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 high-pass filter processing step of performing high-pass filter processing on object data including drift noise to generate drift noise reduced data with reduced drift noise; a correction data estimation step of, on the basis of, the drift noise reduced data, estimating correction data corresponding to a difference between data obtained by excluding the drift noise from the object data and the drift noise reduced data; and a measurement data generation step of adding up the drift noise reduced data and the correction data to generate measurement data.SELECTED DRAWING: Figure 23

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 high-pass filter processing step of high-pass filtering target data including drift noise to generate drift noise reduced data in which the drift noise is reduced; a correction data estimation step of estimating correction data corresponding to a difference between the drift noise reduced data and data obtained by removing the drift noise from the target data, based on the drift noise reduced data; and generating measurement data by adding the drift noise reduced data and the correction data.

[0007] Another aspect of the measurement method according to the present invention is to The target data including drift noise is subjected to high-pass filtering to remove the drift noise. a high pass filtering step to produce reduced drift noise data; 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 estimation 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 target data in the second section based on the drift noise reduced data; and a measurement data generating step of setting the first section to 0, adding the drift noise reduced data and the correction data in the second section, setting the third section to 0, and generating measurement data.

[0008] One aspect of the measuring device according to the present invention is a high-pass filter processing unit that performs high-pass filter processing on target data including drift noise to generate drift noise reduced data in which the drift noise is reduced; 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 target data, based on the drift noise reduced data; and a measurement data generating unit that generates measurement data by adding the drift noise reduced data and the correction 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 high-pass filter processing step of high-pass filtering target data including drift noise to generate drift noise reduced data in which the drift noise is reduced; a correction data estimation step of estimating correction data corresponding to a difference between the drift noise reduced data and data obtained by removing the drift noise from the target data, based on the drift noise reduced data; and generating measurement data by adding the drift noise reduced data and the correction 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] FIG. 13 is a diagram showing frequency characteristics F{Ms(k)} of target data Ms(k). [Diagram 5] A diagram showing the relationship between frequency characteristics F{Ms(k)}, F{fHP(Ms(k))}, and F{fLP(Ms(k))}. [Figure 6] A diagram showing the relationship between frequency characteristics F{Ms(k)}, F{M(k)}, and F{e(k)}. [Figure 7] A diagram showing the relationship between frequency characteristics F{M'(k)}, F{fHP(M(k))}, and F{fLP(M(k))}. [Figure 8] FIG. 4 is a diagram showing target data Ms(k) which is a unit pulse waveform. [Figure 9] FIG. 13 is a diagram showing data fLP(Ms(k)) obtained by low-pass filtering target data Ms(k). [Figure 10] FIG. 13 is a diagram showing data fHP(Ms(k)) obtained by high-pass filtering target data Ms(k). [Figure 11] FIG. 4 is a diagram showing an example of displacement data MU(k). [Figure 12] FIG. 13 is a diagram showing an example of first section correction data MCC1(k) and third section correction data MCC3(k). [Figure 13] FIG. 13 is a diagram showing an example of second section first correction data M1CC2(k). [Figure 14] FIG. 4 is a diagram showing an example of a straight line LC(k). [Figure 15] FIG. 13 is a diagram showing an example of second section second correction data M2CC2(k). [Figure 16] FIG. 13 is a diagram showing an example of second section correction data MCC2(k). [Figure 17] FIG. 13 is a diagram showing an example of correction data MCC(k). [Figure 18] FIG. 13 is a diagram showing an example of measurement data RU(k). [Figure 19] FIG. 4 is a diagram showing an example of a displacement waveform UO(k) and drift noise D(k). [Figure 20] FIG. 4 is a diagram showing an example of an evaluation waveform U(k). [Figure 21] FIG. 13 is a diagram showing measurement data RU(k). [Figure 22] FIG. 13 is a diagram showing measurement data RU(k) and a displacement waveform UO(k) superimposed on each other. [Figure 23] FIG. 4 is a flowchart showing an example of a procedure of a measurement method according to the first embodiment. [Figure 24] FIG. 4 is a flowchart showing an example of a procedure of a correction data estimation step in the first embodiment. [Diagram 25] 1A and 1B are diagrams showing configuration examples of a sensor, a measuring device, and a monitoring device. [Figure 26] FIG. 11 is a flowchart showing an example of a procedure of a measurement method according to a second embodiment. [Figure 27] FIG. 11 is a flowchart showing an example of a procedure of a correction data estimation step in the second embodiment. [Figure 28] FIG. 13 is a diagram showing an example of the arrangement of a measurement device according to a second embodiment. [Figure 29] FIG. 13 is a diagram showing another example of the configuration of the measurement system. [Diagram 30] FIG. 13 is a diagram showing another example of the configuration of the measurement system. [Diagram 31] FIG. 13 is a diagram showing another example of the configuration of the measurement system. [Diagram 32] A cross-sectional view of the superstructure in Figure 31 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] Based on the acceleration data output from the sensor 2, the measurement device 1 obtains the acceleration in a direction intersecting with the plane of the superstructure 7 along which the railcar 6 moves. The plane of the superstructure 7 along which the railcar 6 moves is defined by the direction in 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 in 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 moves in a direction perpendicular to the X and Y directions. Since the floor board F bends in the direction perpendicular to the X and Y directions, it is desirable for the measurement device 1 to acquire acceleration in the Z direction, which is the normal direction of the floor board F, in order to accurately calculate the magnitude of the acceleration of the bending.

[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 s (k), and in Fig. 4, the target data M s (k) frequency characteristic F{M s (k)}. Target data M s If the number of samples included in (k) is N, then k is an integer between 0 and N-1.

[0031] Target data M s (k) is high-pass filtered data HP(M s (k)) and the target data M s (k) is low-pass filtered data, LP (M s (k)), the target data M s (k), Data f HP (M s (k)) and data f LP (M s The relationship between (k) is shown in equation (1).

[0032]

number

[0033] In addition, the target 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))}.

[0034]

number

[0035] Here, as shown in formula (3), the target data M obtained based on the acceleration data is s Let (k) contain a significant signal M(k) and a drift noise e(k).

[0036]

number

[0037] 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. Figure 6 shows the frequency characteristic F{M s The relationship between F{M(k)}, F{e(k)}, and F{M(k)} is shown. Because the drift noise e(k) is observed as an offset error, high-pass filter processing that attenuates the low-frequency signal is effective in removing the drift noise e(k).

[0038] Target data M s When high-pass filtering is applied to data f(k), the drift noise e(k) with its energy distributed in the low frequency range is sufficiently suppressed, and the high-pass filtered data f(k) 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)).

[0039]

number

[0040] The low-frequency signal components of the signal M(k) are also lost due to the high-pass filter processing, so in order to compensate for these signal components, the target 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 (5), the signal M(k) is low-pass filtered to obtain data f LP (M(k)) is the target 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

[0041]

number

[0042] As shown in equation (6), the signal M(k) is obtained by high-pass filtering the signal M(k), HP (M(k)) and the low-pass filtered data f LP Assuming that the sum of F{M'(k)} and F{f HP (M s (k))},F{A LP (f HP (M s (k)))} relationship.

[0043]

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[0044]

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[0045] Target 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 sBy adding the estimated data e(k) to the signal M(k), it is possible to obtain a signal M(k) in which the drift noise e(k) has been reduced.

[0046] In the following, the target data M s As an example, if (k) is displacement data, the target 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.

[0047] First, as shown in equation (8), the target data M s (k) is 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 (8), k is an integer equal to or greater than 0. FIG. 8 shows the target data M s (k) is shown.

[0048]

number

[0049] Target data M s (k) Target 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 It is assumed that the relationship between (k) and (k) is as shown in the above formula (1). For example, if the low-pass filter processing is a moving average processing, formula (9) can be obtained from the above formula (1). In this case, data k is located in the center of the moving average interval 2p+1.

[0050]

number

[0051] In formula (9), p is an integer equal to or greater than 1, and the target 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. 9, the target data M s Data f obtained by low-pass filtering (k) using a moving average LP (M s FIG. 10 shows the target data M s Data f obtained by high-pass filtering (k) HP (M s (k)) is shown.

[0052] Using Fig. 9 and Fig. 10, the target 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)).

[0053] As shown in Figure 9, the target 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 (10).

[0054]

number

[0055] 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.

[0056] 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 (11).

[0057]

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[0058] 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 (12).

[0059]

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[0060] By substituting the above equation (8) into equation (12), the amplitude A is calculated as shown in equation (13).

[0061]

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[0062] From equation (13), if p is sufficiently large, the amplitude A becomes 1 / 2.

[0063] Here, the target data M s The unit pulse waveform assumed as (k) in equation (8) does not include drift noise e(k). Therefore, according to the above equation (3), the target 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 s By measuring the slope a and amplitude A of (k), the target data M s The signal M(k) obtained by removing the drift noise e(k) from (k) is low-pass filtered to obtain data f LP (M(k)) can be estimated.

[0064] 1-3. Details of the measurement method In reality, the target data M, which is the displacement data of the deflection of the superstructure 7 of the bridge 5 when the railway vehicle 6 passes through s The data f(k) includes data of a waveform that is convex in the positive or negative direction, which is different from the unit pulse waveform, and is obtained by low-pass filtering the signal M(k) based on the above estimation method. LP (M(k)) can be estimated. For example, a positive or negative convex wave The shape may be a square waveform, a trapezoidal waveform, or a half-sine waveform.

[0065] First, the measurement device 1 calculates the acceleration data A output from the acceleration sensor as shown in Equation (14). s Integrate (k) to get the velocity data V s (k) is generated, and then the velocity data V s (k) is integrated to obtain the target data M s In equations (14) and (15), ΔT is the time interval of the data.

[0066]

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[0067]

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[0068] Next, the measuring device 1 generates displacement data MU(k) obtained by subjecting target data M s (k) to a high-pass filter process in order to reduce drift noise. FIG. 11 shows an example of the displacement data MU(k).

[0069] [Number]

[0070] Next, the measuring device 1 estimates correction data M LP (M(k)), that is, data corresponding to the difference between the data obtained by subjecting the signal M(k) to a low-pass filter process, that is, the target data M s (k) excluding drift noise and the displacement data MU(k). CC (k).

[0071] As shown in FIG. 11, in the present embodiment, the measuring device 1 identifies the first section T1, the second section T2, and the third section T3 based on the displacement data MU(k), and generates the correction data M CC (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 measuring device 1 calculates a first peak p1 = (k1, mu1) and a second peak p2 = (k2, mu2) of the displacement data MU(k). As shown in FIG. 11, the first peak p1 is the leading peak near the time when the railway vehicle 6 enters the superstructure 7, and the second peak p2 is the trailing peak near the time when the railway vehicle 6 exits the superstructure 7. The first section T1 is the section before the first peak p1, that is, the section where k ≤ k1. The second section T2 is the section between the first peak p1 and the second peak p2, that is, the section where k1 < k < k2. The third section T3 is the section after the second peak p2, that is, the section where k2 ≤ k.

[0072] As in formula (17), 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 M CC3 It can be calculated as the sum of (k).

[0073]

number

[0074] 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 (18). 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 (19). CC1 (k) and the third section correction data M CC3 An example of (k) is shown below.

[0075]

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[0076]

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[0077] Second section correction data M CC2(k) is obtained as follows. First, in the section k≦(k1+k2) / 2 before a certain time in the second section T2, data obtained by rearranging the displacement data MU(k) before the first peak p1 in reverse order to the first peak p1 and after the first peak p1 is MU(2k1-k). Also, in the section (k1+k2) / 2≦k after the certain time in the second section T2, data obtained by rearranging the displacement data MU(k) after the second peak p2 in reverse order to the second peak p2 and before the second peak p2 is MU(2k2-k). Here, the certain time is a time corresponding to k=k1+k2, but it may be another time.

[0078] Then, using the data MU(2k1-k) and the data MU(2k2-k), the first correction data M1 CC2 (k) is obtained. In FIG. 13, the first correction data M1 CC2 An example of (k) is shown below.

[0079]

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[0080] A straight line L passing through the first peak p1 = (k1, mu1) and the second peak p2 = (k2, mu2) C (k) is calculated by equation (21). In FIG. C An example of (k) is shown below.

[0081]

number

[0082] According to equation (22), 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. 15, the second section second correction data M2 CC2 An example of (k) is shown below.

[0083]

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[0084] As shown in equation (23), 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 M2 CC2 It can be calculated as the sum of (k). FIG. 16 shows the second section correction data M CC2 An example of (k) is shown below.

[0085]

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[0086] Correction data M CC (k) is obtained by substituting equations (18), (19), and (23) into equation (17) as equation (24). CC An example of (k) is shown below.

[0087]

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[0088] Then, as shown in equation (25), the displacement data MU(k) and the correction data M CC (k) to obtain measurement data RU(k), which is displacement data with reduced drift noise.

[0089]

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[0090] By substituting equation (24) into equation (25), equation (26) is obtained.

[0091]

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[0092] Equation (26) is transformed into equation (27).

[0093]

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[0094] According to formula (27), the measurement data RU(k) is 0 in the first interval T1 where k≦k1 and the second interval T2 where k2≦k, and the measurement data RU(k) from which drift noise has been removed is obtained. FIG. 18 shows an example of the measurement data RU(k).

[0095] 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) is used as the evaluation waveform U(k) as shown in equation (28). Fig. 19 shows an example of the displacement waveform UO(k) and drift noise D(k). Fig. 20 shows an example of the evaluation waveform U(k).

[0096]

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[0097] The evaluation waveform U(k) is the target data M s The measurement data RU(k) obtained by equations (16) to (27) are compared with the displacement waveform UO(k) as (k). Fig. 21 shows the measurement data RU(k). Fig. 22 shows the measurement data RU(k) and the displacement waveform UO(k) superimposed on each other. As shown in Figs. 21 and 22, it can be confirmed that the measurement method of this embodiment can obtain measurement data RU(k) from which drift noise has been removed and the displacement waveform has been restored.

[0098] 1-4. Measurement method procedure 23 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.

[0099] As shown in FIG. 23, first, in the target data generation step S1, the measurement device 1 generates acceleration data A s (k) is acquired, and the target data M s (k) is generated. Therefore, the target data M s (k) is acceleration data A, which is observed by sensor 2, an observation device. s Specifically, the measurement device 1 calculates the above-mentioned formulas (14) and (15) to obtain the target data M s In this embodiment, the target data M s (k) is data on the displacement of the superstructure 7 caused by the railroad vehicle 6, which is a moving body moving on the superstructure 7, which is a structure, and is data obtained by integrating twice the acceleration in the direction intersecting with the plane of the superstructure 7 on which the railroad vehicle 6 moves. Therefore, the target data M s (k) includes data of a waveform that is convex in the positive or negative direction, specifically, a rectangular waveform, a trapezoidal waveform, or a half-sine waveform. Note that a rectangular waveform includes not only an exact rectangular waveform, but also a waveform that approximates a rectangular waveform. Similarly, a trapezoidal waveform includes not only an exact trapezoidal waveform, but also a waveform that approximates a trapezoidal waveform. Similarly, a half-sine waveform includes not only an exact half-sine waveform, but also a waveform that approximates a half-sine waveform.

[0100] Next, in the high-pass filter processing step S2, the measurement device 1 performs a high-pass filter process on the target data M including the drift noise generated in step S1 as shown in the above-mentioned equation (16). s The displacement data MU(k) is generated as drift noise reduced data by subjecting the target data M s The high-pass filter process in (k) is performed on the target data M s From (k), the target data M s The low-pass filter process may be a moving average process or an FIR filter process. FIR stands for Finite Impulse Response. sThe high-pass filter process in (k) is performed on the target data M s From (k), the target data M s In this embodiment, the target data M s The frequency of the drift noise included in (k) is lower than the minimum value of the natural vibration frequency of the superstructure 7. The minimum value of the natural vibration frequency of the superstructure 7 is, for example, the frequency of the first vibration mode in the longitudinal direction of the superstructure 7. By setting the cutoff frequency of the high-pass filter processing 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 drift noise is reduced in the generated displacement data MU(k) 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 high-pass filter processing may be 1 Hz or higher.

[0101] Next, in the correction data estimation step S3, the measurement device 1 estimates the target data M based on the displacement data MU(k) generated in step S2. 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 (17) to (24) to estimate the correction data M CC Generate (k).

[0102] Next, in the measurement data generation process S4, the measurement device 1 calculates the displacement data MU(k) generated in the process S2 and the correction data M CC (k) to generate measurement data RU(k).

[0103] Next, in a measurement data output step S5, the measurement device 1 outputs the measurement data RU(k) generated in step S4 to the monitoring device 3. Specifically, the measurement device 1 transmits the measurement data RU(k) to the monitoring device 3 via the communication network 4.

[0104] Then, in step S6, until the measurement of the displacement of the superstructure 7 of the bridge 5 is completed, the measuring device 1 repeatedly performs the processes of steps S1 to S5.

[0105] FIG. 24 is a flowchart showing an example of the procedure of the correction data estimation step S3 in FIG. 23.

[0106] As shown in FIG. 24, first, in the section identification step S31, the measuring device 1 calculates the first peak p1 = (k1, mu1) and the second peak p2 = (k2, mu2) of the displacement data MU(k), and determines the first section T1 before the first peak p1, the second section T2 between the first peak p1 and the second peak p2, and the third section T3 after the second peak p2. That is, the first section T1 is the section where k ≤ k1, the second section T2 is the section where k1 < k < k2, and the third section T3 is the section where k2 ≤ k. In the present embodiment, the first peak p1 is the leading peak near the time when the railway vehicle 6 enters the superstructure 7, and the second peak p2 is the trailing peak near the time when the railway vehicle 6 exits the superstructure 7.

[0107] Next, in the first section correction data generation step S32, the measuring device 1 inverts the sign of the displacement data MU(k) in the first section T1 as in the above formula (18) to generate the first section correction data M CC1 (k).

[0108] Next, in the second section correction data generation step S33, the measuring device 1, as in the above formula (23), before a predetermined time in the second section T2, rearranges the displacement data MU(k) before the first peak p1 in reverse order after the first peak p1 to obtain data MU(2k1 - k), and adds the straight line data -2L C (k) multiplied by -2, and after the predetermined time in the second section T2, adds the displacement data MU(k) after the second peak p2 rearranged in reverse order before the second peak p2 to obtain data MU(2k2 - k), and adds the straight line data -2L C (k) to generate the second section correction data M C (k). CC2Generate (k).

[0109] Next, in the third section correction data generating step S34, the measurement device 1 calculates the third section correction data by using the above-mentioned formula (19). In the third section T3, the sign of the displacement data MU(k) is inverted to obtain the third section correction data M CC3 Generate (k).

[0110] Finally, in the correction data generation step S35, the measurement device 1 calculates the first section correction data M generated in step S32 as shown in the above equation (17). CC1 (k) and the second section correction data M generated in step S33 CC2 (k) and the third section correction data M generated in step S34 CC3 (k) is added to obtain the correction data M CC Generate (k).

[0111] 1-5. Configuration of observation, measuring and monitoring equipment FIG. 25 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.

[0112] As shown in FIG. 25, the sensor 2 includes a communication unit 21, an acceleration sensor 22, a processor 23, and a memory unit 24.

[0113] 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.

[0114] The acceleration sensor 22 detects the acceleration occurring in each of the three axial directions.

[0115] 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).

[0116] 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.

[0117] As shown in FIG. 25, the measurement device 1 includes a first communication unit 11, a second communication unit 12, a processor 13, and a storage unit .

[0118] 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).

[0119] 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.

[0120] The processor 13 acquires the observation data 242 received by the first communication unit 11, and stores it in the storage unit 14 as 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 RU(k).

[0121] In this embodiment, the processor 13 executes a measurement program 141 stored in the storage unit 14 to generate target data, a high-pass filter processing unit 132, and a correction unit 133. It functions as a correct data estimation unit 133, a measurement data generation unit 134, and a measurement data output unit 135. That is, the processor 13 includes a target data generation unit 131, a high-pass filter processing unit 132, a correction data estimation unit 133, a measurement data generation unit 134, and a measurement data output unit 135.

[0122] 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 Based on (k), the target data M s Specifically, the target data generation unit 131 performs the calculations of the above-mentioned formulas (14) and (15) to generate the target data M s That is, the target data generating unit 131 performs the process of the target data generating step S1 in FIG.

[0123] The high-pass filter processing unit 132 performs a high-pass filter on the target data M including drift noise generated by the target data generating unit 131 as shown in the above equation (16). s 23. The high-pass filter processing unit 132 performs high-pass filter processing step S2 in FIG. 23 to generate displacement data MU(k) as drift-noise reduced data by reducing drift noise by performing high-pass filter processing on (k).

[0124] The correction data estimation unit 133 estimates the target data M based on the displacement data MU(k) generated by the high-pass filter processing unit 132. 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 133 performs the calculations of the above-mentioned formulas (17) to (24) to generate the correction data M CC Generate (k).

[0125] Specifically, first, the correction data estimation unit 133 calculates a first peak p1=(k1, mu1) and a second peak p2=(k2, mu2) of the displacement data MU(k), and specifies a first section T1 before the first peak p1, a second section T2 between the first peak p1 and the second peak p2, and a third section T3 after the second peak p2. That is, the correction data estimation unit 133 performs the process of the section specification step S31 in FIG.

[0126] Next, the correction data estimation unit 133 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 133 performs the process of the first section correction data generation step S32 in FIG.

[0127] Next, as in the above-described formula (23), the correction data estimation unit 133 calculates data MU(2k1-k) obtained by rearranging the displacement data MU(k) before the first peak p1 in reverse order to data after the first peak p1 before a predetermined time in the second section T2, and a straight line L passing through the first peak p1 and the second peak p2. C Linear data -2L obtained by multiplying (k) by -2 C (k) is added, and the displacement data MU(k) after the second peak p2 is rearranged in reverse order to data before the second peak p2 after a predetermined time in the second section T2, and the straight line data -2L C (k) is added to obtain the second section correction data M CC2 That is, the correction data estimation unit 133 performs the process of the second section correction data generation step S33 in FIG.

[0128] Next, the correction data estimation unit 133 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 133 performs the process of the third section correction data generation step S34 in FIG.

[0129] Finally, the correction data estimation unit 133 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 ) to obtain the correction data M CC That is, the correction data estimation unit 133 performs the process of the correction data generation step S35 in FIG.

[0130] In this manner, the correction data estimation unit 133 performs the process of the correction data estimation step S3 in FIG. 23, specifically, the processes of steps S31 to S35 in FIG.

[0131] The measurement data generating unit 134 calculates the displacement data MU(k) generated by the high-pass filter processing unit 132 and the correction data M CC 24. The measured data RU(k) generated by the measured data generating unit 134 is stored in the storage unit 14 as measured data 143.

[0132] The measurement data output unit 135 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 135, 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 135 performs the process of the measurement data output step S5 in FIG.

[0133] 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.

[0134] As shown in FIG. 25, 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.

[0135] 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 RU(k).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The measurement data acquisition unit 321 acquires the measurement data 143 received by the communication unit 31. The obtained measurement data 143 is added to the measurement data string 352 stored in the storage unit 35 .

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 25 shows only one sensor 2, 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.

[0148] 1-6.Effects In the measurement method of the first embodiment described above, the measurement device 1 processes target data M s (k) to generate displacement data MU(k) with reduced drift noise, and corrective data M CC (k) is estimated. Then, the correction data M CC (k) is the target data M s Since it corresponds to the difference between the data with the drift noise removed from (k) and the displacement data MU(k), it is the significant signal removed by the high-pass filter processing. 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 By adding (k) to the target data M s According to the measurement method of the first embodiment, the measurement device 1 can generate measurement data RU(k) in which drift noise is reduced with respect to the target data M s (k), the displacement data MU(k) and the correction data M CC (k) and the displacement data MU(k) and the correction data M CC By adding RU(k) to the measurement data RU(k), it is possible to generate measurement data RU(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 RU(k) with high accuracy regardless of changes in the environment, and to reduce costs.

[0149] In particular, according to the measurement method of the first embodiment, the measurement device 1 measures the target data M s Based on the characteristics of the displacement data MU(k) in which drift noise has been reduced for (k), the first interval T1, the second interval T2, and the third interval T3 are identified, and appropriate 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.

[0150] According to the measurement method of the first embodiment, the measurement device 1 measures the target data M s As a high-pass filter process for (k), the target data M s From (k), the target data M s By performing a process of subtracting data obtained by moving average processing or FIR filter processing from (k), high-pass filter processing can be easily performed. 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.

[0151] In the measurement method of the first embodiment, the target data M s (k) 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 RU(k), which is displacement data of the superstructure 7 due to the movement of the railway vehicle 6 and in which drift noise has been reduced, and therefore it is possible to measure the displacement of the superstructure 7 of the bridge 5 with high accuracy.

[0152] According to the measurement method of the first embodiment, the measurement device 1 integrates twice the acceleration in a direction intersecting with the surface of the upper structure 7 detected by the sensor 2 installed on the upper structure 7 to obtain the target data M s Since (k) is generated, the displacement of the upper structure 7 can be measured with high accuracy.

[0153] In the measurement method of the first embodiment, the target data M s Since the frequency of the drift noise included in (k) is lower than the minimum value of the natural vibration frequency of the upper structure 7, the target data M s The cutoff frequency of the high-pass filter processing for (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 RU(k) without reducing the signal component of the natural vibration frequency of the upper structure 7 and its harmonic components.

[0154] In the measurement method of the first embodiment, the target data M s Since (k) 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, the measurement device 1 can obtain 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.

[0155] 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.

[0156] 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) to generate measurement data RU(k). On the other hand, as shown in the above equation (27), the displacement data MU(k) and the correction data M CCThe measurement data RU(k) obtained by addition with (k) always becomes 0 in the first interval T1 and the third interval T3. Therefore, in the measurement method of the second embodiment, the measuring device 1 does not generate the first interval correction data M CC1 (k) and the third interval correction data M CC3 (k), but generates the correction data M CC2 (k) in the second interval T2. Then, as shown in Equation (29), the measuring device 1 sets it to 0 in the interval of k ≦ k1 which is the first interval T1 and the interval of k2 ≦ k which is the third interval T3, and in the interval of k1 < k < k2 which is the second interval T2, adds the displacement data MU(k) and the correction data M CC2 (k) to generate the measurement data RU(k).

[0157]

Number

[0158] In the above Equation (27), in the interval of k1 < k < k2, the correction data M CC (k) coincides with the second interval correction data M CC2 (k). Therefore, the calculation result by Equation (29) coincides with the calculation result by Equation (27).

[0159] FIG. 26 is a flowchart 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 measuring device 1 executes the procedure shown in FIG. 26.

[0160] As shown in FIG. 26, first, in the target data generation step S110, the measuring device 1 acquires the acceleration data A s (k) which is the observation data, and generates the target data M s (k). Specifically, the measuring device 1 performs the calculations of the above Equations (14) and (15) to generate the target data M s (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. 23.

[0161] Next, in the high-pass filter processing step S120, the measuring device 1 performs high-pass filter processing on the target data M s (k) including the drift noise generated in step S110 to generate displacement data MU(k) as drift noise-reduced data with reduced drift noise. The processing of the high-pass filter processing step S120 is the same as the processing of the high-pass filter processing step S2 in FIG. 23.

[0162] Next, in the section specifying step S130, the measuring device 1 calculates the first peak p1 = (k1, mu1) and the second peak p2 = (k2, mu2) of the displacement data MU(k) generated in step S120, and determines the first section T1 before the first peak p1, the second section T2 between the first peak p1 and the second peak p2, and the third section T3 after the second peak p2. That is, the first section T1 is the section where k ≤ k1, the second section T2 is the section where k1 < k < k2, and the third section T3 is the section where k2 ≤ k. The processing of the section specifying step S130 is the same as the processing of the section specifying step S31 in FIG. 24. is the same as the processing of the specifying step S31.

[0163] Next, in the correction data estimation step S140, the measuring device 1 generates correction data M s (k) corresponding to the difference between the data obtained by removing the drift noise from the target data M CC2 (k) and the displacement data MU(k) in the second section T2. Specifically, the measuring device 1 performs the calculations of the above-described equations (20) to (23) to generate correction data M CC2 (k).

[0164] Next, in the measurement data generation step S150, the measuring device 1 sets the first section T1 to 0, adds the displacement data MU(k) generated in step S120 and the correction data M CC2 (k) generated in step S140 in the second section T2, sets the third section T3 to 0, and generates measurement data RU(k).

[0165] Next, in a measurement data output step S160, the measurement device 1 outputs the measurement data RU(k) generated in step S150 to the monitoring device 3. Specifically, the measurement device 1 transmits the measurement data RU(k) to the monitoring device 3 via the communication network 4. The process of the measurement data output step S160 is the same as the process of the measurement data output step S5 in FIG.

[0166] Then, in step S170, the measuring device 1 repeats the processes of steps S110 to S160 until the measurement of the displacement of the superstructure 7 of the bridge 5 is completed.

[0167] FIG. 27 is a flowchart showing an example of the procedure of the correction data estimation step S140 of FIG.

[0168] As shown in FIG. 27, in step S141, the measurement device 1 calculates data MU(2k1-k) obtained by rearranging the displacement data MU(k) before the first peak p1 in reverse order to the data after the first peak p1 before a predetermined time in the second section T2, as in the above-mentioned formula (23), and calculates a straight line L passing through the first peak p1 and the second peak p2. C Linear data -2L obtained by multiplying (k) by -2 C (k) is added, and the displacement data MU(k) after the second peak p2 is rearranged in reverse order to data before the second peak p2 after a predetermined time in the second section T2, and the straight line data -2L C (k) is added to obtain the correction data M CC2 Generate (k).

[0169] Fig. 28 is a diagram showing an example of the configuration of the measurement device 1 in the second embodiment. As shown in Fig. 28, 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.

[0170] 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 high-pass filter processing unit 132, a correction data estimation unit 133, a measurement data generation unit 134, a measurement data output unit 135, and a section specification unit 136. That is, the processor 13 includes the target data generation unit 131, the high-pass filter processing unit 132, the correction data estimation unit 133, the measurement data generation unit 134, the measurement data output unit 135, and the section specification unit 136.

[0171] The functions of the target data generating unit 131, the high-pass filter processing unit 132, and the measurement data output unit 135 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. 26. The high-pass filter processing unit 132 performs the process of the high-pass filter processing step S120 in FIG. 26. The measurement data output unit 135 performs the process of the measurement data output step S16 in FIG. 26. Perform 0 processing.

[0172] The section specifying unit 136 calculates the first peak p1=(k1, mu1) and the second peak p2=(k2, mu2) of the displacement data MU(k) generated by the high-pass filter processing unit 132, and specifies a first section T1 before the first peak p1, a second section T2 between the first peak p1 and the second peak p2, and a third section T3 after the second peak p2. That is, the section specifying unit 136 performs the process of the section specifying step S130 in FIG. 26.

[0173] The correction data estimation unit 133 estimates the target 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 133 performs the calculations of the above-mentioned equations (20) to (23) to generate the correction data M CC2Specifically, the correction data estimation unit 133 generates data MU(2k1-k) obtained by rearranging the displacement data MU(k) before the first peak p1 in reverse order to data after the first peak p1 before a predetermined time in the second section T2, as in the above-mentioned formula (23), and a straight line L passing through the first peak p1 and the second peak p2. C Linear data -2L obtained by multiplying (k) by -2 C (k) is added, and the displacement data MU(k) after the second peak p2 is rearranged in reverse order to data before the second peak p2 after a predetermined time in the second section T2, and the straight line data -2L C (k) is added to obtain the correction data M CC2 Generate (k).

[0174] In this manner, the correction data estimation unit 133 performs the process of the correction data estimation step S140 in FIG. 26, specifically, the process of step S141 in FIG.

[0175] As in the above-mentioned formula (29), the measurement data generation unit 134 sets the first section T1 to 0, and in the second section T2, the displacement data MU(k) generated by the high-pass filter processing unit 132 and the correction data M CC2 26. The measured data RU(k) generated by the measured data generating unit 134 is stored in the storage unit 14 as measured data 143.

[0176] 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.

[0177] In the measurement method of the second embodiment described above, the measurement device 1 processes target data M s(k) is used to generate displacement data MU(k) with reduced drift noise, and 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 target 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 sets the first section T1 and the third section T3 to 0, and in the second section T2, the measurement device 1 sets the displacement data MU(k) and the correction data M CC By adding (k) to the target data M s According to the measurement method of the second embodiment, the measurement device 1 can generate measurement data RU(k) in which drift noise is reduced with respect to the target data M s (k), the displacement data MU(k) and the correction data M CC2 (k) is generated, and the displacement data MU(k) and the correction data M CC2 By adding RU(k) to the measurement data RU(k), it is possible to generate measurement data RU(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 RU(k) with high accuracy regardless of changes in the environment, and to reduce costs.

[0178] 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 Since the addition with (k) is not necessary, the amount of calculation is reduced.

[0179] In particular, according to the measurement method of the second embodiment, the measurement device 1 measures the target data Ms Based on the characteristics of the displacement data MU(k) in which the drift noise is reduced relative to the displacement data MU(k), more appropriate correction data M CC2 (k) can be generated, so the generated correction data M CC2 The estimation accuracy of (k) can be improved.

[0180] 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.

[0181] 3. Variations The present invention is not limited to the present embodiment, and various modifications are possible within the scope of the present invention.

[0182] In each of the above embodiments, the observation device receives acceleration data A s Sensor 2 outputs (k), and the target data is acceleration data A s The target data M obtained by integrating (k) twice s (k), 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.

[0183] As an example, Fig. 29 shows a configuration example of a measurement system 10 using a ring-type displacement meter as the observation device. Also, Fig. 30 shows a configuration example of a measurement system 10 using a displacement measuring device using image processing as the observation device. In Figs. 29 and 30, the same components as in Fig. 1 are given the same reference numerals, and their explanation will be omitted. In the measurement system 10 shown in Fig. 29, a piano wire 41 is fixed between the upper surface of a ring-type displacement meter 40 and the lower surface of a main girder G directly above it, and the ring-type displacement meter 40 measures the displacement of the piano wire 41 due to the bending of the superstructure 7, and the measured target data M s (k) is transmitted to the measuring device 1. The measuring device 1 transmits the target data M s 30, a camera 50 captures an image of a target 51 provided on a side surface of a main girder G and transmits the image to the measurement device 1. The measurement device 1 processes the image transmitted from the camera 50, calculates the displacement of the target 51 due to the deflection of the superstructure 7, and generates the target data M s (k) is generated, and the generated target data M s In the example of FIG. 30, the measurement device 1 generates measurement data RU(k) by removing drift noise from the target data M s (k) is generated by a displacement measuring device (not shown) different from the measuring device 1, which processes the image to generate the target data M s (k) may be generated.

[0184] 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. 31 shows a configuration example of the measurement system 10 when the bridge moves. In FIG. 31, the same components as those in FIG. 1 are denoted with the same reference numerals. As shown in FIG. 31, a road bridge 5 is composed of a superstructure 7 and a substructure 8, as in a railway bridge. FIG. 32 is a cross-sectional view of the superstructure 7 cut along the line AA in FIG. 31. As shown in FIGS. 31 and 32, the superstructure 7 includes a bridge deck 7a composed of a deck plate F, a main girder G, a cross beam (not shown), and a support 7b. Also, as shown in FIG. 31, 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. The bridge 5 is, for example, a steel bridge, a girder bridge, an RC bridge, or the like.

[0185] 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 31 and 32, each sensor 2 is installed on the main girder G of the superstructure 7.

[0186] As shown in FIG. 32, the superstructure 7 has two lanes L1, L2 and three main girders G on which the vehicle 6a, which is a moving body, can move. In the example of FIG. 31 and FIG. 32, a sensor 2 is provided on each of the two main girders at both ends in the longitudinal center of the superstructure 7, and an observation point R1 is provided at a position on the surface of the lane L1 vertically above one of the sensors 2, and an observation point R2 is provided at a position on the surface of the lane L2 vertically above the other sensor 2. That is, the two sensors 2 are observation devices that observe the observation points R1, R2, respectively. The two sensors 2 that observe the observation points R1, R2, respectively, may be provided at positions that can detect the acceleration generated at the observation points R1, R2 due to the running of the vehicle 6a, but are preferably provided at positions close to the observation points R1, R2. The number and installation positions of the sensors 2 and the number of lanes are not limited to the examples shown in FIG. 31 and FIG. 32, and various modifications are possible.

[0187] The measuring device 1 calculates the displacement of the flexure of the lanes L1, L2 caused by the travel of the vehicle 6a based on the acceleration data output from each sensor 2, and transmits information on the displacement of the lanes L1, L2 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 abnormalities in the superstructure 7 based on the information, for example.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] The following can be derived from the above-described embodiment and modifications.

[0193] One aspect of the measurement method is a high-pass filter processing step of high-pass filtering target data including drift noise to generate drift noise reduced data in which the drift noise is reduced; a correction data estimation step of estimating correction data corresponding to a difference between the drift noise reduced data and data obtained by removing the drift noise from the target data, based on the drift noise reduced data; and generating measurement data by adding the drift noise reduced data and the correction data.

[0194] In this measurement method, drift noise reduced data in which drift noise is reduced is generated using target data to be processed, and correction data is estimated based on the drift noise reduced data. The correction data corresponds to the difference between the data obtained by removing drift noise from the target data and the drift noise reduced data, and therefore includes 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 and the correction data. Also, according to this measurement method, drift noise reduced data and correction data are generated using target data to be processed, and the drift noise reduced data and the correction data are added, so that measurement data in which drift noise is reduced can be generated without preparing information for reducing drift noise in advance. Therefore, by using this measurement method, accurate measurement data can be obtained regardless of changes in the environment, and costs can be reduced.

[0195] 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; Adding the first interval correction data, the second interval correction data, and the third interval correction data and generating the correction data by using the calculated correction data.

[0196] According to this measurement method, three sections can be identified based on the characteristics of drift noise reduced data in which drift noise has been 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.

[0197] Another aspect of the measurement method is a high-pass filter processing step of high-pass filtering target data including drift noise 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 estimation 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 target data in the second section based on the drift noise reduced data; and a measurement data generating step of setting the first section to 0, adding the drift noise reduced data and the correction data in the second section, setting the third section to 0, and generating measurement data.

[0198] In this measurement method, drift noise reduced data in which drift noise is reduced is generated using target data to be processed, three sections are identified based on the characteristics of the drift noise reduced data, and correction data is estimated in the second section. The correction data corresponds to the difference between the data obtained by removing drift noise from the target data and the drift noise reduced data in the second section, and therefore contains significant signal components removed by high-pass filter processing. Therefore, according to this measurement method, measurement data in which drift noise is reduced relative to the target data can be generated by setting the first and third sections to 0 and adding the drift noise reduced data and the correction data in the second section. Also, according to this measurement method, drift noise reduced data and correction data are generated using target data to be processed, and the drift noise reduced data and the correction data are added in the second section, so that measurement data in which drift noise is reduced can be generated without preparing information for reducing drift noise in advance. Therefore, by using this measurement method, accurate measurement data can be obtained regardless of changes in the environment, and costs can be reduced.

[0199] Furthermore, according to this measurement method, since it is not necessary to generate correction data or add the drift noise reduction data and the correction data in the first and third sections to generate the measurement data, the amount of calculation is reduced.

[0200] 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.

[0201] According to this measurement method, more appropriate correction data is generated in the second section based on the characteristics of the drift noise reduced data in which the drift noise of the target data is reduced. This makes it possible to improve the estimation accuracy of the generated correction data.

[0202] In one embodiment of the measurement method, The high-pass filter process may be a process of subtracting, from the target data, data obtained by performing a moving average process or an FIR filter process on the target data.

[0203] 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 target data is constant in moving average processing or FIR filter processing, correction data can be estimated with high accuracy.

[0204] 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.

[0205] 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.

[0206] In one embodiment of the measurement method, The target data may be data obtained by integrating twice the acceleration in a direction intersecting with the plane of the structure in which the moving body moves.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] In one embodiment of the measurement method, The structure may be a bridge superstructure.

[0211] According to this measurement method, the displacement of the superstructure of a bridge can be measured with high accuracy.

[0212] 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.

[0213] According to this measurement method, by setting the cutoff frequency of the high-pass filter processing to be 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.

[0214] In one embodiment of the measurement method, The moving object may be a vehicle or a railcar.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] In one embodiment of the measurement method, The waveform may be a square waveform, a trapezoidal waveform or a half-sine waveform.

[0219] 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.

[0220] One aspect of the measurement device is a high-pass filter processing unit that performs high-pass filter processing on target data including drift noise to generate drift noise reduced data in which the drift noise is reduced; 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 target data, based on the drift noise reduced data; and a measurement data generating unit that generates measurement data by adding the drift noise reduced data and the correction data.

[0221] This measurement device uses target data to be processed to generate drift noise reduced data in which drift noise has been reduced, and estimates correction data based on the drift noise reduced data. The correction data corresponds to the difference between the data obtained by removing drift noise from the target data and the drift noise reduced data, and therefore includes significant signal components removed by high-pass filter processing. Therefore, this measurement device can generate measurement data in which drift noise has been reduced compared to the target data by adding the drift noise reduced data and the correction data. Furthermore, this measurement device can generate drift noise reduced data and correction data using target data to be processed, and add the drift noise reduced data and the correction data to generate measurement data in which drift noise has been reduced without having to prepare information for reducing drift noise in advance. Therefore, by using this measurement device, it is possible to obtain accurate measurement data regardless of changes in the environment, and to reduce costs.

[0222] 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.

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

[0224] In this measurement program, drift noise reduced data in which drift noise is reduced is generated using the target data to be processed, and correction data is estimated based on the drift noise reduced data. The correction data corresponds to the difference between the data obtained by removing drift noise from the target data and the drift noise reduced data, and therefore includes 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 and the correction data. Also, according to this measurement program, drift noise reduced data and correction data are generated using the target data to be processed, and the drift noise reduced data and the correction data are added, so that measurement data in which drift noise is reduced can be generated without preparing information for reducing drift noise in advance. Therefore, by using this measurement program, accurate measurement data can be obtained regardless of changes in the environment, and costs can be reduced. [Explanation of symbols]

[0225] 1...Measuring device, 2...Sensor, 3...Monitoring device, 4...Communication network, 5...Bridge, 6...Railway vehicle, 6a...Vehicle, 7...Superstructure, 7a...Bridge deck, 7b...Bearing, 7c...Rail, 7d...Sleeper, 7e...Ballast, F...Deck, 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, 3 4...operation unit, 35...memory unit, 40...ring-type displacement meter, 41...piano wire, 50...camera, 51...target, 131...object data generation unit, 132...high-pass filter processing unit, 133...correction data estimation unit, 134...measurement data generation unit, 135...measurement data output unit, 136...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 high-pass filter processing step of high-pass filtering target data including drift noise to generate drift noise reduced data in which the drift noise is reduced; a correction data estimation step of estimating correction data corresponding to a difference between data obtained by subtracting the drift noise from the target data and the drift noise reduced data, based on the drift noise reduced data; a measurement data generating step of generating measurement data by adding the drift noise reduced data and the correction data, 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; 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.

2. a high-pass filter processing step of high-pass filtering target data including drift noise to generate drift noise reduced data in which the drift noise is reduced; A first peak and a second peak of the drift noise reduced data are calculated, and a first section before the first peak, a second section between the first peak and the second peak, and the second peak are calculated. a section identification step of identifying a third section following the first section; a correction data estimation step of estimating correction data corresponding to a difference between data obtained by subtracting the drift noise from the target data and the drift noise reduced data in the second section, based on the drift noise reduced data; a measurement data generating step of generating measurement data by setting the first section to 0, adding the drift noise reduced data and the correction data in the second section, and setting the third section to 0; In the correction data estimation step, the measurement method including: 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 the correction data.

3. In claim 1 or 2, The measurement method, wherein the high-pass filter processing is a process of subtracting data obtained by performing moving average processing or FIR filter processing on the target data from the target data.

4. In any one of claims 1 to 3, A measurement method, wherein the target data is data on the displacement of a structure caused by a moving object moving across the structure.

5. In claim 4, A measurement method, wherein the target data is data obtained by integrating twice the acceleration in a direction intersecting with the surface of the structure in which the moving body moves.

6. In claim 4, A measurement method in which the target data is 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.

7. In any one of claims 4 to 6, The measurement method, wherein the structure is a superstructure of a bridge.

8. In claim 7, A measurement method, wherein the frequency of the drift noise is lower than the minimum natural vibration frequency of the superstructure.

9. In any one of claims 4 to 8, The measuring method, wherein the moving object is a vehicle or a railway vehicle.

10. In any one of claims 1 to 9, A measurement method, wherein the target data includes data of a waveform that is convex in a positive or negative direction.

11. In claim 10, The measurement method, wherein the waveform is a rectangular waveform, a trapezoidal waveform, or a half-sine waveform.

12. a high-pass filter processing unit that performs high-pass filter processing on target data including drift noise to generate drift noise reduced data in which the drift noise is reduced; a correction data estimation unit that estimates correction data corresponding to a difference between data obtained by subtracting the drift noise from the target data and the drift noise reduced data, based on the drift noise reduced data; a measurement data generating unit that generates measurement data by adding the drift noise reduction data and the correction data, The correction data estimation unit calculating a first peak and a second peak of the drift noise reduced data, and identifying 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; Inverting a sign of the drift noise reduction data in the first section to generate first section correction data; before a predetermined time in the second section, adding 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 after the predetermined time in the second section, adding 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 section correction data; Inverting a sign of the drift noise reduction data in the third section to generate third section correction data; A measurement device that generates the correction data by adding the first interval correction data, the second interval correction data, and the third interval correction data.

13. A high-pass filter processing unit that performs high-pass filter processing on target data including drift noise to generate drift noise reduced data in which the drift noise is reduced; a section specifying unit that calculates a first peak and a second peak of the drift noise reduced data and specifies 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 estimation unit that estimates correction data corresponding to a difference between data obtained by subtracting the drift noise from the target data and the drift noise reduced data in the second section based on the drift noise reduced data; a measurement data generating unit that sets the first section to 0, adds the drift noise reduction data and the correction data in the second section, and sets the third section to 0 to generate measurement data, The correction data estimation unit the measurement device adds, 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 adds, 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 the correction data.

14. The measuring device according to claim 12 or 13, An observation device for observing an observation point, A measurement system, wherein the target data is data based on observation data obtained by the observation device.

15. a high-pass filter processing step of high-pass filtering target data including drift noise to generate drift noise reduced data in which the drift noise is reduced; Based on the drift noise reduced data, correction data corresponding to a difference between the data obtained by subtracting the drift noise from the target data and the drift noise reduced data is estimated. A correction data estimation step; a measurement data generating step of generating measurement data by adding the drift noise reduction data and the correction data; 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; 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.

16. A high-pass filter processing step of high-pass filtering target data including drift noise 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 estimation step of estimating correction data corresponding to a difference between data obtained by subtracting the drift noise from the target data and the drift noise reduced data in the second section, based on the drift noise reduced data; a measurement data generating step of generating measurement data by setting the first section to 0, adding the drift noise reduction data and the correction data in the second section, and setting the third section to 0; In the correction data estimation step, the measurement program generating the correction data by 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 by 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.

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