Measurement method, measurement device, measurement system, and measurement program

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

[Technical field]

[0001] The present invention relates to a measurement method, a measurement device, a measurement system, and a measurement program. [Background technology]

[0002] Patent Document 1 describes a displacement acquisition device that includes a static component memory unit that stores a time series of static components, which are components independent of the movement of a bridge girder, of the time series of displacement of the bridge girder caused by the passage of a railway vehicle; a displacement detection unit that detects a time series of displacement of the girder of the bridge to be measured based on at least one of acceleration measurement values ​​or speed measurement values ​​of the girder of the bridge to be measured caused by the passage of the railway vehicle to be measured; a dynamic component extraction unit that extracts a time series of dynamic components, which are the remaining components after removing static components that may contain errors, from the time series of displacement detected by the displacement detection unit; a static component acquisition unit that acquires the time series of static components from the static component memory unit; and a synthesis unit that synthesizes the time series of dynamic components extracted by the dynamic component extraction unit and the time series of static components acquired by the static component acquisition unit.

[0003] 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 girder displacement and replaced with stored static components, thereby making it possible to obtain a time series of displacement free of errors. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2009-237805 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, in the displacement acquisition device described in Patent Document 1, the accuracy of the obtained displacement time series is greatly affected by the approximation between the static components included in the detected girder displacement time series and the stored static components, so if the accuracy of the approximation is insufficient, the accuracy of the displacement time series may decrease. In addition, in the displacement acquisition device described in Patent Document 1, if the static components included in the displacement time series at the time of measurement change due to environmental changes or the like, there is no means for recognizing the deviation between the static components and the stored static components, and it is not possible to know that there is a problem with the displacement accuracy. In addition, in the displacement acquisition device described in Patent Document 1, data on static components for each classification of railway vehicles and each classification of bridges must be stored, and the data must be acquired and updated, which makes the configuration complicated and makes it difficult to reduce costs. Therefore, a method for reducing errors without preparing information for reducing errors, such as static component data, is desired. [Means for solving the problem]

[0006] One aspect of the measurement method according to the present invention is to a low-pass filter processing step of low-pass filtering target data including drift noise and vibration components to generate vibration component reduced data in which the vibration components are reduced; a high-pass filter processing step of processing the vibration component reduced data through a high-pass filter to generate drift noise reduced data in which the drift noise is reduced; a correction data estimating step of estimating correction data corresponding to a difference between the drift noise reduced data and data obtained by removing the drift noise from the vibration component reduced data, based on the drift noise reduced data; a vibration component data generating step of generating vibration component data including the vibration component by subtracting the vibration component reduced data from the target data; and generating measurement data by adding the drift noise reduced data, the correction data, and the vibration component data.

[0007] Another aspect of the measurement method according to the present invention is to a low-pass filter processing step of low-pass filtering target data including drift noise and vibration components to generate vibration component reduced data in which the vibration components are reduced; a high-pass filter processing step of processing the vibration component reduced data through a high-pass filter to generate drift noise reduced data in which the drift noise is reduced; a section specifying step of calculating a first peak and a second peak of the drift noise reduced data and specifying a first section before the first peak, a second section between the first peak and the second peak, and a third section after the second peak; a correction data estimating step of estimating correction data corresponding to a difference between the drift noise reduced data and data obtained by removing the drift noise from the vibration component reduced data in the second section based on the drift noise reduced data; a vibration component data generating step of generating vibration component data including the vibration component by subtracting the vibration component reduced data from the target data; The method includes a measurement data generating process for generating measurement data by using the first section as the vibration component reduced data, adding the drift noise reduced data, the correction data and the vibration component data in the second section, and using the third section as the vibration component reduced data.

[0008] One aspect of the measuring device according to the present invention is a low-pass filter processing unit that performs low-pass filter processing on target data including drift noise and vibration components to generate vibration component reduced data in which the vibration components are reduced; a high-pass filter processing unit that generates drift noise reduced data by high-pass filtering the vibration component reduced data to reduce the drift noise; a correction data estimation unit that estimates correction data corresponding to a difference between the drift noise reduced data and data obtained by removing the drift noise from the vibration component reduced data, based on the drift noise reduced data; a vibration component data generating unit that generates vibration component data including the vibration component by subtracting the vibration component reduced data from the target data; and a measurement data generating unit that generates measurement data by adding the drift noise reduced data, the correction data, and the vibration component data.

[0009] One aspect of the measurement system according to the present invention is An embodiment of the measuring device; An observation device for observing an observation point, The target data is data based on observation data obtained by the observation device.

[0010] One aspect of the measurement program according to the present invention is a low-pass filter processing step of low-pass filtering target data including drift noise and vibration components to generate vibration component reduced data in which the vibration components are reduced; a high-pass filter processing step of processing the vibration component reduced data through a high-pass filter to generate drift noise reduced data in which the drift noise is reduced; a correction data estimating step of estimating correction data corresponding to a difference between the drift noise reduced data and data obtained by removing the drift noise from the vibration component reduced data, based on the drift noise reduced data; a vibration component data generating step of generating vibration component data including the vibration component by subtracting the vibration component reduced data from the target data; and generating measurement data by adding the drift noise reduced data, the correction data, and the vibration component data. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a diagram showing an example of the configuration of a measurement system. [Diagram 2] A cross-sectional view of the superstructure in Figure 1 taken along line AA. [Diagram 3] 4 is a diagram illustrating acceleration detected by an acceleration sensor. [Figure 4] A diagram showing the relationship between frequency characteristics F{Md(k)}, F{M(k)}, and F{e(k)}. [Diagram 5] A diagram showing the relationship between frequency characteristics F{Ms(k)}, F{M(k)}, and F{e(k)}. [Figure 6] A diagram showing frequency characteristics F{MV(k)}. [Figure 7] A diagram showing the relationship between frequency characteristics F{Ms(k)}, F{fHP(Ms(k))}, and F{fLP(Ms(k))}. [Figure 8] A graph showing the relationship between frequency characteristics F{Ms'(k)}, F{fHP(Ms(k))}, and F{ALP(fHP(Ms(k)))}. [Figure 9] A diagram showing the relationship between frequency characteristics F{Md'(k)}, F{Ms'(k)}, and F{MV(k)}. [Figure 10] FIG. 4 is a diagram showing data Ms(k) which is a unit pulse waveform. [Figure 11] FIG. 13 is a diagram showing data fLP(Ms(k)) obtained by low-pass filtering data Ms(k). [Figure 12] FIG. 13 is a diagram showing data fHP(Ms(k)) obtained by high-pass filtering data Ms(k). [Figure 13] FIG. 4 is a diagram showing an example of target data U(k). [Figure 14] FIG. 13 is a diagram showing the power spectrum density of target data U(k). [Figure 15] FIG. 4 is a diagram showing an example of displacement data Ms(k). [Figure 16] FIG. 13 is a diagram showing the power spectrum density of displacement data Ms(k). [Figure 17] FIG. 4 is a diagram showing an example of vibration component data UOSC(k). [Figure 18] FIG. 4 is a diagram showing an example of displacement data MU(k). [Figure 19] FIG. 13 is a diagram showing an example of first section correction data MCC1(k) and third section correction data MCC3(k). [Figure 20] FIG. 13 is a diagram showing an example of second section first correction data M1CC2(k). [Figure 21] FIG. 4 is a diagram showing an example of a straight line LC(k). [Figure 22] FIG. 13 is a diagram showing an example of second section second correction data M2CC2(k). [Diagram 23] FIG. 13 is a diagram showing an example of second section correction data MCC2(k). [Figure 24] FIG. 13 is a diagram showing an example of correction data MCC(k). [Diagram 25] FIG. 4 is a diagram showing an example of displacement data RU(k). [Figure 26] FIG. 4 is a diagram showing an example of measurement data U'(k). [Figure 27] FIG. 4 is a diagram showing an example of a displacement waveform UO(k) and drift noise D(k). [Figure 28] FIG. 4 is a diagram showing an example of target data U(k). [Figure 29] FIG. 13 is a diagram showing measurement data U'(k). [Diagram 30] FIG. 13 is a diagram showing measurement data U'(k) and a displacement waveform UO(k) superimposed on each other. [Diagram 31] FIG. 4 is a flowchart showing an example of a procedure of a measurement method according to the first embodiment. [Diagram 32] FIG. 4 is a flowchart showing an example of a procedure of a correction data estimation step in the first embodiment. [Diagram 33] 1A and 1B are diagrams showing configuration examples of a sensor, a measuring device, and a monitoring device. [Diagram 34] FIG. 11 is a flowchart showing an example of a procedure of a measurement method according to a second embodiment. [Diagram 35] FIG. 11 is a flowchart showing an example of a procedure of a correction data estimation step in the second embodiment. [Diagram 36] FIG. 13 is a diagram showing an example of the arrangement of a measurement device according to a second embodiment. [Figure 37] FIG. 13 is a diagram showing another example of the configuration of the measurement system. [Figure 38] FIG. 13 is a diagram showing another example of the configuration of the measurement system. [Figure 39] FIG. 13 is a diagram showing another example of the configuration of the measurement system. [Diagram 40] A cross-sectional view of the superstructure in Figure 39 taken along line AA. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Preferred embodiments of the present invention will be described in detail below with reference to the drawings. Note that the embodiments described below do not unduly limit the contents of the present invention described in the claims. Furthermore, not all of the configurations described below are necessarily essential components of the present invention.

[0013] 1. First embodiment 1-1. Measurement system configuration In the following, a measurement system for implementing the measurement method of this embodiment will be described using an example in which the structure is the superstructure of a bridge and the moving body is a railway vehicle.

[0014] 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. The measurement system 10 may also include a monitoring device 3.

[0015] The bridge 5 is composed of a superstructure 7 and a substructure 8. FIG. 2 is a cross-sectional view of the superstructure 7 taken along line AA in FIG. 1. As shown in FIGS. 1 and 2, the superstructure 7 includes a bridge deck 7a including a deck F, a main girder G, a cross girder (not shown), a support 7b, a rail 7c, a sleeper 7d, and a ballast 7e. As shown in FIG. 1, the substructure 8 includes a pier 8a and an abutment 8b. The superstructure 7 is a structure that is bridged over either one of the adjacent abutments 8b and pier 8a, two adjacent abutments 8b, or two adjacent piers 8a. Both ends of the superstructure 7 are located at the positions of the adjacent abutments 8b and pier 8a, the positions of the two adjacent abutments 8b, or the positions of the two adjacent piers 8a.

[0016] The measurement device 1 and each sensor 2 are connected, for example, by a cable (not shown) and communicate with each other via a communication network such as CAN. CAN is an abbreviation for Controller Area Network. Alternatively, the measurement device 1 and each sensor 2 may communicate with each other via a wireless network.

[0017] For example, each sensor 2 outputs data for calculating the displacement of the superstructure 7 due to the movement of the railcar 6, which is a moving body. In this embodiment, each sensor 2 is an acceleration sensor, and may be, for example, a quartz acceleration sensor or a MEMS acceleration sensor. MEMS is an abbreviation for Micro Electro Mechanical Systems.

[0018] In this embodiment, each sensor 2 is installed in the longitudinal center of the superstructure 7, specifically, in the longitudinal center of the main girder G. However, it is sufficient for each sensor 2 to be able to detect acceleration for calculating the displacement of the superstructure 7, and the installation position is not limited to the central part of the superstructure 7. If each sensor 2 is installed on the deck F of the superstructure 7, there is a risk that it will be destroyed by the running of the railway vehicle 6, and there is also a risk that the measurement accuracy will be affected by local deformation of the bridge deck 7a. Therefore, in the example of Figs. 1 and 2, each sensor 2 is installed on the main girder G of the superstructure 7.

[0019] The floor plates F, main girders G, etc. of the superstructure 7 are deflected in the vertical direction due to the load of the railway vehicle 6 traveling on the superstructure 7. Each sensor 2 detects the acceleration of the deflection of the floor plates F and main girders G due to the load of the railway vehicle 6 traveling on the superstructure 7.

[0020] The measuring device 1 calculates the deflection displacement of the superstructure 7 caused by the running of the railway vehicle 6, based on the acceleration data output from each sensor 2. The measuring device 1 is installed, for example, on the bridge abutment 8b.

[0021] The measuring device 1 and the monitoring device 3 can communicate with each other via a communication network 4, such as a wireless network for mobile phones and the Internet. The measuring device 1 transmits information on the displacement of the superstructure 7 caused by the running of the railway vehicle 6 to the monitoring device 3. The monitoring device 3 stores the information in a storage device (not shown) and may perform processing such as monitoring the railway vehicle 6 and determining whether there is an abnormality in the superstructure 7 based on the information.

[0022] In this embodiment, the bridge 5 is a railway bridge, for example, a steel bridge, a girder bridge, an RC bridge, etc. RC is an abbreviation for Reinforced Concrete.

[0023] As shown in Fig. 2, in this embodiment, an observation point R is set in association with the sensor 2. 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. In other words, 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 where it can detect the acceleration generated at the observation point R due to the traveling of the railway vehicle 6, but it is preferable that the sensor 2 be provided at a position close to the observation point R.

[0024] 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.

[0025] The measurement device 1 acquires acceleration in a direction intersecting with the plane of the superstructure 7 along which the railcar 6 moves, based on the acceleration data output from the sensor 2. The plane of the superstructure 7 along which the railcar 6 moves is defined by the direction along which the railcar 6 moves, i.e., the X direction which is the longitudinal direction of the superstructure 7, and the direction perpendicular to the direction along which the railcar 6 moves, i.e., the Y direction which is the width direction of the superstructure 7. As the railcar 6 travels, the observation point R is deflected in a direction perpendicular to the X and Y directions. Therefore, in order to accurately calculate the magnitude of the acceleration of the deflection, it is desirable for the measurement device 1 to acquire acceleration in a direction perpendicular to the X and Y directions, i.e., the Z direction which is the normal direction of the floor panel F.

[0026] 3 is a diagram illustrating the acceleration detected by the sensor 2. The sensor 2 is an acceleration sensor that detects acceleration occurring in each of three axial directions that are perpendicular to each other.

[0027] In order to detect the acceleration of the deflection at observation point R due to the running of the railway vehicle 6, the sensor 2 is installed so that one of the three detection axes, the x-axis, the y-axis, and the z-axis, intersects with the X-direction and the Y-direction. In Fig. 1 and Fig. 2, the sensor 2 is installed so that the first axis intersects with the X-direction and the Y-direction. Since the observation point R deflects in a direction perpendicular to the X-direction and the Y-direction, in order to accurately detect the acceleration of the deflection, ideally the sensor 2 is installed so that the first axis is aligned with the Z-direction perpendicular to the X-direction and the Y-direction, i.e., the normal direction of the floor panel F.

[0028] However, when the sensor 2 is installed on the superstructure 7, the installation location may be tilted. Even if one of the three detection axes of the sensor 2 is not installed in the normal direction of the floor board F, the error is small and negligible as long as it is roughly oriented in the normal direction. Furthermore, even if one of the three detection axes of the sensor 2 is not installed in the normal direction of the floor board F, the measurement device 1 can correct the detection error due to the tilt of the sensor 2 by using a three-axis composite acceleration obtained by combining the accelerations of the x-axis, y-axis, and z-axis. Furthermore, the sensor 2 may be a one-axis acceleration sensor that detects at least the acceleration occurring in a direction approximately parallel to the vertical direction, or the acceleration in the normal direction of the floor board F.

[0029] In the following, first, the basic concept of the measurement method according to this embodiment executed by the measurement apparatus 1 will be described, and then the details will be described.

[0030] 1-2. Basic concept of measurement method First, the target data to be processed, which is obtained based on the acceleration data output from the sensor 2, is d (k), and the target data M d The target data M(k) contains a significant signal M(k) including a vibration component and a drift noise e(k). d If the number of samples included in (k) is N, then k is an integer between 0 and N-1.

[0031]

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[0032] The drift noise e(k) is not primarily a signal input to the sensor 2, but an error signal generated inside the sensor 2 due to zero-point error, drift due to temperature change, drift due to nonlinear sensitivity, etc. The drift noise e(k) is a long-period fluctuation compared to the signal input to the sensor 2, and its energy is distributed in the low-frequency range. d (k) frequency characteristic F{M d 4 shows the relationship between the frequency characteristic F{M(k)} of the signal M(k) and the frequency characteristic F{e(k)} of the drift noise e(k).

[0033] The vibration components contained in the signal M(k) are, for example, a fundamental frequency signal component and its harmonic components generated by the natural vibration of the bridge 5, and generally have energy distributed in a higher frequency range than the drift noise e(k). Therefore, as shown in formula (2), the target data M d By low-pass filtering (k), the vibration components are reduced to the data M s (k) is obtained.

[0034]

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[0035] The low-pass filter process to reduce the vibration component has a frequency characteristic of F{M d (k)}, the target data M d (k) may be processed by taking a moving average, or may be processed by an FIR filter that attenuates signal components with frequencies equal to or higher than the fundamental frequency. FIR stands for Finite Impulse Response. d The data M obtained by moving average processing of (k) s (k) frequency characteristic F{M s 4 shows the relationship between the frequency characteristic F{M(k)} of the signal M(k) and the frequency characteristic F{e(k)} of the drift noise e(k).

[0036] Also, as shown in equation (3), the target data M d (k) to Data M s By subtracting (k), the data containing the vibration component M V (k) is obtained. Figure 6 shows the data M V The frequency characteristic F{MV(k)} of (k) is shown.

[0037]

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[0038] Data M s (k) is high-pass filtered data HP (M s (k)) and data M s (k) is low-pass filtered data, LP (M s (k)), then data M s (k), Data f HP (M s (k)) and data f LP (M s The relationship between (k) is shown in equation (4).

[0039]

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[0040] Also, Data M s (k) frequency characteristic F{M s (k)}, data f HP (M s (k)) frequency characteristic F{f HP (M s (k))} and data f LP (M s (k)) frequency characteristic F{f LP (M s The relationship between the frequency response F{M s (k)},F{f HP (M s(k))},F{f LP (M s (k))}.

[0041]

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[0042] Since the drift noise e(k) is observed as an offset error, a high-pass filter that attenuates the low-frequency signal is effective for removing the drift noise e(k). s When (k) is subjected to a high-pass filter, the drift noise e(k) with its energy distributed in the low frequency range is sufficiently suppressed, and the data f after the high-pass filter processing is expressed as follows: HP (M s (k)) is the data f(k) obtained by high-pass filtering the signal M(k). HP We assume that it is approximately equal to (M(k)).

[0043]

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[0044] The low-frequency signal components of the signal M(k) are also lost due to the high-pass filter processing. To compensate for this signal component, the data M s Data f obtained by high-pass filtering (k) HP (M s (k)), the signal M(k) is low-pass filtered to obtain data f LP (M(k)). As shown in equation (7), the signal M(k) is low-pass filtered to obtain data f LP (M(k)) is the data M s Data f obtained by high-pass filtering (k) HP (M s (k)), the signal M(k) is low-pass filtered to obtain data f LP Data A used to estimate (M(k)) LP (f HP (M s Assume that (k)) is approximately equal to

[0045]

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[0046] As shown in equation (8), the data M s The data obtained by removing the drift noise e(k) from (k) is the data M s Data f obtained by high-pass filtering (k) HP (M s (k)) and the low-pass filtered data f LP Assuming that (k) is equal to the sum of (M(k)), equation (9) can be obtained from equations (6), (7), and (8).

[0047]

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[0048]

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[0049] From equation (9), the data M s '(k) frequency characteristic F{M s '(k)}, data f HP (M s (k)) frequency characteristic F{f HP (M s (k))} and Data A LP (f HP (M s (k))) frequency characteristic F{A LP (f HP (M s The relationship between the frequency response F{M s '(k)},F{f HP (M s (k))},F{A LP (f HP (M s (k)))} relationship.

[0050]

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[0051] As shown in equation (11), the data M obtained by equation (9) s '(k) and data M containing vibration components V By adding (k) and (s), data M(s) that approximates the signal M(s) is obtained. d '(k) is obtained. d '(k)},F{M s '(k)},F{M V (k)}.

[0052]

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[0053] Data M s By high-pass filtering data f(k), the drift noise e(k) is reduced. HP (M s (k)) is obtained, so this data f HP (M s (k)), the signal M(k) is low-pass filtered to obtain data f LP (M(k)) and estimate the data f HP (M s (k)) and the estimated data and data M V By adding together the drift noise e(k) and the signal M(k), a signal M(k) in which the drift noise e(k) has been reduced can be obtained.

[0054] In the following, data M s For example, if (k) is displacement data, data M s Data f obtained by high-pass filtering (k) HP (M s (k)), the signal M(k) is low-pass filtered to obtain data f LP The procedure for estimating (M(k)) will now be described.

[0055] First, as shown in equation (12), the data M s (k) is assumed to be a simplified unit pulse waveform representing the deflection displacement of the superstructure 7 of the bridge 5 when the railway vehicle 6 passes through. In equation (12), k is an integer equal to or greater than 0. FIG. 10 shows data M s (k) is shown.

[0056]

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[0057] Data M s (k), Data M s Data f obtained by high-pass filtering (k) HP (M s (k)) and the low-pass filtered data f LP (M s The relationship of (k) is assumed to be as shown in equation (13).

[0058]

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[0059] For example, if the low-pass filter process is a moving average process, then equation (14) can be obtained from equation (13). At this time, data k is located in the center of the moving average interval 2p+1.

[0060]

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[0061] In formula (14), p is an integer equal to or greater than 1, and data M s Data f obtained by low-pass filtering (k) LP (M s Since we want to have a flat area in p<(k a -k b ) / 2. In FIG. 11, data M sData f obtained by low-pass filtering (k) using a moving average LP (M s FIG. 12 shows the data M s Data f obtained by high-pass filtering (k) HP (M s (k)) is shown.

[0062] Using Figs. 11 and 12, data M s Data f obtained by high-pass filtering (k) HP (M s (k)) and the low-pass filtered data f LP (M s Compare with (k)).

[0063] As shown in Figure 11, data M s Data f obtained by low-pass filtering (k) LP (M s k in (k) a -p to k a The slope b of the section up to +p is calculated using equation (15).

[0064]

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[0065] Also, data f LP (M s k in (k) b -p to k b The slope of the section up to +p is -b, and k a +p to k b The amplitude B in the section up to -p is -1.

[0066] On the other hand, as shown in FIG. s Data f obtained by high-pass filtering (k) HP (M s k in (k) a -p to k a The slope a of the section up to is calculated by equation (16).

[0067]

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[0068] Also, data f HP (M s k in (k) b From b The slope of the section up to +p is -a, and k=k a The amplitude A of -1 is calculated by equation (17).

[0069]

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[0070] By substituting the above equation (12) into equation (17), the amplitude A is calculated as shown in equation (18).

[0071]

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[0072] From equation (18), if p is sufficiently large, the amplitude A becomes 1 / 2.

[0073] Here, the data M s The unit pulse waveform assumed as (k) in equation (12) does not include drift noise e(k). Therefore, the data M s Data f obtained by low-pass filtering (k) LP (M s (k)) is the data f(k) obtained by low-pass filtering the signal M(k). LP (M(k)). Therefore, the data f HP (M s (k)) and data f LP (M s (k)) is compared with data f HP (M s (k)) and data f LP (M(k)) and data f HP (M sBy measuring the slope a and amplitude A of the signal M(k), the data f LP (M(k)) can be estimated.

[0074] 1-3. Details of the measurement method In reality, the target data U(k), which is the displacement data of the deflection of the superstructure 7 of the bridge 5 when the railway vehicle 6 passes through, includes data of a waveform that is convex in the positive or negative direction, which is different from the unit pulse waveform. d (k) is replaced with the target data U(k), and the signal M(k) is subjected to low-pass filtering based on the estimation method described above to obtain data f LP (M(k)) can be estimated. For example, a waveform that is convex in the positive or negative direction is a rectangular waveform, a trapezoidal waveform, or a half-sine waveform.

[0075] First, the measurement device 1 calculates the acceleration data A output from the acceleration sensor as shown in Equation (19). s Integrate (k) to get the velocity data V s (k) is generated, and then the velocity data V s (k) is integrated to generate target data U(k). In equations (19) and (20), ΔT is the time interval of the data. Figure 13 shows an example of target data U(k).

[0076]

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[0077]

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[0078] Next, the measurement device 1 detects the fundamental frequency F f In order to reduce the vibration components and their harmonics, the target data U(k) is processed through a low-pass filter to obtain the displacement data M s Generate (k).

[0079] Specifically, the measurement device 1 first performs a fast Fourier transform on the target data U(k) to calculate the power spectrum density, and identifies the peak of the power spectrum density as the fundamental frequency F f FIG. 14 shows the power spectrum density obtained by performing a fast Fourier transform on the target data U(k) in FIG. 13. In the example in FIG. 14, the fundamental frequency F f is calculated to be about 3 Hz. Then, the measurement device 1 calculates the fundamental frequency F f From the fundamental period T f Calculate the fundamental period T f The moving average interval k is adjusted to the time resolution of the data by dividing by ΔT. mf Calculate the fundamental period T f is the fundamental frequency F f is the period corresponding to T f >2ΔT.

[0080]

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[0081]

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[0082] Then, the measurement device 1 performs low-pass filtering to obtain the fundamental period T f The target data U(k) is subjected to moving average processing to obtain the displacement data M(k) as vibration component reduced data in which the vibration components contained in the signal M(k) are reduced. s This moving average process not only requires a small amount of calculation, but also generates the fundamental frequency F f The attenuation of the signal component and its harmonic components is very large, so the vibration components are effectively reduced in the displacement data M s (k) is obtained. In FIG. 15, the displacement data M s An example of the displacement data M s15 and 16, the power spectrum density of the displacement data M(k) from which most of the vibration components contained in the target data U(k) have been removed is shown. s (k) is obtained.

[0083]

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[0084] In addition, the measurement device 1 performs low-pass filtering on the target data U(k) by applying a fundamental period T f The displacement data M is processed by FIR filtering to attenuate the signal components with frequencies above s (k) may be generated. FIR stands for Finite Impulse Response. This FIR filter process requires more calculations than the moving average process, but it is possible to generate a fundamental frequency F f All signal components at these frequencies can be attenuated.

[0085] Next, the measurement device 1 calculates displacement data M in which the vibration components are reduced from the target data U(k) by using the equation (24). s (k) is subtracted to obtain the vibration component data U OSC (k) is generated. OSC An example of (k) is shown below.

[0086]

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[0087] In addition, the measurement device 1 calculates the displacement data M s Then, displacement data MU(k) is generated by high-pass filtering (k). An example of the displacement data MU(k) is shown in FIG.

[0088]

number

[0089] Next, the measurement device 1 generates data f by low-pass filtering the signal M(k) based on the displacement data MU(k). LP (M(k)), i.e., the displacement data M s Correction data M, which corresponds to the difference between the data obtained by removing drift noise from (k) and the displacement data MU(k), CC Estimate (k).

[0090] As shown in FIG. 18, in this embodiment, the measurement device 1 specifies a first section T1, a second section T2, and a third section T3 based on the displacement data MU(k), and calculates correction data M CC The measurement device 1 generates the displacement data MU(k) by dividing it into these three sections. In order to identify the first section T1, the second section T2, and the third section T3, the measurement 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 p 1 is the leading peak around the time when the railcar 6 enters the superstructure 7, and the second peak p 2 is the rearmost peak around the time when the railcar 6 leaves the superstructure 7. The first section T1 is the first peak p 1 Previous interval, i.e., k≦k 1 The second section T2 is the section from the first peak p 1 and the second peak p 2 The interval between 1 <k<k 2 The third section T3 is the section of the second peak p 2 The following interval, i.e. k 2 ≦k.

[0091] As shown in equation (26), the correction data M CC (k) is the first section correction data M CC1 (k), and the second section correction data M CC2 (k) and the third section correction data MCC3 It can be calculated as the sum of (k).

[0092]

number

[0093] First section correction data M CC1 (k) is calculated by using the data MU'(k) obtained by inverting the sign of the displacement data MU(k) according to the formula (27). CC3 The first section correction data M(k) is calculated by using the data MU'(k) obtained by inverting the sign of the displacement data MU(k) and the formula (28). CC1 (k) and the third section correction data M CC3 An example of (k) is shown below.

[0094]

number

[0095]

number

[0096] Second section correction data M CC2 (k) is calculated as follows. First, k≦(k 1 +k 2 ) / 2, the first peak p 1 The previous displacement data MU(k) is the first peak p 1 The data sorted in reverse order is then MU(2k 1 In addition, it is after a predetermined time in the second section T2 (k 1 +k 2 ) / 2≦k, the second peak p 2 The subsequent displacement data MU(k) is the second peak p 2 The data previously sorted in reverse order is MU(2k 2 -k), where the given time is k=k 1 +k 2However, it may be a time other than this.

[0097] Then, according to equation (29), the data MU(2k 1 -k) and Data MU(2k 2 -k) to obtain the first correction data M1 for the second section CC2 (k) is obtained. In FIG. 20, the first correction data M1 CC2 An example of (k) is shown below.

[0098]

number

[0099] First peak p 1 =(k 1 ,mu 1 ) and the second peak p2 = (k 2 ,mu 2 ) and the straight line L C (k) is calculated by equation (30). In FIG. C An example of (k) is shown below.

[0100]

number

[0101] According to equation (31), the line L C Linear data -2L obtained by multiplying (k) by -2 C Using (k), the second section second correction data M2 CC2 (k) is obtained. In FIG. 22, the second section second correction data M2 CC2 An example of (k) is shown below.

[0102]

number

[0103] As shown in equation (32), the second section correction data M CC2 (k) is the first correction data M1 for the second section CC2 (k) and the second section second correction data M2CC2 The second section correction data M CC2 An example of (k) is shown below.

[0104]

number

[0105] Correction data M CC (k) can be obtained by substituting equations (27), (28), and (32) into equation (26) as in equation (33). CC An example of (k) is shown below.

[0106]

number

[0107] Then, as shown in equation (34), the displacement data MU(k) and the correction data M CC (k) to obtain displacement data RU(k) in which the vibration component and drift noise have been reduced.

[0108]

number

[0109] By substituting equation (33) into equation (34), equation (35) is obtained.

[0110]

number

[0111] Equation (35) is transformed into equation (36).

[0112]

number

[0113] From equation (36), the displacement data RU(k) is in the first section T1, k≦k 1 k in the second interval T2 2 In the range ≦k, the displacement data RU(k) is 0 and the vibration component and drift noise are removed. Fig. 25 shows an example of the displacement data RU(k).

[0114] Then, as shown in equation (37), the displacement data RU(k) and the vibration component data U OSC 26 shows an example of the measurement data U'(k).

[0115]

number

[0116] By substituting equation (36) into equation (37), equation (38) is obtained.

[0117]

number

[0118] In order to confirm the effect of removing drift noise by the measurement method of this embodiment, a waveform obtained by adding drift noise D(k) to the displacement waveform UO(k) as shown in equation (39) is used as the target data U(k). An example of the displacement waveform UO(k) and drift noise D(k) is shown in Fig. 27. An example of the target data U(k) is shown in Fig. 28.

[0119]

number

[0120] The measurement data U'(k) and the displacement waveform UO(k) obtained by equations (21) to (38) for the target data U(k) are compared. Fig. 29 shows the measurement data U'(k). Fig. 30 shows the measurement data U'(k) and the displacement waveform UO(k) superimposed on each other. As shown in Figs. 29 and 30, it can be confirmed that the measurement method of this embodiment can obtain measurement data U'(k) from which drift noise has been removed and the displacement waveform has been restored.

[0121] 1-4. Measurement method procedure 31 is a flow chart showing an example of the procedure of the measurement method of the first embodiment for measuring the displacement of the superstructure 7 of the bridge 5. In this embodiment, the measurement device 1 executes the procedure shown in FIG.

[0122] As shown in FIG. 31, first, in the target data generation step S1, the measurement device 1 generates acceleration data A s (k) is acquired to generate target data U(k). Therefore, the target data U(k) is the acceleration data A(k) which is the observation data by the sensor 2 which is the observation device. s (k). Specifically, the measurement device 1 generates the target data U(k) by performing calculations of the above-mentioned formulas (19) and (20). In this embodiment, the target data U(k) to be processed is data on the displacement of the superstructure 7, which is a structure, caused by the railway vehicle 6, which is a moving body moving on the superstructure 7, and is data obtained by integrating twice the acceleration in the direction intersecting with the surface of the superstructure 7 on which the railway vehicle 6 moves. Therefore, the target data U(k) includes data on a waveform that is convex in the positive or negative direction, specifically, a rectangular waveform, a trapezoidal waveform, or a sine half-wave waveform. Note that the rectangular waveform includes not only an accurate rectangular waveform but also a waveform that approximates a rectangular waveform. Similarly, the trapezoidal waveform includes not only an accurate trapezoidal waveform but also a waveform that approximates a trapezoidal waveform. Similarly, the sine half-wave waveform includes not only an accurate sine half-wave waveform but also a waveform that approximates a sine half-wave waveform.

[0123] Next, in a low-pass filter processing step S2, the measurement device 1 performs low-pass filter processing on the target data U(k) containing the drift noise and vibration components generated in step S1 to reduce the vibration components, thereby generating displacement data M s For example, the measurement device 1 performs a fast Fourier transform on the target data U(k) to generate a fundamental frequency F f Then, as shown in the above equation (23), the fundamental frequency F f The fundamental period T f Then, the moving average of the target data U(k) is applied to obtain the displacement data M s For example, the measurement device 1 may perform a fast Fourier transform on the target data U(k) to generate a fundamental frequency F f Calculate the fundamental frequency F for the target data U(k) as a low-pass filter. f The displacement data M is processed by FIR filtering to attenuate the signal components with frequencies above s (k) may be generated.

[0124] Next, in the high-pass filter processing step S3, the measurement device 1 performs a high-pass filter process on the displacement data M including drift noise generated in step S2 as shown in the above-mentioned equation (25). s The displacement data M(k) is subjected to high-pass filtering to reduce the drift noise, thereby generating the displacement data M s The high-pass filter process of (k) is performed by applying the displacement data M s From (k), the displacement data M s The low-pass filter processing may be a moving average process or an FIR filter process. s The high-pass filter process of (k) is s From (k), the displacement data M s The data (k) may be subjected to moving average processing or FIR filter processing and then subtracted.

[0125] Next, in a correction data estimation step S4, the measurement device 1 estimates the displacement data M based on the displacement data MU(k) generated in step S3. s Correction data M, which corresponds to the difference between the data obtained by removing drift noise from (k) and the displacement data MU(k), CC Specifically, the measurement device 1 performs the calculations of the above-mentioned formulas (26) to (33) to estimate the correction data M CC Generate (k).

[0126] In addition, in the vibration component data generation step S5, the measurement device 1 calculates the displacement data M generated in step S2 from the target data U(k) generated in step S1 as shown in the above equation (24). s (k) is subtracted to obtain the vibration component data U OSC In this embodiment, 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 minimum value of the natural vibration frequency of the upper structure 7 is, for example, the frequency of the first vibration mode in the longitudinal direction of the upper structure 7. By setting the cutoff frequency of the low-pass filter processing in step S2 and the cutoff frequency of the high-pass filter processing in step S3 to be higher than the frequency of the drift noise of the upper structure 7 and lower than the minimum value of the natural vibration frequency, the vibration component data U(k) generated in step S5 is OSC In (k), drift noise is reduced without reducing the signal component of the natural vibration frequency of the upper structure 7 and its harmonic components. For example, the frequency of the drift noise may be less than 1 Hz, and the cutoff frequency of the low-pass filter processing and the cutoff frequency of the high-pass filter processing may be 1 Hz or more.

[0127] Next, in a measurement data generation process S6, the measurement device 1 calculates the displacement data MU(k) generated in the process S3 and the correction data M CC (k) and the vibration component data U generated in step S5 OSC (k) to generate measurement data U'(k).

[0128] Next, in a measurement data output step S7, the measurement device 1 outputs the measurement data U'(k) generated in step S6 to the monitoring device 3. Specifically, the measurement device 1 transmits the measurement data U'(k) to the monitoring device 3 via the communication network 4.

[0129] 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.

[0130] FIG. 32 is a flowchart showing an example of the procedure of the correction data estimation step S4 of FIG.

[0131] As shown in FIG. 32, first, in the section identification step S41, the measurement device 1 detects the first peak p 1 =(k 1 ,mu 1 ) and the second peak p 2 =(k 2 ,mu 2 ) was calculated, and the first peak p 1 The previous first section T1 and the first peak p 1 and the second peak p 2 A second section T2 between the first peak p 2 The third interval T3 is identified. That is, the first interval T1 is k≦k 1 The second interval T2 is k 1 <k<k 2 The third section T3 is k 2 In this embodiment, the first peak p 1 is the leading peak around the time when the railcar 6 enters the superstructure 7, and the second peak p 2 is the rearmost peak around the time when railcar 6 exits from superstructure 7.

[0132] Next, in the first interval correction data generating step S42, the measurement device 1 inverts the sign of the displacement data MU(k) in the first interval T1 to generate the first interval correction data M CC1 Generate (k).

[0133] Next, in the second interval correction data generation step S43, the measurement device 1 calculates the first peak p 1 The previous displacement data MU(k) is the first peak p 1 Then, the data sorted in reverse order is MU(2k 1 -k) and the first peak p 1 and the second peak p 2 A straight line L passing through C Linear data -2L obtained by multiplying (k) by -2 C (k) is added, and the second peak p 2 The subsequent displacement data MU(k) is the second peak p 2 Previously sorted data in reverse order MU(2k 2 -k) and straight line data-2L C (k) is added to obtain the second section correction data M CC2 Generate (k).

[0134] Next, in the third interval correction data generating step S44, the measurement device 1 inverts the sign of the displacement data MU(k) in the third interval T3 to generate the third interval correction data M CC3 Generate (k).

[0135] Finally, in the correction data generation step S45, the measurement device 1 calculates the first section correction data M generated in step S42 as shown in the above equation (26). CC1 (k) and the second section correction data M generated in step S43 CC2 (k) and the third section correction data M generated in step S44 CC3 (k) is added to obtain the correction data M CC Generate (k).

[0136] 1-5. Configuration of observation, measuring and monitoring equipment FIG. 33 is a diagram showing an example of the configuration of a sensor 2, a measuring device 1, and a monitoring device 3, which are observation devices.

[0137] As shown in FIG. 33, the sensor 2 includes a communication unit 21, an acceleration sensor 22, a processor 23, and a memory unit 24.

[0138] The storage unit 24 is a memory that stores various programs, data, etc. for the processor 23 to perform calculation processing and control processing. The storage unit 24 also stores programs, data, etc. for the processor 23 to realize predetermined application functions.

[0139] The acceleration sensor 22 detects the acceleration occurring in each of the three axial directions.

[0140] 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 store the generated observation data 242 in the storage unit 24. In this embodiment, the observation data 242 is acceleration data A s (k).

[0141] Under the control of the processor 23, the communication unit 21 transmits the observation data 242 stored in the memory unit 24 to the measurement device 1.

[0142] As shown in FIG. 33, the measurement device 1 includes a first communication unit 11, a second communication unit 12, a processor 13, and a storage unit .

[0143] The first communication unit 11 receives the observation data 242 from the sensor 2 and outputs the received observation data 242 to the processor 13. As described above, the observation data 242 includes the acceleration data A s (k).

[0144] The storage unit 14 is a memory that stores programs, data, etc. for the processor 13 to perform calculation processing and control processing. The storage unit 14 also stores various programs, data, etc. for the processor 13 to realize predetermined application functions. The processor 13 may also receive various programs, data, etc. via the communication network 4 and store them in the storage unit 14.

[0145] 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 stores the generated measurement data 143 in the storage unit 14. In this embodiment, the measurement data 143 is measurement data U'(k).

[0146] In this embodiment, the processor 13 executes a measurement program 141 stored in the storage unit 14 to function as a target data generating unit 131, a low-pass filter processing unit 132, a high-pass filter processing unit 133, a correction data estimating unit 134, a vibration component data generating unit 135, a measurement data generating unit 136, and a measurement data output unit 137. That is, the processor 13 includes the target data generating unit 131, the low-pass filter processing unit 132, the high-pass filter processing unit 133, the correction data estimating unit 134, the vibration component data generating unit 135, the measurement data generating unit 136, and the measurement data output unit 137.

[0147] The target data generating unit 131 reads out the observation data 142 stored in the storage unit 14, and converts the acceleration data A s 31. Based on (k), the target data generation unit 131 generates target data U(k). Specifically, the target data generation unit 131 performs the calculations of the above-mentioned equations (19) and (20) to generate the target data U(k). That is, the target data generation unit 131 performs the process of the target data generation step S1 in FIG.

[0148] The low-pass filter processing unit 132 performs low-pass filter processing on the target data U(k) including drift noise and vibration components generated by the target data generating unit 131 to reduce the vibration components, and generates displacement data M s 31. That is, the low-pass filter processing unit 132 performs the process of the low-pass filter processing step S2 in FIG.

[0149] The high-pass filter processing unit 133 filters the displacement data M generated by the low-pass filter processing unit 132 as shown in the above equation (25). s 31. The high-pass filter processing unit 133 performs high-pass filter processing step S3 in FIG. 31 to generate displacement data MU(k) as drift-noise reduced data by reducing drift noise by performing high-pass filter processing on (k).

[0150] The correction data estimation unit 134 estimates the displacement data M based on the displacement data MU(k) generated by the high-pass filter processing unit 133. s Correction data M, which corresponds to the difference between the data obtained by removing drift noise from (k) and the displacement data MU(k), CC The correction data estimation unit 134 performs the calculations of the above-mentioned equations (26) to (33) to generate the correction data M CC Generate (k).

[0151] Specifically, first, the correction data estimation unit 134 estimates the first peak p 1 =(k 1 ,mu 1 ) and the second peak p 2 =(k 2 ,mu 2 ) was calculated, and the first peak p 1 The previous first section T1 and the first peak p 1 and the second peak p 2 A second section T2 between the first peak p 2 32. That is, the correction data estimation unit 134 performs the process of the section specification step S41 in FIG.

[0152] Next, the correction data estimation unit 134 inverts the sign of the displacement data MU(k) in the first section T1 to obtain the first section correction data M CC1 That is, the correction data estimation unit 134 performs the process of the first section correction data generation step S42 in FIG.

[0153] Next, the correction data estimation unit 134 calculates the first peak p 1 The previous displacement data MU(k) is the first peak p 1 Then, the data sorted in reverse order is MU(2k 1 -k) and the first peak p 1 and the second peak p 2 A straight line L passing through C Linear data -2L obtained by multiplying (k) by -2 C (k) is added, and the second peak p 2 The subsequent displacement data MU(k) is the second peak p 2 Previously sorted data in reverse order MU(2k 2 -k) and straight line data-2L C (k) is added to obtain the second section correction data M CC2 That is, the correction data estimation unit 134 performs the process of the second section correction data generation step S43 in FIG.

[0154] Next, the correction data estimation unit 134 inverts the sign of the displacement data MU(k) in the third section T3 to obtain the third section correction data M CC3 That is, the correction data estimation unit 134 performs the process of the third section correction data generation step S44 in FIG.

[0155] Finally, the correction data estimation unit 134 calculates the first section correction data M CC1 (k) and the second section correction data M CC2 (k) and the third section correction data M CC3 (k) is added to obtain the correction data M CCThat is, the correction data estimation unit 134 performs the process of the correction data generation step S45 in FIG.

[0156] In this manner, the correction data estimation unit 134 performs the process of the correction data estimation step S4 in FIG. 31, specifically, the processes of steps S41 to S45 in FIG.

[0157] The vibration component data generating unit 135 converts the displacement data M generated by the low-pass filter processing unit 132 from the object data U(k) generated by the object data generating unit 131 as shown in the above equation (24). s (k) is subtracted to obtain the vibration component data U OSC That is, the vibration component data generating unit 135 performs the process of vibration component data generating step S5 in FIG.

[0158] The measurement data generating unit 136 calculates 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 generated by the vibration component data generating unit 135 OSC 31. The measured data U' generated by the measured data generating unit 136 is stored in the storage unit 14 as measured data 143.

[0159] The measurement data output unit 137 reads out the measurement data 143 stored in the storage unit 14, and outputs the measurement data 143 to the monitoring device 3. Then, under the control of the measurement data output unit 137, 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. That is, the measurement data output unit 137 performs the process of the measurement data output step S7 in FIG.

[0160] In this way, 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.

[0161] As shown in FIG. 33, the monitoring device 3 includes a communication unit 31, a processor 32, a display unit 33, an operation unit 34, and a storage unit 35.

[0162] The communication unit 31 receives the 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).

[0163] The display unit 33 displays various types of 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.

[0164] The operation unit 34 outputs operation data corresponding to an operation by a user to the processor 32. The operation unit 34 may be, for example, an input device such as a mouse, a keyboard, or a microphone.

[0165] The storage unit 35 is a memory that stores various programs, data, etc. for the processor 32 to perform calculation processing and control processing. The storage unit 35 also stores programs, data, etc. for the processor 32 to realize predetermined application functions.

[0166] The processor 32 acquires the measurement data 143 received by the communication unit 31, evaluates the change in displacement of the superstructure 7 over time based on the acquired measurement data 143, generates evaluation information, and displays the generated evaluation information on the display unit 33.

[0167] In this embodiment, the processor 32 functions as a measurement data acquiring unit 321 and a monitoring unit 322 by executing a monitoring program 351 stored in the storage unit 35. That is, the processor 32 includes the measurement data acquiring unit 321 and the monitoring unit 322.

[0168] 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 sequence 352 stored in the storage unit .

[0169] The monitoring unit 322 statistically evaluates the change over time in the displacement of the superstructure 7, based on the measurement data string 352 stored in the memory unit 35. Then, the monitoring unit 322 generates evaluation information indicating the evaluation result, and causes the generated evaluation information to be displayed on the display unit 33. The user can monitor the condition of the superstructure 7, based on the evaluation information displayed on the display unit 33.

[0170] The monitoring unit 322 may perform processes such as monitoring the railway vehicle 6 and determining whether there is an abnormality in the superstructure 7, based on the measurement data sequence 352 stored in the memory unit 35.

[0171] Furthermore, 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 unit 31 based on operation data output from the operation unit 34. The operating conditions of the measuring device 1 are adjusted based on the information received via the second communication unit 12. Furthermore, the measuring device 1 transmits information for adjusting the operating conditions of the sensor 2 received via the second communication unit 12 to the sensor 2 via the first communication unit 11. The operating conditions of the sensor 2 are adjusted based on the information received via the communication unit 21.

[0172] In addition, the processors 13, 23, and 32 may realize the functions of each part by individual hardware, or may realize the functions of each part by integrated hardware. For example, the processors 13, 23, and 32 include hardware, and the hardware may include at least one of a circuit for processing digital signals and a circuit for processing analog signals. The processors 13, 23, and 32 may be a CPU, a GPU, or a DSP. CPU stands for Central Processing Unit, GPU stands for Graphics Processing Unit, and DSP stands for Digital Signal Processor. In addition, the processors 13, 23, and 32 may be configured as a custom IC such as an ASIC to realize the functions of each part, or may realize the functions of each part by a CPU and an ASIC. ASIC stands for Application Specific Integrated Circuit, and IC stands for Integrated Circuit.

[0173] The storage units 14, 24, and 35 are configured by, for example, various IC memories such as ROM, flash ROM, and RAM, hard disks, memory cards, and other recording media. ROM stands for Read Only Memory, RAM stands for Random Access Memory, and IC stands for Integrated Circuit. The storage units 14, 24, and 35 include non-volatile information storage devices that are devices or media that can be read by a computer, and various programs, data, and the like may be stored in the information storage devices. The information storage devices may be optical disks such as optical disks DVD and CDs, hard disk drives, or various memories such as card-type memories and ROMs.

[0174] Although only one sensor 2 is shown in Fig. 33, multiple sensors 2 may each generate observation data 242 and transmit it to the measurement device 1. In this case, the measurement device 1 receives the multiple observation data 242 transmitted from the multiple sensors 2, generates multiple measurement data 143, and transmits it to the monitoring device 3. In addition, the monitoring device 3 receives the multiple measurement data 143 transmitted from the measurement device 1, and monitors the states of the multiple superstructures 7 based on the received multiple measurement data 143.

[0175] 1-6.Effects In the measurement method of the first embodiment described above, the measurement device 1 uses the target data U(k) to be processed to generate displacement data M in which the vibration component has been reduced. s (k) and vibration component data U OSC (k) is generated, and the displacement data M s (k), the drift noise of which is reduced, is generated, and correction data M CC Since the vibration components of the displacement data MU(k) are reduced, the correction data M CC (k) is obtained. Then, the correction data M CC (k) is the displacement data M s Since this corresponds to the difference between the data obtained by removing drift noise from (k) and the displacement data MU(k), it contains significant signal components that have been removed by the high-pass filter process. Therefore, according to the measurement method of the first embodiment, the measurement device 1 calculates the displacement data MU(k) and the correction data M CC (k) and vibration component data U OSC By adding the correction data M(k) to the target data U(k), it is possible to generate measurement data U'(k) in which drift noise has been reduced with respect to the target data U(k). Furthermore, according to the measurement method of the first embodiment, the measurement device 1 uses the target data U(k) to be processed to generate the displacement data MU(k) and the correction data M CC (k) and vibration component data U OSC (k) and the displacement data MU(k) and the correction data M CC (k) and vibration component data U OSCBy adding U'(k) to the measurement data U'(k), it is possible to generate measurement data U'(k) with reduced drift noise without having to prepare information for reducing drift noise in advance. Therefore, by using the measurement method of the first embodiment, it is possible to obtain measurement data U'(k) with high accuracy regardless of changes in the environment, and to reduce costs.

[0176] In particular, according to the measurement method of the first embodiment, the measurement device 1 specifies the first interval T1, the second interval T2, and the third interval T3 based on the characteristics of the displacement data MU(k) in which drift noise and vibration components are reduced with respect to the target data U(k), and generates appropriate first interval correction data M CC1 (k), second section correction data M CC2 (k) and the third section correction data M CC3 (k) can be generated, and the correction data M CC The estimation accuracy of (k) can be improved.

[0177] According to the measurement method of the first embodiment, the measurement device 1 measures the fundamental frequency F f The period T f By performing moving average processing on the target data U(k), not only is the amount of calculation required small, but the fundamental frequency F f Since the attenuation of the signal component and its harmonic components is very large, the displacement data M with the vibration components effectively reduced is obtained. s (k) is obtained, and the effect of the vibration component is eliminated and the correction data M CC Alternatively, the measurement device 1 can estimate the fundamental frequency F(k) of the target data U(k). f The displacement data M is processed by FIR filtering to attenuate the signal components with frequencies above s (k), the amount of calculation required is greater than with moving average processing, but the fundamental frequency F f Since all signal components with frequencies above the fundamental frequency F can be attenuated, f The effect of the above vibration components is eliminated and the correction data M CC The estimation accuracy of (k) can be improved.

[0178] According to the measurement method of the first embodiment, the measurement device 1 obtains the displacement data M s As a high-pass filter process for (k), the displacement data M s From (k), the displacement data M s By performing a process of subtracting data obtained by moving average processing or FIR filter processing from the displacement data M s Since the group delay of each signal component included in (k) is constant, the correction data M CC (k) can be estimated with high accuracy.

[0179] Moreover, in the measurement method of the first embodiment, the target data U(k) to be processed is data on the displacement of the superstructure 7 of the bridge 5 due to the railway vehicle 6 moving on the superstructure 7. Therefore, according to the measurement method of the first embodiment, the measurement device 1 generates measurement data U'(k) in which drift noise has been reduced, which is displacement data of the superstructure 7 due to the movement of the railway vehicle 6, and therefore the displacement of the superstructure 7 of the bridge 5 can be measured with high accuracy.

[0180] Furthermore, according to the measurement method of the first embodiment, the measurement device 1 generates the target data U(k) to be processed by twice integrating the acceleration in a direction intersecting the surface of the superstructure 7 detected by the sensor 2 installed on the superstructure 7, thereby enabling the displacement of the superstructure 7 to be measured with high accuracy.

[0181] Furthermore, in the measurement method of the first embodiment, since the frequency of the drift noise contained in the target data U(k) is lower than the minimum value of the natural vibration frequency of the upper structure 7, the cutoff frequency of the low-pass filter processing and high-pass filter processing for the target data U(k) can be set higher than the frequency of the drift noise of the upper structure 7 and lower than the minimum value of the natural vibration frequency. Therefore, according to the measurement method of the first embodiment, it is possible to reduce the drift noise in the generated measurement data U'(k) without reducing the signal component of the natural vibration frequency of the upper structure 7 and its harmonic components.

[0182] In the measurement method of the first embodiment, the target data U(k) to be processed includes data of a waveform that is convex in the positive or negative direction, such as a rectangular waveform, a trapezoidal waveform, or a half-sine waveform, so that the measurement device 1 can generate more appropriate correction data M based on the characteristics of these waveforms. CC (k) can be generated, so the generated correction data M CC The estimation accuracy of (k) can be improved.

[0183] 2. Second embodiment In the following, the second embodiment will be described mainly with respect to the differences from the first embodiment, with the same reference numerals used for the same components as in the first embodiment, and explanations that overlap with the first embodiment will be omitted or simplified.

[0184] In the measurement method of the first embodiment, the measurement device 1 generates first section correction data M CC1 (k) and the second section correction data M CC2 (k) and the third section correction data M CC3 (k) is added to obtain the correction data M CC (k) is generated, and the displacement data MU(k) and the correction data M CC (k) and vibration component data U OSC (k) to generate the measurement data U'(k). On the other hand, as shown in the above equation (38), the displacement data MU(k) and the correction data M CC (k) and vibration component data U OSCThe measurement data U'(k) obtained by adding the vibration component data U(k) is always the same as the vibration component data U OSC Therefore, in the measurement method of the second embodiment, the measurement device 1 calculates the first section correction data M CC1 (k) and the third section correction data M CC3 (k) is not generated, and the correction data M CC2 Then, the measurement device 1 generates a signal k≦k (k) in the first section T1 as shown in equation (40). 1 k in the interval T1 and the third interval T2 2 In the section ≦k, the vibration component data U OSC (k), where k is the second section T2. 1 <k<k 2 In the section, the displacement data MU(k) and the correction data M CC2 (k) and vibration component data U OSC (k) to generate measurement data U'(k).

[0185]

number

[0186] In the above equation (38), k 1 <k<k 2 In the section, the correction data M CC (k) is the second section correction data M CC2 (k), the calculation result using equation (40) coincides with the calculation result using equation (38).

[0187] 34 is a flow chart showing an example of the procedure of the measurement method of the second embodiment for measuring the displacement of the superstructure 7 of the bridge 5. In this embodiment, the measurement device 1 executes the procedure shown in FIG.

[0188] As shown in FIG. 34, first, in the target data generation step S110, the measurement device 1 generates acceleration data A s(k) and generates target data U(k). Specifically, the measurement device 1 performs the calculations of the above-mentioned formulas (19) and (20) to generate target data U(k). The process of the target data generation step S110 is the same as the process of the target data generation step S1 in FIG.

[0189] Next, in a low-pass filter processing step S120, the measurement device 1 performs low-pass filter processing on the target data U(k) including the drift noise and vibration components generated in step S110 to reduce the vibration components, thereby generating displacement data M s The processing in the low-pass filter processing step S120 is the same as the processing in the low-pass filter processing step S2 in FIG.

[0190] Next, in a high-pass filter processing step S130, the measurement device 1 performs a high-pass filter process on the displacement data M including the drift noise generated in step S120 as shown in the above-mentioned equation (25). s 31. Then, displacement data MU(k) is generated as drift noise reduced data by high-pass filtering (k). The process of high-pass filtering step S130 is the same as the process of high-pass filtering step S3 in FIG.

[0191] Next, in the section identification step S140, the measurement device 1 identifies the first peak p 1 =(k 1 ,mu 1 ) and the second peak p 2 =(k 2 ,mu 2 ) was calculated, and the first peak p 1 The previous first section T1 and the first peak p 1 and the second peak p 2 A second section T2 between the first peak p 2 The third interval T3 is identified. That is, the first interval T1 is k≦k 1 The second interval T2 is k 1 <k<k 2 The third section T3 is k 2The process of the section specification step S140 is the same as the process of the section specification step S41 in FIG.

[0192] Next, in the correction data estimation step S150, the measurement device 1 estimates the displacement data M s Correction data M, which corresponds to the difference between the data obtained by removing drift noise from (k) and the displacement data MU(k), CC2 Specifically, the measurement device 1 performs the calculations of the above-mentioned formulas (29) to (32) to generate the correction data M CC2 Generate (k).

[0193] In addition, in the vibration component data generation step S160, the measurement device 1 calculates the displacement data M generated in step S120 from the object data U(k) generated in step S110 as shown in the above equation (24). s (k) is subtracted to obtain the vibration component data U OSC The processing in vibration component data generating step S160 is the same as the processing in vibration component data generating step S5 in FIG.

[0194] Next, in the measurement data generation step S170, the measurement device 1 generates the vibration component data U OSC (k), and in the second section T2, the displacement data MU(k) generated in step S130 and the correction data M CC2 (k) and vibration component data U OSC (k) is added to the vibration component data U OSC (k), measurement data U'(k) is generated.

[0195] Next, in a measurement data output step S180, the measurement device 1 outputs the measurement data U'(k) generated in step S170 to the monitoring device 3. Specifically, the measurement device 1 transmits the measurement data U'(k) to the monitoring device 3 via the communication network 4. The process of the measurement data output step S180 is the same as the process of the measurement data output step S7 in FIG.

[0196] 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.

[0197] FIG. 35 is a flowchart showing an example of the procedure of the correction data estimation step S150 of FIG.

[0198] As shown in FIG. 35, in step S151, the measurement device 1 calculates the first peak p 1 The previous displacement data MU(k) is the first peak p 1 Then, the data sorted in reverse order is MU(2k 1 -k) and the first peak p 1 and the second peak p 2 A straight line L passing through C Linear data -2L obtained by multiplying (k) by -2 C (k) is added, and the second peak p 2 The subsequent displacement data MU(k) is the second peak p 2 Previously sorted data in reverse order MU(2k 2 -k) and straight line data-2L C (k) is added to obtain the correction data M CC2 Generate (k).

[0199] Fig. 36 is a diagram showing an example of the configuration of the measurement device 1 in the second embodiment. As shown in Fig. 36, the measurement device 1 in the second embodiment includes a first communication unit 11, a second communication unit 12, a processor 13, and a storage unit 14, similar to the first embodiment. The functions of the first communication unit 11, the second communication unit 12, and the storage unit 14 are similar to those in the first embodiment, and therefore description thereof will be omitted.

[0200] In this embodiment, the processor 13 executes a measurement program 141 stored in the storage unit 14 to function as 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 a section specification unit 138. That is, the processor 13 includes the target data generation unit 131, the low-pass filter processing unit 132, the high-pass filter processing unit 133, the correction data estimation unit 134, the vibration component data generation unit 135, the measurement data generation unit 136, the measurement data output unit 137, and the section specification unit 138.

[0201] The functions of the target data generating unit 131, the low-pass filter processing unit 132, the high-pass filter processing unit 133, the vibration component data generating unit 135, and the measurement data output unit 137 are the same as those in the first embodiment, and therefore the description thereof will be omitted. The target data generating unit 131 performs the process of the target data generating step S110 in FIG. 34. The low-pass filter processing unit 132 performs the process of the low-pass filter processing step S120 in FIG. 34. The high-pass filter processing unit 133 performs the process of the high-pass filter processing step S130 in FIG. 34. The vibration component data generating unit 135 performs the process of the vibration component data generating step S160 in FIG. 34. The measurement data output unit 137 performs the process of the measurement data output step S180 in FIG. 34.

[0202] The section identification unit 138 detects the first peak p 1 =(k 1 ,mu 1 ) and the second peak p 2 =(k 2 ,mu 2 ) was calculated, and the first peak p 1 The previous first section T1 and the first peak p 1 and the second peak p 2 A second section T2 between the first peak p 234. That is, the section identification unit 138 performs the process of the section identification step S140 in FIG.

[0203] The correction data estimation unit 134 estimates the displacement data M s Correction data M, which corresponds to the difference between the data obtained by removing drift noise from (k) and the displacement data MU(k), CC2 The correction data estimation unit 134 performs the calculations of the above-mentioned formulas (29) to (32) to generate the correction data M CC2 Specifically, the correction data estimation unit 134 generates the correction data p 1 The previous displacement data MU(k) is the first peak p 1 Then, the data sorted in reverse order is MU(2k 1 -k) and the first peak p 1 and the second peak p 2 A straight line L passing through C Linear data -2L obtained by multiplying (k) by -2 C (k) is added, and the second peak p 2 The subsequent displacement data MU(k) is the second peak p 2 Previously sorted data in reverse order MU(2k 2 -k) and straight line data-2L C (k) is added to obtain the correction data M CC2 Generate (k).

[0204] In this manner, the correction data estimation unit 134 performs the process of the correction data estimation step S150 in FIG. 34, specifically, the process of step S151 in FIG.

[0205] The measurement data generating unit 136 converts the vibration component data U OSC (k), and in the second section T2, the displacement data MU(k) generated by the high-pass filter processing unit 133 and the correction data MCC2 (k) and vibration component data U OSC (k) is added to the vibration component data U OSC 34. The measured data U'(k) generated by the measured data generating unit 136 is stored in the storage unit 14 as measured data 143. That is, the measured data generating unit 136 performs the process of the measured data generating step S170 in FIG.

[0206] In this way, 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.

[0207] In the measurement method of the second embodiment described above, the measurement device 1 generates displacement data M in which the vibration component is reduced, using the target data U(k) to be processed. s (k) and vibration component data U OSC (k). Then, the measurement device 1 generates the displacement data M s (k), a displacement data MU(k) in which drift noise has been reduced is generated, a first interval T1, a second interval T2, and a third interval T3 are specified based on the characteristics of the displacement data MU(k), and correction data M CC (k) is estimated. Correction data M CC (k) is the displacement data M in the second section T2 s Since this corresponds to the difference between the data obtained by removing drift noise from (k) and the displacement data MU(k), it contains significant signal components that have been removed by high-pass filtering. Therefore, according to the measurement method of the second embodiment, the measurement device 1 converts the first section T1 and the third section T3 into vibration component data U OSC (k), and in the second section T2, the displacement data MU(k) and the correction data M CC (k) and vibration component data U OSC By adding the correction data M(k) to the target data U(k), it is possible to generate measurement data U'(k) in which drift noise has been reduced with respect to the target data U(k). Furthermore, according to the measurement method of the second embodiment, the measurement device 1 uses the target data U(k) to be processed to generate the displacement data MU(k) and the correction data MCC2 (k) and vibration component data U OSC (k) is generated, and the displacement data MU(k) and the correction data M CC2 (k) and vibration component data U OSC By adding U′(k) to the measurement data U′(k), it is possible to generate measurement data U′(k) with reduced drift noise without having to prepare information for reducing drift noise in advance. Therefore, by using the measurement method of the second embodiment, it is possible to obtain measurement data U′(k) with high accuracy regardless of changes in the environment, and to reduce costs.

[0208] According to the measurement method of the second embodiment, the measurement device 1 calculates the correction data M CC1 (k),M CC3 (k) and the displacement data MU(k) and correction data M CC1 (k),M CC3 (k) and vibration component data U OSC Since the addition with (k) is not necessary, the amount of calculation is reduced.

[0209] In particular, according to the measurement method of the second embodiment, the measurement device 1 generates more appropriate correction data M in the second section T2 based on the characteristics of the displacement data MU(k) in which drift noise and vibration components are reduced with respect to the target data U(k). CC2 (k) can be generated, so the generated correction data M CC2 The estimation accuracy of (k) can be improved.

[0210] In addition, according to the measurement method of the second embodiment, it is possible to achieve the same effects as the measurement method of the first embodiment.

[0211] 3. Variations The present invention is not limited to the present embodiment, and various modifications are possible within the scope of the present invention.

[0212] In each of the above embodiments, the observation device receives acceleration data A sSensor 2 outputs (k), and the target data is acceleration data A s The target data U(k) is obtained by integrating (k) twice, but the observation device and the target data are not limited to this. For example, the observation device may be a contact type displacement meter, a ring type displacement meter, a laser displacement meter, a pressure sensor, a displacement measuring device using image processing, or a displacement measuring device using optical fiber, and the target data may be observation data of any of these observation devices. The contact type displacement meter, the ring type displacement meter, the laser displacement meter, the displacement measuring device using image processing, or the displacement measuring device using optical fiber measures the displacement of the observation point R due to the running of the railway vehicle 6. The pressure sensor detects the stress change of the observation point R due to the running of the railway vehicle 6. Also, 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 measurement methods, the measurement device 1 can accurately measure the displacement of the superstructure 7 using the data of the displacement, stress change, or speed.

[0213] As an example, FIG. 37 shows a configuration example of the measurement system 10 using a ring-type displacement meter as the observation device. FIG. 38 shows a configuration example of the measurement system 10 using a displacement measuring device by image processing as the observation device. In FIG. 37 and FIG. 38, the same components as those in FIG. 1 are given the same reference numerals, and their description will be omitted. In the measurement system 10 shown in FIG. 37, a piano wire 41 is fixed between the upper surface of the ring-type displacement meter 40 and the lower surface of the main girder G directly above it, and the ring-type displacement meter 40 measures the displacement of the piano wire 41 due to the deflection of the superstructure 7 and transmits the measured target data U(k) to the measurement 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 meter 40. In the measurement system 10 shown in FIG. 38, a camera 50 transmits an image of a target 51 provided on the side of the main girder G to the measurement device 1. The measuring device 1 processes the image transmitted from the camera 50, calculates the displacement of the target 51 due to the deflection of the upper structure 7, generates target data U(k), and generates measurement data U'(k) by removing drift noise from the generated target data U(k). In the example of Fig. 38, the measuring device 1 generates the target data U(k) as a displacement measuring device by image processing, but a displacement measuring device (not shown) different from the measuring device 1 may generate the target data U(k) by image processing.

[0214] In addition, in each of the above embodiments, the bridge 5 is a railway bridge, and the moving body moving on the bridge 5 is a railway vehicle 6, but the bridge 5 may be a road bridge, and the moving body moving on the bridge 5 may be a vehicle such as an automobile, a tram, or a construction vehicle. FIG. 39 shows a configuration example of the measurement system 10 when the bridge 5 is a road bridge and a vehicle 6a moves on the bridge 5. In FIG. 39, the same components as in FIG. 1 are given the same reference numerals. As shown in FIG. 39, the bridge 5, which is a road bridge, is composed of a superstructure 7 and a substructure 8, similar to a railway bridge. FIG. 40 is a cross-sectional view of the superstructure 7 cut along the line AA in FIG. 39. As shown in FIGS. 39 and 40, the superstructure 7 includes a bridge deck 7a composed of a deck plate F, a main girder G, a cross girder (not shown), and a support 7b. As shown in FIG. 39, the substructure 8 includes a pier 8a and an abutment 8b. The superstructure 7 is a structure that spans either 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 the positions of adjacent abutments 8b and piers 8a, two adjacent abutments 8b, or two adjacent piers 8a. The bridge 5 is, for example, a steel bridge, a girder bridge, an RC bridge, or the like.

[0215] Each sensor 2 is installed in the longitudinal center of the superstructure 7, specifically, in the longitudinal center of the main girder G. However, as long as each sensor 2 is capable of detecting acceleration for calculating the displacement of the superstructure 7, its installation position is not limited to the central part of the superstructure 7. If each sensor 2 is installed on the deck F of the superstructure 7, there is a risk that it will be destroyed by the running vehicle 6a, and there is also a risk that the measurement accuracy will be affected by local deformation of the bridge deck 7a. Therefore, in the examples of Figures 39 and 40, each sensor 2 is installed on the main girder G of the superstructure 7.

[0216] As shown in FIG. 40, the superstructure 7 has two lanes L on which a vehicle 6a, which is a moving body, can move. 1 ,L 2 39 and 40, a sensor 2 is provided on each of the two main girders at both ends in the longitudinal center of the superstructure 7, and the lane L located vertically above one of the sensors 2 is 1 At the surface position of observation point R1 is provided, and a lane L located vertically above the other sensor 2 is provided. 2 At the surface position of observation point R 2 That is, the two sensors 2 are provided at the observation points R 1 ,R 2 Observation point R is an observation device that observes the following: 1 ,R 2 The two sensors 2, which respectively observe the following points, are located at the observation points R 1 ,R 2 The acceleration occurring at the observation point R must be detected. 1 ,R 2 It is preferable that the number of sensors 2 and their installation positions, as well as the number of lanes, are not limited to the examples shown in Figures 39 and 40, and various modifications are possible.

[0217] The measurement device 1 detects the lane L of the vehicle 6a based on the acceleration data output from each sensor 2. 1 ,L 2 Calculate the deflection displacement of lane L 1 ,L 2 The information on the displacement of the upper structure 7 is transmitted to the monitoring device 3 via the communication network 4. The monitoring device 3 stores the information in a storage device (not shown) and may perform processing such as monitoring the vehicle 6a and determining whether there is an abnormality in the upper structure 7 based on the information.

[0218] Furthermore, in each of the above embodiments, the sensors 2 are provided on the main girders G of the superstructure 7, but they may also be provided on the surface or interior of the superstructure 7, on the underside of the deck F, on the piers 8a, etc. Furthermore, in each of the above embodiments, the superstructure of a bridge is given as an example of a structure, but this is not limiting, and the structure may be any structure that deforms due to the movement of a moving object.

[0219] Railway cars or vehicles passing over bridges are heavy and can be measured by BWIM. BWIM is an abbreviation for Bridge Weigh in Motion, and is a technology that measures the weight and number of axles of railway cars or vehicles passing over bridges by treating the bridge as a "scale" and measuring the deformation of the bridge. The superstructure of a bridge, which can analyze the weight of a running railway car or vehicle from responses such as deformation and strain, is the structure in which BWIM functions, and the BWIM system, which applies the physical process between the action on the superstructure of the bridge and the response, makes it possible to measure the weight of a running vehicle.

[0220] The above-described embodiment and modifications are merely examples, and the present invention is not limited to these. For example, the embodiments and modifications can be appropriately combined.

[0221] The present invention includes configurations that are substantially the same as the configurations described in the embodiments, for example, configurations with the same functions, methods, and results, or configurations with the same purpose and effects. The present invention also includes configurations in which non-essential parts of the configurations described in the embodiments are replaced. The present invention also includes configurations that achieve the same effects as the configurations described in the embodiments, or configurations that can achieve the same purpose. The present invention also includes configurations in which publicly known technology is added to the configurations described in the embodiments.

[0222] The following can be derived from the above-described embodiment and modifications.

[0223] One aspect of the measurement method is a low-pass filter processing step of low-pass filtering target data including drift noise and vibration components to generate vibration component reduced data in which the vibration components are reduced; a high-pass filter processing step of processing the vibration component reduced data through a high-pass filter to generate drift noise reduced data in which the drift noise is reduced; a correction data estimating step of estimating correction data corresponding to a difference between the drift noise reduced data and data obtained by removing the drift noise from the vibration component reduced data, based on the drift noise reduced data; a vibration component data generating step of generating vibration component data including the vibration component by subtracting the vibration component reduced data from the target data; and generating measurement data by adding the drift noise reduced data, the correction data, and the vibration component data.

[0224] In this measurement method, vibration component reduced data in which vibration components are reduced and vibration component data including vibration components are generated using target data to be processed, drift noise reduced data in which drift noise is reduced is generated from the vibration component reduced data, and correction data is estimated based on the drift noise reduced data. Since the drift noise reduced data has reduced vibration components, correction data estimated with high accuracy can be obtained. Since the correction data corresponds to the difference between the drift noise reduced data and data in which drift noise is removed from the vibration component reduced data, it contains significant signal components removed by high-pass filter processing. Therefore, according to this measurement method, measurement data in which drift noise is reduced with respect to the target data can be generated by adding the drift noise reduced data, the correction data, and the vibration component data. Furthermore, according to this measurement method, drift noise reduced data, the correction data, and vibration component data are generated using target data to be processed, and measurement data in which drift noise is reduced can be generated without having to prepare information for reducing drift noise in advance by adding the drift noise reduced data, the correction data, and the vibration component data. Therefore, by using this measurement method, it is possible to obtain accurate measurement data regardless of changes in the environment, and also to reduce costs.

[0225] In one embodiment of the measurement method, The correction data estimation step includes: a section specifying step of calculating a first peak and a second peak of the drift noise reduced data and specifying a first section before the first peak, a second section between the first peak and the second peak, and a third section after the second peak; a first section correction data generating step of generating first section correction data by inverting a sign of the drift noise reduced data in the first section; a second interval correction data generating step of adding, before a predetermined time in the second interval, data obtained by rearranging the drift noise reduction data before the first peak in reverse order to data after the first peak, and straight line data obtained by multiplying a straight line passing through the first peak and the second peak by −2, and adding, after the predetermined time in the second interval, data obtained by rearranging the drift noise reduction data after the second peak in reverse order to data before the second peak, and the straight line data, to generate second interval correction data; a third section correction data generating step of generating third section correction data by inverting a sign of the drift noise reduction data in the third section; The method may further include a correction data generating step of generating the correction data by adding the first interval correction data, the second interval correction data, and the third interval correction data.

[0226] According to this measurement method, three sections can be identified based on the characteristics of drift noise reduced data in which drift noise and vibration components are reduced for the target data, and more appropriate correction data can be generated for each section, thereby improving the estimation accuracy of the generated correction data.

[0227] Another aspect of the measurement method is a low-pass filter processing step of low-pass filtering target data including drift noise and vibration components to generate vibration component reduced data in which the vibration components are reduced; a high-pass filter processing step of processing the vibration component reduced data through a high-pass filter to generate drift noise reduced data in which the drift noise is reduced; a section specifying step of calculating a first peak and a second peak of the drift noise reduced data and specifying a first section before the first peak, a second section between the first peak and the second peak, and a third section after the second peak; a correction data estimating step of estimating correction data corresponding to a difference between the drift noise reduced data and data obtained by removing the drift noise from the vibration component reduced data in the second section based on the drift noise reduced data; a vibration component data generating step of generating vibration component data including the vibration component by subtracting the vibration component reduced data from the target data; The method includes a measurement data generation process for generating measurement data by using the first section as the vibration component reduced data, adding the drift noise reduced data, the correction data, and the vibration component data in the second section, and using the third section as the vibration component reduced data.

[0228] In this measurement method, vibration component reduced data in which the vibration component is reduced and vibration component data including the vibration component are generated using target data to be processed, drift noise reduced data in which drift noise is reduced is generated from the vibration component reduced data, three sections are identified based on the characteristics of the drift noise reduced data, and correction data is estimated in the second section. Since the drift noise reduced data has reduced vibration components, correction data estimated with high accuracy is obtained in the second section. In addition, since the correction data corresponds to the difference between the data obtained by removing drift noise from the vibration component reduced data and the drift noise reduced data in the second section, it contains significant signal components removed by high-pass filter processing. Therefore, according to this measurement method, the first and third sections are vibration component data, and in the second section, the drift noise reduced data, the correction data, and the vibration component data are added to generate measurement data in which drift noise is reduced with respect to the target data. Furthermore, according to this measurement method, drift noise reduced data, correction data, and vibration component data are generated using the target data to be processed, and the drift noise reduced data, correction data, and vibration component data are added in the second section, thereby generating measurement data with reduced drift noise without having to prepare information for reducing drift noise in advance. Therefore, by using this measurement method, it is possible to obtain accurate measurement data regardless of changes in the environment, and to reduce costs.

[0229] In addition, according to this measurement method, since it is not necessary to generate correction data or add drift noise reduction data, correction data, and vibration component data in the first and third sections to generate measurement data, the amount of calculations is reduced.

[0230] In one embodiment of the measurement method, In the correction data estimation step, Before a predetermined time in the second section, data obtained by rearranging the drift noise reduction data before the first peak in reverse order to data after the first peak and straight-line data obtained by multiplying a straight line passing through the first peak and the second peak by −2 are added together, and after the predetermined time in the second section, data obtained by rearranging the drift noise reduction data after the second peak in reverse order to data before the second peak and the straight-line data are added together to generate the correction data.

[0231] According to this measurement method, more appropriate correction data can be generated in the second section based on the characteristics of drift noise reduced data in which drift noise and vibration components are reduced compared to the target data, thereby improving the estimation accuracy of the generated correction data.

[0232] In one embodiment of the measurement method, In the low pass filter processing step, The target data may be subjected to a fast Fourier transform to calculate a fundamental frequency, and the vibration component reduced data may be generated by performing a moving average process on the target data in a period corresponding to the fundamental frequency as the low-pass filter process.

[0233] 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 fundamental frequency signal component and its harmonic components, so that vibration component reduced data in which the vibration components are effectively reduced can be obtained. Therefore, according to this measurement method, the influence of the vibration components can be eliminated and the estimation accuracy of the correction data can be improved.

[0234] In one embodiment of the measurement method, In the low pass filter processing step, The target data may be subjected to a fast Fourier transform process to calculate a fundamental frequency, and as the low-pass filter process, an FIR filter process may be performed on the target data to attenuate signal components having frequencies equal to or higher than the fundamental frequency, thereby generating the vibration component reduced data.

[0235] In this measurement method, the FIR filter process requires a larger amount of calculation than the moving average process, but it can attenuate all signal components with frequencies equal to or higher than the fundamental frequency. Therefore, this measurement method can eliminate the effects of vibration components equal to or higher than the fundamental frequency and improve the estimation accuracy of the correction data.

[0236] In one embodiment of the measurement method, The high-pass filter process may be a process of subtracting data obtained by performing moving average processing or FIR filter processing on the vibration component reduced data from the vibration component reduced data.

[0237] According to this measurement method, high-pass filter processing can be performed easily, and since the group delay of each signal component contained in the vibration component reduced data is constant in moving average processing or FIR filter processing, the correction data can be estimated with high accuracy.

[0238] In one embodiment of the measurement method, The target data may be data on the displacement of a structure caused by a moving object moving across the structure.

[0239] According to this measurement method, displacement data of a structure caused by the movement of a mobile object is obtained as measurement data with reduced drift noise, so that the displacement of the structure can be measured with high accuracy.

[0240] In one embodiment of the measurement method, The target data may be data obtained by integrating twice the acceleration in a direction intersecting with a plane of the structure in which the moving body moves.

[0241] According to this measurement method, the displacement of a structure can be measured with high accuracy using output data from an acceleration sensor installed in the structure.

[0242] In one embodiment of the measurement method, The target data may be observation data from a contact type displacement meter, a ring type displacement meter, a laser displacement meter, a pressure sensor, a displacement measuring device using image processing, or a displacement measuring device using optical fiber, or data obtained by integrating the speed detected by a speed sensor.

[0243] According to this measurement method, the displacement of a structure can be measured with high accuracy using data on the displacement, stress change, or velocity.

[0244] In one embodiment of the measurement method, The structure may be a bridge superstructure.

[0245] According to this measurement method, the displacement of the superstructure of a bridge can be measured with high accuracy.

[0246] In one embodiment of the measurement method, The frequency of the drift noise may be lower than the minimum natural vibration frequency of the superstructure.

[0247] According to this measurement method, by setting the cutoff frequencies of the low-pass filter processing and the high-pass filter processing higher than the frequency of the drift noise of the superstructure and lower than the minimum value of the natural vibration frequency, it is possible to reduce the drift noise in the generated displacement data without reducing the signal component of the natural vibration frequency of the superstructure and its harmonic components.

[0248] In one embodiment of the measurement method, The moving object may be a vehicle or a railcar.

[0249] According to this measurement method, the displacement of a structure caused by the movement of a vehicle or railcar can be measured with high accuracy.

[0250] In one embodiment of the measurement method, The target data may include data of a waveform that is convex in a positive or negative direction.

[0251] According to this measurement method, more appropriate correction data can be generated based on the characteristics of a waveform that is convex in the positive or negative direction, so that the estimation accuracy of the generated correction data can be improved.

[0252] In one embodiment of the measurement method, The waveform may be a square waveform, a trapezoidal waveform or a half-sine waveform.

[0253] According to this measurement method, more appropriate correction data can be generated based on the characteristics of a rectangular waveform, a trapezoidal waveform, or a half-sine waveform, so that the estimation accuracy of the generated correction data can be improved.

[0254] One aspect of the measurement device is a low-pass filter processing unit that performs low-pass filter processing on target data including drift noise and vibration components to generate vibration component reduced data in which the vibration components are reduced; a high-pass filter processing unit that generates drift noise reduced data by high-pass filtering the vibration component reduced data to reduce the drift noise; a correction data estimation unit that estimates correction data corresponding to a difference between the drift noise reduced data and data obtained by removing the drift noise from the vibration component reduced data, based on the drift noise reduced data; a vibration component data generating unit that generates vibration component data including the vibration component by subtracting the vibration component reduced data from the target data; and a measurement data generating unit that generates measurement data by adding the drift noise reduced data, the correction data, and the vibration component data.

[0255] This measurement device generates vibration component reduced data in which vibration components are reduced and vibration component data including vibration components using target data to be processed, generates drift noise reduced data in which drift noise is reduced from the vibration component reduced data, and estimates correction data based on the drift noise reduced data. Since the vibration components are reduced in the drift noise reduced data, correction data estimated with high accuracy can be obtained. Since the correction data corresponds to the difference between the data obtained by removing drift noise from the vibration component reduced data and the drift noise reduced data, it includes significant signal components removed by the high-pass filter processing. Therefore, according to this measurement device, by adding the drift noise reduced data, the correction data, and the vibration component data, it is possible to generate measurement data in which drift noise is reduced compared to the target data. Furthermore, according to this measurement device, by using the target data to be processed to generate drift noise reduced data, correction data, and vibration component data, and by adding the drift noise reduced data, correction data, and vibration component data, it is possible to generate measurement data in which drift noise is reduced without having to prepare information for reducing drift noise in advance. Therefore, by using this measuring device, it is possible to obtain accurate measurement data regardless of changes in the environment, and also to reduce costs.

[0256] One aspect of the measurement system is An embodiment of the measuring device; An observation device for observing an observation point, The target data is data based on observation data obtained by the observation device.

[0257] One aspect of the measurement program is a low-pass filter processing step of low-pass filtering target data including drift noise and vibration components to generate vibration component reduced data in which the vibration components are reduced; a high-pass filter processing step of processing the vibration component reduced data through a high-pass filter to generate drift noise reduced data in which the drift noise is reduced; a correction data estimating step of estimating correction data corresponding to a difference between the drift noise reduced data and data obtained by removing the drift noise from the vibration component reduced data, based on the drift noise reduced data; a vibration component data generating step of generating vibration component data including the vibration component by subtracting the vibration component reduced data from the target data; and generating measurement data by adding the drift noise reduced data, the correction data, and the vibration component data.

[0258] In this measurement program, vibration component reduced data in which the vibration component is reduced and vibration component data including the vibration component are generated using the target data to be processed, drift noise reduced data in which the drift noise is reduced are generated from the vibration component reduced data, and correction data is estimated based on the drift noise reduced data. Since the drift noise reduced data has reduced vibration components, correction data estimated with high accuracy can be obtained. Since the correction data corresponds to the difference between the data obtained by removing drift noise from the vibration component reduced data and the drift noise reduced data, it contains significant signal components removed by the high-pass filter processing. Therefore, according to this measurement program, measurement data in which drift noise is reduced with respect to the target data can be generated by adding the drift noise reduced data, the correction data, and the vibration component data. Furthermore, according to this measurement program, drift noise reduced data, the correction data, and vibration component data are generated using the target data to be processed, and measurement data in which drift noise is reduced can be generated without having to prepare information for reducing drift noise in advance by adding the drift noise reduced data, the correction data, and the vibration component data. Therefore, by using this measurement program, it is possible to obtain accurate measurement data regardless of changes in the environment, and also to reduce costs. [Explanation of symbols]

[0259] 1...measuring device, 2...sensor, 3...monitoring device, 4...communication network, 5...bridge, 6...railroad vehicle, 6a...vehicle, 7...superstructure, 7a...bridge deck, 7b...bearing, 7c...rail, 7d...sleeper, 7e...ballast, F...floor plate, G...main girder, 8...substructure, 8a...pier, 8b...abutment, 10...measuring system, 11...first communication unit, 12...second communication unit, 13...processor, 14...memory unit, 21...communication unit, 22...acceleration sensor, 23...processor, 24...memory unit, 31...communication unit, 32...processor, 33...display unit, 34...operation unit, 35...memory unit, 40... Ring type displacement meter, 41... piano wire, 50... camera, 51... target, 131... object data generation unit, 132... low pass filter processing unit, 133... high pass filter processing unit, 134... correction data estimation unit, 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

1. A low-pass filter processing step of performing low-pass filter processing on target data including drift noise and vibration components to generate vibration component reduction data with the vibration components reduced; A high-pass filter processing step of performing high-pass filter processing on the vibration component reduction data to generate drift noise reduction data with the drift noise reduced; A correction data estimation step of estimating correction data corresponding to the difference between the data obtained by subtracting the drift noise from the vibration component reduction data based on the drift noise reduction data and the drift noise reduction data; A vibration component data generation step of subtracting the vibration component reduction data from the target data to generate vibration component data including the vibration components; A measurement data generation step of adding the drift noise reduction data, the correction data, and the vibration component data to generate measurement data, including: The correction data estimation step includes: An interval identification step of calculating a first peak and a second peak of the drift noise reduction data, and identifying a first interval before the first peak, a second interval between the first peak and the second peak, and a third interval after the second peak; A first interval correction data generation step of inverting the sign of the drift noise reduction data in the first interval to generate first interval correction data; A second interval correction data generation step of adding, before a predetermined time in the second interval, data obtained by arranging the drift noise reduction data before the first peak in reverse order after the first peak and linear data obtained by multiplying a straight line passing through the first peak and the second peak by -2, and adding, after the predetermined time in the second interval, data obtained by arranging the drift noise reduction data after the second peak in reverse order before the second peak and the linear data to generate second interval correction data; A third interval correction data generation step of inverting the sign of the drift noise reduction data in the third interval to generate third interval correction data; A correction data generation step of adding the first interval correction data, the second interval correction data, and the third interval correction data to generate the correction data, including a measurement method.

2. A low-pass filter processing step of performing low-pass filter processing on target data including drift noise and vibration components to generate vibration component reduction data with the vibration components reduced; A high-pass filter processing step of performing high-pass filter processing on the vibration component reduction data to generate drift noise reduction data with the drift noise reduced; ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ By subjecting the vibration component reduction data to high-pass filter processing, the drift noise A high-pass filter processing step for generating drift noise reduction data with reduced drift noise; Calculating a first peak and a second peak of the drift noise reduction data, and the first peak A first section before, a second section between the first peak and the second peak, and the second peak An interval specifying step of specifying a third section after the peak; Based on the drift noise reduction data, in the second section, the vibration component reduction A correction data estimation step of estimating correction data corresponding to the difference between the data obtained by subtracting the drift noise from the data and 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; Using the first section as the vibration component data, in the second section, adding the drift noise Reduction data, the correction data, and the vibration component data, and using the third section as the vibration An measurement data generation step of generating measurement data as component data; In the correction data estimation step, Before a predetermined time in the second section, the drift noise reduction before the first peak Data obtained by rearranging the data in reverse order after the first peak and straight line data obtained by multiplying by -2 the straight line passing through the first peak and the second Peak are added, and after the predetermined time in the second section, the drift noise reduction data after the second peak is rearranged in reverse order before the second peak The data and the straight line data are added to generate the correction data. A measurement method.

3. In claim 1 or 2, In the low-pass filter processing step, The target data is subjected to fast Fourier transform processing to calculate a fundamental frequency, and as the low-pass filter Processing, the target data is subjected to moving average processing with a period corresponding to the fundamental frequency to generate the Vibration component reduction data. A measurement method.

4. In claim 1 or 2, In the low-pass filter processing step, The target data is subjected to fast Fourier transform processing to calculate a fundamental frequency, and as the low-pass filter Processing, FIR filter processing for attenuating signal components having a frequency equal to or higher than the fundamental frequency with respect to the target data is performed to generate the Vibration component reduction data. A measurement method.

5. In any one of claims 1 to 4, ​ The high-pass filter process is a process of subtracting data obtained by subjecting the vibration component reduction data to a moving average process or an FIR filter process from the vibration component reduction data, measurement method

6. 。 In any one of Claims 1 to 5, the target data is data of displacement of the structure by a moving body that moves the structure, measurement method

7. In Claim 6, the target data is data obtained by double integrating acceleration in a direction intersecting a surface of the structure where the moving body moves, measurement method

8. In Claim 6, the target data is observation data of a contact displacement meter, a ring displacement meter, a laser displacement meter, a pressure sensor, a displacement measurement device by image processing or a displacement measurement device by an optical fiber, or data obtained by integrating the speed detected by a speed sensor, measurement method

9. In any one of Claims 6 to 8, the structure is an upper structure of a bridge, measurement method

10. In Claim 9, the frequency of the drift noise is lower than the minimum value of the natural vibration frequency of the upper structure, measurement method

11. In any one of Claims 6 to 10, the moving body is a vehicle or a railway vehicle, measurement method

12. In any one of Claims 1 to 11, the target data includes data of a waveform convex in the positive or negative direction, measurement method

13. In Claim 12, the waveform is a rectangular waveform, a trapezoidal waveform or a sine half-waveform, measurement method

14. a low-pass filter processing unit that generates vibration component reduction data by subjecting target data including drift noise and vibration components to low-pass filter processing to reduce the vibration components; a high-pass filter processing unit that generates drift noise reduction data by subjecting the vibration component reduction data to high-pass filter processing to reduce the drift noise; a correction data estimation unit that estimates correction data corresponding to a difference between data obtained by subtracting the drift noise from the vibration component reduction data based on the drift noise reduction data and the drift noise reduction data; a vibration component data generation unit that generates vibration component data including the vibration components 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, and includes the correction data estimation unit ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ Calculate the first peak and the second peak of the drift noise reduction data, and the first peak A first section before, a second section between the first peak and the second peak, and a third section after the second peak Are identified, In the first section, reverse the sign of the drift noise reduction data to generate first section correction Data, Before a predetermined time in the second section, the drift noise reduction before the first peak The data obtained by rearranging the data in reverse order after the first peak and the straight line data obtained by multiplying by -2 the straight line passing through the first peak and the second peak Are added. After the predetermined time in the second section, the drift noise reduction data after the second peak The data rearranged in reverse order before the second peak and the straight line data are added to generate second section correction data Data, Are generated, In the third section, reverse the sign of the drift noise reduction data to generate third section correction Data, Add the first section correction data, the second section correction data, and the third section correction data To generate the correction data, a measuring device.

15. A low-pass filter processing unit that performs low-pass filter processing on target data including drift noise and vibration components to generate vibration component reduction data with the vibration Components reduced; A high-pass filter processing unit that performs high-pass filter processing on the vibration component reduction data to generate drift noise reduction data with the drift Noise reduced; Calculate the first peak and the second peak of the drift noise reduction data, and the first peak A first section before, a second section between the first peak and the second peak, and a third section after the second peak An interval specifying unit that specifies; Based on the drift noise reduction data, in the second section, the vibration component reduction A correction data estimation unit that estimates correction data corresponding to the difference between the data obtained by subtracting the drift noise from the data and the drift noise reduction data; A vibration component data generation unit that subtracts the vibration component reduction data from the target data to generate vibration component data including the vibration component; The first section is used as the vibration component data. In the second section, the drift noise Reduction data, the correction data, and the vibration component data are added. The third section is used as the vibration Component data, and a measurement data generation unit that generates measurement data, Including, The correction data estimation unit is ​ Before a predetermined time in the second interval, the drift noise reduction data before the first peak is rearranged in reverse order after the first peak, and the linear data obtained by multiplying by -2 the straight line passing through the first peak and the second peak is added. After the predetermined time in the second interval, the drift noise reduction data after the second peak is rearranged in reverse order before the second peak, and the linear data is added to generate the correction data. Measuring device.

16. The measuring device according to claim 14 or 15, and an observation device for observing an observation point, wherein the target data is data based on the observation data by the observation device. Measuring system.

17. A low-pass filter processing step of generating vibration component reduction data in which a target data including drift noise and vibration components is low-pass filtered to reduce the vibration components; a high-pass filter processing step of generating drift noise reduction data in which the drift noise is reduced by high-pass filtering the vibration component reduction data; a correction data estimation step of estimating correction data corresponding to the difference between the data obtained by subtracting the drift noise from the vibration component reduction data based on the drift noise reduction data and the drift noise reduction data; a vibration component data generation step of generating vibration component data including the vibration components by subtracting the vibration component reduction data from the target data; and a measurement data generation step of generating measurement data by adding the drift noise reduction data, the correction data, and the vibration component data. The computer is caused to execute the correction data estimation step, which calculates the first peak and the second peak of the drift noise reduction data, and identifies a first interval before the first peak, a second interval between the first peak and the second peak, and a third interval after the second peak. In the first interval, the sign of the drift noise reduction data is inverted to generate first interval correction data. Before a predetermined time in the second interval, the drift noise reduction data before the first peak is rearranged in reverse order after the first peak, and the linear data obtained by multiplying by -2 the straight line passing through the first peak and the second peak is added. ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ After the time, the drift noise reduction data after the second peak is added to the data arranged in reverse order before the second peak and the linear data to generate second interval correction data. A second interval correction data generation step of generating second interval correction data; In the third interval, a third interval correction data generation step of inverting the sign of the drift noise reduction data to generate third interval correction data; A correction data generation step of adding the first interval correction data, the second interval correction data, and the third interval correction data to generate the correction data. The measurement program includes:

18. A low-pass filter processing step of performing low-pass filter processing on target data including drift noise and vibration components to generate vibration component reduction data with the vibration components reduced; A high-pass filter processing step of performing high-pass filter processing on the vibration component reduction data to generate drift noise reduction data with the drift noise reduced; An interval identification step of calculating a first peak and a second peak of the drift noise reduction data, and identifying a first interval before the first peak, a second interval between the first peak and the second peak, and a third interval after the second peak; Based on the drift noise reduction data, in the second interval, a correction data estimation step of estimating correction data corresponding to the difference between the data obtained by subtracting the drift noise from the vibration component reduction data and the drift noise reduction data; A vibration component data generation step of subtracting the vibration component reduction data from the target data to generate vibration component data including the vibration components; A measurement data generation step of using the first interval as the vibration component data, adding the drift noise reduction data, the correction data, and the vibration component data in the second interval, and using the third interval as the vibration component data to generate measurement data, and causing a computer to execute; In the correction data estimation step, Before a predetermined time in the second interval, the drift noise reduction data before the first peak is added to the data arranged in reverse order after the first peak and the linear data obtained by multiplying by -2 the straight line passing through the first peak and the second peak, and after the predetermined time in the second interval, the drift noise reduction data after the second peak is arranged in reverse order before the second peak. ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ A measurement program that adds data rearranged in reverse order before K and the linear data to generate the correction data.

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