k) generated by the measurement data generation unit 136 is stored in the storage unit 14 as the measurement data 143 .

Thus, the measurement program 141 is a program that causes the measurement device 1, which is a computer, to execute each procedure of the flowchart shown in FIG.

In the measurement method of the second embodiment described above, the measurement apparatus 1 uses the target data U(k) to be processed, and includes the displacement data M _s (k) obtained by reducing the vibration component and the vibration component. Generate vibration component data U _OSC (k). Then, the measuring apparatus 1 generates displacement data MU(k) in which drift noise is reduced from the displacement data M _s (k), and based on the characteristics of the displacement data MU(k), the first section T1 and the second section T2 and the third interval T3 are identified, and correction data M _CC (k) is estimated in the second interval T2. The correction data M _CC (k) corresponds to the difference between the displacement data M _s (k) from which the drift noise has been removed and the displacement data MU(k) in the second section T2, so it is removed by high-pass filtering. contains significant signal components. Therefore, according to the measurement method of the second embodiment, the measurement device 1 sets the vibration component data U _OSC (k) for the first section T1 and the third section T3, and the displacement data MU(k) for the second section T2. and correction data M _CC (k) and vibration component data U _OSC (k) to generate measurement data U′(k) in which drift noise is reduced with respect to target data U(k) can be done. Further, according to the measurement method of the second embodiment, the measurement apparatus 1 uses the target data U(k) to be processed, the displacement data MU(k), the correction data M _CC2 (k), and the vibration component data. U _OSC (k) is generated, and the displacement data MU(k), the correction data M _CC2 (k), and the vibration component data U _OSC (k) are added in the second section T2 to reduce the drift noise. Measurement data U′(k) with reduced drift noise can be generated without preparing information for the above in advance. Therefore, by using the measurement method of the second embodiment, accurate measurement data U'(k) can be obtained regardless of changes in the environment, and cost can be reduced.

In addition, according to the measurement method of the second embodiment, the measurement apparatus 1 generates the measurement data U′(k) in the first section T1 and the third section T3 using the correction data M _CC1 (k), Since generation of M _CC3 (k) and addition of displacement data MU (k), correction data M _CC1 (k), M _CC3 (k), and vibration component data U _OSC (k) are unnecessary, the amount of calculation is reduced. reduced.

In particular, according to the measurement method of the second embodiment, the measurement apparatus 1 calculates the second Since more appropriate correction data M _CC2 (k) can be generated in section T2, the estimation accuracy of the generated correction data M _CC2 (k) can be improved.

In addition, according to the measurement method of the second embodiment, the same effects as those of the measurement method of the first embodiment can be obtained.

3. Modifications The present invention is not limited to this embodiment, and various modifications are possible within the scope of the present invention.

In each of the above embodiments, the observation device is the sensor 2 that outputs the acceleration data A _s (k), and the target data is the target data U(k) obtained by integrating the acceleration data A _s (k) twice. However, the observation equipment and target data are not limited to this. For example, the observation device is a contact-type displacement gauge, a ring-type displacement gauge, a laser displacement gauge, a pressure-sensitive sensor, a displacement measurement device by image processing, or a displacement measurement device by optical fiber, and the target data is any of these observation devices. may be observation data of A contact-type displacement gauge, a ring-type displacement gauge, a laser displacement gauge, a displacement-measuring device using image processing, and a displacement-measuring device using an optical fiber measure the displacement of the observation point R due to the running of the railway vehicle 6 . The pressure-sensitive sensor detects changes in stress at the observation point R due to running of the railroad vehicle 6 . Further, for example, the observation device may be a speed sensor, and the target data may be data obtained by integrating the speed detected by the speed sensor. According to these measuring methods, the measuring device 1 can accurately measure the displacement of the superstructure 7 using the displacement, stress change, or velocity data.

As an example, FIG. 37 shows a configuration example of a measurement system 10 using a ring-type displacement gauge as an observation device. Further, FIG. 38 shows a configuration example of a measurement system 10 using a displacement measurement device using image processing as an observation device. In FIGS. 37 and 38, the same components as those in FIG. 1 are denoted by the same reference numerals, and description thereof will be omitted. In the measurement system 10 shown in FIG. 37, the piano wire 41 is fixed between the upper surface of the ring-type displacement gauge 40 and the lower surface of the main girder G directly above it, and the ring-type displacement gauge 40 measures the deflection of the superstructure 7. is measured, and the measured object data U(k) is transmitted to the measuring device 1 . The measurement device 1 generates measurement data U′(k) by removing drift noise from the target data U(k) transmitted from the ring-type displacement gauge 40 . Moreover, in the measurement system 10 shown in FIG. 38 , the camera 50 transmits an image of the target 51 provided on the side surface of the main girder G to the measurement device 1 . The measurement device 1 processes the image transmitted from the camera 50, calculates the displacement of the target 51 due to the deflection of the superstructure 7, generates the target data U(k), and calculates the drift from the generated target data U(k). Measured data U'(k) with noise removed is generated. In the example of FIG. 38, the measuring device 1 generates target data U(k) as a displacement measuring device by image processing. (k) may be generated.

In each of the above-described embodiments, the bridge 5 is a railway bridge and the moving object that moves over the bridge 5 is the railway vehicle 6. However, the bridge 5 is a road bridge, and the moving object that moves over the bridge 5 is an automobile. It may be a vehicle such as a streetcar, a construction vehicle, or the like. FIG. 39 shows a configuration example of the measurement system 10 when the bridge 5 is a road bridge and the vehicle 6a moves on the bridge 5. As shown in FIG. In FIG. 39, the same reference numerals are assigned to the same components as in FIG. As shown in FIG. 39, a bridge 5, which is a road bridge, consists of an upper structure 7 and a lower structure 8, like a railway bridge. FIG. 40 is a cross-sectional view of the upper structure 7 taken along line AA of FIG. As shown in FIGS. 39 and 40, the superstructure 7 includes a floor plate F, a main girder G, a bridge floor 7a composed of cross girders (not shown), and bearings 7b. Further, as shown in FIG. 39, the substructure 8 includes a bridge pier 8a and an abutment 8b. The superstructure 7 is a structure spanning any one of adjacent abutments 8b and piers 8a, two adjacent abutments 8b, or two adjacent piers 8a. Both ends of the superstructure 7 are located at adjacent abutments 8b and piers 8a, at two adjacent abutments 8b, or at two adjacent piers 8a. The bridge 5 is, for example, a steel bridge, a girder bridge, an RC bridge, or the like.

Each sensor 2 is installed in the longitudinal central portion of the superstructure 7, specifically, in the longitudinal central portion of the main girder G. As shown in FIG. However, each sensor 2 only needs to be able to detect the acceleration for calculating the displacement of the upper structure 7 , and its installation position is not limited to the central portion of the upper structure 7 . If the sensors 2 are provided on the floor plate F of the superstructure 7, they may be destroyed by the running of the vehicle 6a, and local deformation of the bridge floor 7a may affect the measurement accuracy. And in the example of FIG. 40 , each sensor 2 is provided on the main girder G of the superstructure 7 .

As shown in FIG. 40, the superstructure 7 has two lanes L ₁ and L ₂ and three main girders G in which a vehicle 6a, which is a moving object, can move. In the example of FIGS. 39 and 40 , sensors 2 are provided on each of the two main girders at both ends in the central portion of the superstructure 7 in the longitudinal direction, and the lane L ₁ located vertically above one of the sensors 2 . An observation point R ₁ is provided on the surface of the other sensor 2 , and an observation point R ₂ is provided on the surface of the lane L ₂ vertically above the other sensor 2 . That is, the two sensors 2 are observation devices that observe the observation points R ₁ and R ₂ respectively. The two sensors 2 for observing the observation points R ₁ and R ₂ may be provided at positions where they can detect the acceleration generated at the observation points R ₁ and R ₂ due to the running of the vehicle 6 _a . , _R2 . Note that the number of sensors 2, installation positions, and the number of lanes are not limited to the examples shown in FIGS. 39 and 40, and various modifications are possible.

The measuring device 1 calculates the deflection displacement of the lanes L ₁ and L ₂ due to the traveling of the vehicle 6a based on the acceleration data output from each sensor 2, and communicates the information of the displacement of the lanes L ₁ and L ₂ . It transmits to the monitoring device 3 via the network 4 . The monitoring device 3 may store the information in a storage device (not shown), and perform processes such as monitoring the vehicle 6a and determining abnormality of the upper structure 7 based on the information.

In each of the above embodiments, each sensor 2 is provided on the main girder G of the superstructure 7, but is provided on the surface or inside of the superstructure 7, the lower surface of the floor plate F, the pier 8a, and the like. good too. Also, in each of the above embodiments, the superstructure of a bridge was taken as an example of the structure, but the structure is not limited to this, and the structure may be any structure as long as it is deformed by the movement of the moving body.

A rail vehicle or vehicle passing over a bridge is a heavy vehicle that can be scaled by BWIM. BWIM is an abbreviation for Bridge Weigh in Motion, and is a technique for measuring the weight, number of axles, etc., of railroad vehicles or vehicles passing over a bridge by measuring the deformation of the bridge using the bridge as a scale. A bridge superstructure that can analyze the weight of a running railway vehicle or vehicle from responses such as deformation and strain is the structure on which BWIM functions, and the physical process between the action and response to the bridge superstructure. makes it possible to measure the weight of the vehicle on which the BWIM system is applied.

The above-described embodiments and modifications are examples, and the present invention is not limited to these. For example, it is also possible to appropriately combine each embodiment and each modification.

The present invention includes configurations that are substantially the same as the configurations described in the embodiments, for example, configurations that have the same function, method and result, or configurations that have the same purpose and effect. Moreover, the present invention includes configurations obtained by replacing non-essential portions of the configurations described in the embodiments. In addition, the present invention includes a configuration that achieves the same effects or achieves the same purpose as the configurations described in the embodiments. In addition, the present invention includes configurations obtained by adding known techniques to the configurations described in the embodiments.

The following content is derived from the embodiment and modifications described above.

One aspect of the measurement method is
a low-pass filtering step of low-pass filtering target data including drift noise and vibration components to generate vibration component reduction data in which the vibration components are reduced;
a high-pass filtering step of performing high-pass filtering on the vibration component-reduced data to generate drift noise-reduced data in which the drift noise is reduced;
a correction data estimation step of estimating correction data corresponding to a difference between data obtained by removing the drift noise from the vibration component reduction data and the drift noise reduction data, based on the drift noise reduction data;
a vibration component data generation step of generating vibration component data including the vibration component by subtracting the vibration component reduction data from the target data;
and a measurement data generation step of adding the drift noise reduction data, the correction data, and the vibration component data to generate measurement data.

In this measurement method, the target data to be processed is used to generate vibration component reduction data in which the vibration component is reduced and vibration component data including the vibration component, and drift noise reduction in which the drift noise is reduced from the vibration component reduction data. Generating data and estimating correction data based on the drift noise reduction data. Since the vibration component is reduced in the drift noise reduction data, correction data estimated with high accuracy can be obtained. Since the corrected data corresponds to the difference between the data obtained by removing the drift noise from the reduced vibration component data and the reduced drift noise data, it contains significant signal components that have been removed by high-pass filtering. Therefore, according to this measurement method, by adding the drift noise reduction data, the correction data, and the vibration component data, it is possible to generate measurement data in which the drift noise is reduced with respect to the target data. Further, according to this measurement method, the target data to be processed is used to generate the drift noise reduction data, the correction data, and the vibration component data, and the drift noise reduction data, the correction data, and the vibration component data are added. Thus, measurement data with reduced drift noise can be generated without preparing information for reducing drift noise in advance. Therefore, by using this measurement method, accurate measurement data can be obtained regardless of changes in the environment, and cost can be reduced.

In one aspect of the measurement method,
The correction data estimation step includes:
Calculate the first peak and the second peak of the drift noise reduction data, the first section before the first peak, the second section between the first peak and the second peak, and the second peak a section identifying step of identifying a subsequent third section;
a first section correction data generation step of inverting the sign of the drift noise reduction data in the first section to generate first section correction data;
A straight line passing through data obtained by rearranging the drift noise reduction data before the first peak in reverse order after the first peak before a predetermined time in the second section, and the first peak and the second peak. is added with linear data obtained by multiplying -2, and after the predetermined time in the second section, the drift noise reduction data after the second peak are rearranged in reverse order to before the second peak. , the straight line data, and a second section correction data generating step of generating second section correction data;
a third section correction data generation step of inverting the sign of the drift noise reduction data in the third section to generate third section correction data;
and a correction data generation step of adding the first section correction data, the second section correction data, and the third section correction data to generate the correction data.

According to this measurement method, three sections are specified based on the characteristics of the drift noise reduction data obtained by reducing drift noise and vibration components for the target data, and more appropriate correction data is generated in each section. is possible, it is possible to improve the estimation accuracy of the generated correction data.

Another aspect of the measurement method is
a low-pass filtering step of low-pass filtering target data including drift noise and vibration components to generate vibration component reduction data in which the vibration components are reduced;
a high-pass filtering step of performing high-pass filtering on the vibration component-reduced data to generate drift noise-reduced data in which the drift noise is reduced;
Calculate the first peak and the second peak of the drift noise reduction data, the first section before the first peak, the second section between the first peak and the second peak, and the second peak a section identifying step of identifying a subsequent third section;
a correction data estimating step of estimating correction data corresponding to a difference between the data obtained by removing the drift noise from the vibration component reduction data and the drift noise reduction data in the second section based on the drift noise reduction data; ,
a vibration component data generation step of generating vibration component data including the vibration component by subtracting the vibration component reduction data from the target data;
Measurement is performed using the first section as the vibration component reduction data, the drift noise reduction data, the correction data, and the vibration component data in the second section, and the third section as the vibration component reduction data. and a measurement data generating step for generating data.

In this measurement method, the target data to be processed is used to generate vibration component reduction data in which the vibration component is reduced and vibration component data including the vibration component, and drift noise reduction in which the drift noise is reduced from the vibration component reduction data. Data is generated, three intervals are identified based on characteristics of the drift noise reduction data, and correction data is estimated in the second interval. Since the vibration component is reduced in the drift noise reduction data, correction data estimated with high accuracy in the second interval can be obtained. In the second interval, the corrected data corresponds to the difference between the data obtained by removing the drift noise from the reduced vibration component data and the reduced drift noise data, so it contains significant signal components that have been removed by the high-pass filtering process. . Therefore, according to this measurement method, the first section and the third section are used as the vibration component data, and the drift noise reduction data, the correction data, and the vibration component data are added in the second section. Measurement data with reduced drift noise can be generated. Further, according to this measurement method, the drift noise reduction data, the correction data, and the vibration component data are generated using the target data to be processed, and the drift noise reduction data, the correction data, and the vibration component data are generated in the second interval. By adding , it is possible to generate measurement data with reduced drift noise without preparing information for reducing drift noise in advance. Therefore, by using this measurement method, accurate measurement data can be obtained regardless of changes in the environment, and cost can be reduced.

Further, according to this measurement method, generation of correction data and addition of drift noise reduction data, correction data, and vibration component data are unnecessary in the first and third sections in order to generate measurement data. , the computational complexity is reduced.

In one aspect of the measurement method,
In the correction data estimation step,
A straight line passing through data obtained by rearranging the drift noise reduction data before the first peak in reverse order after the first peak before a predetermined time in the second section, and the first peak and the second peak. is added with linear data obtained by multiplying -2, and after the predetermined time in the second section, the drift noise reduction data after the second peak are rearranged in reverse order to before the second peak. , and the straight line data may be added to generate the correction data.

According to this measurement method, it is possible to generate more appropriate correction data in the second section based on the characteristics of the drift noise reduction data obtained by reducing the drift noise and vibration components with respect to the target data. It is possible to improve the estimation accuracy of the correction data to be applied.

In one aspect of the measurement method,
In the low-pass filtering step,
The target data may be subjected to fast Fourier transform processing to calculate a fundamental frequency, and as the low-pass filter processing, moving average processing may be performed on the target data at a period corresponding to the fundamental frequency to generate the vibration component reduction data. .

In this measurement method, the moving average process not only requires a small amount of calculation, but also has a very large attenuation of the signal component of the fundamental frequency and its harmonic components, so the vibration component is effectively reduced. Reduction data are obtained. Therefore, according to this measurement method, it is possible to eliminate the influence of the vibration component and improve the estimation accuracy of the correction data.

In one aspect of the measurement method,
In the low-pass filtering step,
Fast Fourier transform processing is performed on the target data to calculate a fundamental frequency, and as the low-pass filter processing, FIR filter processing for attenuating signal components of frequencies equal to or higher than the fundamental frequency is performed on the target data to reduce the vibration component. data may be generated.

In this measurement method, the FIR filter process requires a larger amount of calculation than the moving average process, but can attenuate all signal components of frequencies equal to or higher than the fundamental frequency. Therefore, according to this measurement method, it is possible to eliminate the influence of the vibration component having the fundamental frequency or more and improve the estimation accuracy of the correction data.

In one aspect of the measurement method,
The high-pass filtering process may be a process of subtracting, from the vibration component reduction data, data obtained by subjecting the vibration component reduction data to moving average processing or FIR filter processing.

According to this measurement method, the high-pass filter process can be easily performed, and since the group delay of each signal component included in the vibration component reduction data is constant in the moving average process or the FIR filter process, the correction data can be obtained with accuracy. can be estimated well.

In one aspect of the measurement method,
The target data may be data of displacement of the structure caused by a moving body that moves the structure.

According to this measurement method, since the displacement data of the structure caused by the movement of the moving body is obtained as the measurement data with the drift noise reduced, the displacement of the structure can be accurately measured.

In one aspect of the measurement method,
The target data may be data obtained by twice integrating acceleration in a direction intersecting the surface of the structure on which the moving object moves.

According to this measurement method, the displacement of the structure can be accurately measured using the output data of the acceleration sensor installed on the structure.

In one aspect of the measurement method,
The target data is observation data of a contact-type displacement gauge, a ring-type displacement gauge, a laser displacement gauge, a pressure-sensitive sensor, a displacement-measuring device using image processing or a displacement-measuring device using an optical fiber, or data obtained by integrating velocity detected by a velocity sensor. may be

According to this measuring method, it is possible to accurately measure the displacement of a structure using data on displacement, stress change, or velocity.

In one aspect of the measurement method,
The structure may be a superstructure of a bridge.

According to this measurement method, the displacement of the superstructure of the bridge can be measured with high accuracy.

In one aspect of the measurement method,
A frequency of the drift noise may be lower than a minimum natural vibration frequency of the superstructure.

According to this measurement method, the cutoff frequencies of the low-pass filtering and high-pass filtering are set higher than the drift noise frequency of the upper structure and lower than the minimum natural vibration frequency. Drift noise can be reduced in the displacement data without reducing the signal component of the natural vibration frequency of the upper structure and its harmonic components.

In one aspect of the measurement method,
The mobile object may be a vehicle or a railroad vehicle.

According to this measurement method, it is possible to accurately measure the displacement of the structure caused by the movement of the vehicle or railway vehicle.

In one aspect of the measurement method,
The target data may include data of a convex waveform in the positive or negative direction.

According to this measurement method, it is possible to generate more appropriate correction data based on the characteristics of the waveform that is convex in the positive direction or the negative direction, so it is possible to increase the estimation accuracy of the generated correction data.

In one aspect of the measurement method,
The waveform may be a rectangular waveform, a trapezoidal waveform or a half-sine waveform.

According to this measurement method, it is possible to generate more appropriate correction data based on the characteristics of the rectangular waveform, the trapezoidal waveform, or the half-sine waveform, so it is possible to improve the estimation accuracy of the generated correction data.

One aspect of the measuring device is
a low-pass filter processing unit that performs low-pass filtering on target data containing drift noise and vibration components to generate vibration component reduction data in which the vibration components are reduced;
a high-pass filter processing unit that performs high-pass filtering on the vibration component reduction data to generate drift noise reduction data in which the drift noise is reduced;
a correction data estimation unit for estimating correction data corresponding to a difference between data obtained by removing the drift noise from the vibration component reduction data and the drift noise reduction data, based on the drift noise reduction data;
a vibration component data generation unit that generates vibration component data including the vibration component by subtracting the vibration component reduction data from the target data;
a measurement data generation unit that adds the drift noise reduction data, the correction data, and the vibration component data to generate measurement data.

This measuring device generates vibration component reduction data in which vibration components are reduced and vibration component data including vibration components using target data to be processed, and drift noise reduction in which drift noise is reduced from the vibration component reduction data. Generating data and estimating correction data based on the drift noise reduction data. Since the vibration component is reduced in the drift noise reduction data, correction data estimated with high accuracy can be obtained. Since the corrected data corresponds to the difference between the data obtained by removing the drift noise from the reduced vibration component data and the reduced drift noise data, it contains significant signal components that have been removed by high-pass filtering. Therefore, according to this measuring device, by adding the drift noise reduction data, the correction data, and the vibration component data, it is possible to generate the measurement data in which the drift noise is reduced with respect to the target data. Further, according to this measuring device, the target data to be processed is used to generate the drift noise reduction data, the correction data, and the vibration component data, and the drift noise reduction data, the correction data, and the vibration component data are added. Thus, measurement data with reduced drift noise can be generated without preparing information for reducing drift noise in advance. Therefore, by using this measuring device, accurate measurement data can be obtained regardless of changes in the environment, and cost can be reduced.

One aspect of the measurement system is
An aspect of the measuring device;
and an observation device that observes the observation point,
The target data is data based on observation data by the observation device.

One aspect of the measurement program is
a low-pass filtering step of low-pass filtering target data including drift noise and vibration components to generate vibration component reduction data in which the vibration components are reduced;
a high-pass filtering step of performing high-pass filtering on the vibration component-reduced data to generate drift noise-reduced data in which the drift noise is reduced;
a correction data estimation step of estimating correction data corresponding to a difference between data obtained by removing the drift noise from the vibration component reduction data and the drift noise reduction data, based on the drift noise reduction data;
a vibration component data generation step of generating vibration component data including the vibration component by subtracting the vibration component reduction data from the target data;
and a measurement data generation step of adding the drift noise reduction data, the correction data, and the vibration component data to generate measurement data.

In this measurement program, the target data to be processed is used to generate vibration component reduction data in which the vibration component is reduced and vibration component data including the vibration component, and drift noise reduction in which the drift noise is reduced from the vibration component reduction data. Generating data and estimating correction data based on the drift noise reduction data. Since the vibration component is reduced in the drift noise reduction data, correction data estimated with high accuracy can be obtained. Since the corrected data corresponds to the difference between the data obtained by removing the drift noise from the reduced vibration component data and the reduced drift noise data, it contains significant signal components that have been removed by high-pass filtering. Therefore, according to this measurement program, by adding the drift noise reduction data, the correction data, and the vibration component data, it is possible to generate the measurement data in which the drift noise is reduced with respect to the target data. Further, according to this measurement program, the target data to be processed is used to generate the drift noise reduction data, the correction data, and the vibration component data, and the drift noise reduction data, the correction data, and the vibration component data are added. Thus, measurement data with reduced drift noise can be generated without preparing information for reducing drift noise in advance. Therefore, by using this measurement program, accurate measurement data can be obtained regardless of changes in the environment, and cost can be reduced.

DESCRIPTION OF SYMBOLS 1... Measuring device, 2... Sensor, 3... Monitoring device, 4... Communication network, 5... Bridge, 6... Rail vehicle, 6a... Vehicle, 7... Superstructure, 7a... Bridge floor, 7b... Bearing, 7c... Rail, 7d... sleeper, 7e... ballast, F... floor plate, G... main girder, 8... lower structure, 8a... bridge pier, 8b... abutment, 10... measurement system, 11... first communication unit, 12... second communication unit, 13 ... processor, 14 ... storage unit, 21 ... communication unit, 22 ... acceleration sensor, 23 ... processor, 24 ... storage unit, 31 ... communication unit, 32 ... processor, 33 ... display unit, 34 ... operation unit, 35 ... storage unit , 40... Ring type displacement meter 41... Piano wire 50... Camera 51... Target 131... Target data generator 132... Low pass filter processor 133... High pass filter processor 134... Correction data estimator 135 Vibration component data generation unit 136 Measurement data generation unit 137 Measurement data output unit 138 Section identification unit 141 Measurement program 142 Observation data 143 Measurement data 241 Observation program 242 Observation Data 321...Measurement data acquisition unit 322...Monitoring unit 351...Monitoring program 352...Measurement data string

Claims (18)

a low-pass filtering step of low-pass filtering target data including drift noise and vibration components to generate vibration component reduction data in which the vibration components are reduced;
a high-pass filtering step of performing high-pass filtering on the vibration component-reduced data to generate drift noise-reduced data in which the drift noise is reduced;
a correction data estimation step of estimating correction data corresponding to a difference between data obtained by removing the drift noise from the vibration component reduction data and the drift noise reduction data, based on the drift noise reduction data;
a vibration component data generation step of generating vibration component data including the vibration component by subtracting the vibration component reduction data from the target data;
a measurement data generation step of adding the drift noise reduction data, the correction data, and the vibration component data to generate measurement data. In claim 1,
The correction data estimation step includes:
Calculate the first peak and the second peak of the drift noise reduction data, the first section before the first peak, the second section between the first peak and the second peak, and the second peak a section identifying step of identifying a subsequent third section;
a first section correction data generation step of inverting the sign of the drift noise reduction data in the first section to generate first section correction data;
A straight line passing through data obtained by rearranging the drift noise reduction data before the first peak in reverse order after the first peak before a predetermined time in the second section, and the first peak and the second peak. is added with linear data obtained by multiplying -2, and after the predetermined time in the second section, the drift noise reduction data after the second peak are rearranged in reverse order to before the second peak. , the straight line data, and a second section correction data generating step of generating second section correction data;
a third section correction data generation step of inverting the sign of the drift noise reduction data in the third section to generate third section correction data;
and a correction data generation step of adding the first section correction data, the second section correction data, and the third section correction data to generate the correction data. a low-pass filtering step of low-pass filtering target data including drift noise and vibration components to generate vibration component reduction data in which the vibration components are reduced;
a high-pass filtering step of performing high-pass filtering on the vibration component-reduced data to generate drift noise-reduced data in which the drift noise is reduced;
Calculate the first peak and the second peak of the drift noise reduction data, the first section before the first peak, the second section between the first peak and the second peak, and the second peak a section identifying step of identifying a subsequent third section;
a correction data estimating step of estimating correction data corresponding to a difference between the data obtained by removing the drift noise from the vibration component reduction data and the drift noise reduction data in the second section based on the drift noise reduction data; ,
a vibration component data generation step of generating vibration component data including the vibration component by subtracting the vibration component reduction data from the target data;
Measurement is performed using the first section as the vibration component reduction data, the drift noise reduction data, the correction data, and the vibration component data in the second section, and the third section as the vibration component reduction data. a measurement data generation step of generating data. In claim 3,
In the correction data estimation step,
A straight line passing through data obtained by rearranging the drift noise reduction data before the first peak in reverse order after the first peak before a predetermined time in the second section, and the first peak and the second peak. is added with linear data obtained by multiplying -2, and after the predetermined time in the second section, the drift noise reduction data after the second peak are rearranged in reverse order to before the second peak. , and the straight line data to generate the correction data. In any one of claims 1 to 4,
In the low-pass filtering step,
A measurement method comprising performing fast Fourier transform processing on the target data to calculate a fundamental frequency, and performing moving average processing on the target data at a cycle corresponding to the fundamental frequency as the low-pass filter processing to generate the vibration component reduction data. . In any one of claims 1 to 4,
In the low-pass filtering step,
Fast Fourier transform processing is performed on the target data to calculate a fundamental frequency, and as the low-pass filter processing, FIR filter processing for attenuating signal components of frequencies equal to or higher than the fundamental frequency is performed on the target data to reduce the vibration component. A measurement method that produces data. In any one of claims 1 to 6,
The measurement method, wherein the high-pass filtering process is a process of subtracting, from the vibration component reduction data, data obtained by subjecting the vibration component reduction data to moving average processing or FIR filter processing. In any one of claims 1 to 7,
The measurement method, wherein the target data is data of displacement of the structure caused by a moving body that moves the structure. In claim 8,
The measurement method, wherein the target data is data obtained by twice integrating acceleration in a direction intersecting the surface of the structure on which the moving object moves. In claim 8,
The target data is observation data of a contact-type displacement gauge, a ring-type displacement gauge, a laser displacement gauge, a pressure-sensitive sensor, a displacement-measuring device using image processing or a displacement-measuring device using an optical fiber, or data obtained by integrating velocity detected by a velocity sensor. is the measurement method. In any one of claims 8 to 10,
The measurement method, wherein the structure is a superstructure of a bridge. In claim 11,
The measurement method, wherein the frequency of the drift noise is lower than the minimum value of the natural vibration frequency of the upper structure. In any one of claims 8 to 12,
The measuring method, wherein the moving body is a vehicle or a railroad vehicle. In any one of claims 1 to 13,
The measurement method, wherein the target data includes data of a waveform convex in a positive direction or a negative direction. In claim 14,
The measuring method, wherein the waveform is a rectangular waveform, a trapezoidal waveform, or a half-sine waveform. a low-pass filter processing unit that performs low-pass filtering on target data containing drift noise and vibration components to generate vibration component reduction data in which the vibration components are reduced;
a high-pass filter processing unit that performs high-pass filtering on the vibration component reduction data to generate drift noise reduction data in which the drift noise is reduced;
a correction data estimation unit for estimating correction data corresponding to a difference between data obtained by removing the drift noise from the vibration component reduction data and the drift noise reduction data, based on the drift noise reduction data;
a vibration component data generation unit that generates vibration component data including the vibration component by subtracting the vibration component reduction data from the target data;
a measurement data generation unit that generates measurement data by adding the drift noise reduction data, the correction data, and the vibration component data. A measuring device according to claim 16;
and an observation device that observes the observation point,
The measurement system, wherein the target data is data based on observation data obtained by the observation device. a low-pass filtering step of low-pass filtering target data including drift noise and vibration components to generate vibration component reduction data in which the vibration components are reduced;
a high-pass filtering step of performing high-pass filtering on the vibration component-reduced data to generate drift noise-reduced data in which the drift noise is reduced;
a correction data estimation step of estimating correction data corresponding to a difference between data obtained by removing the drift noise from the vibration component reduction data and the drift noise reduction data, based on the drift noise reduction data;
a vibration component data generation step of generating vibration component data including the vibration component by subtracting the vibration component reduction data from the target data;
A measurement program for causing a computer to execute a measurement data generation step of adding the drift noise reduction data, the correction data, and the vibration component data to generate measurement data.

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