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

Abstract

To provide a measurement method that can reduce drift caused by performing integration processing on data based on observation data when a railway vehicle travels on a bridge.SOLUTION: A measurement method comprises: an observation data acquisition step of acquiring observation data which is output from an observation device that observes an observation point of a bridge and includes a response to an action of a railway vehicle traveling on the bridge on the observation point; a fundamental frequency calculation step of calculating, on the basis of first measurement data based on the observation data, a fundamental frequency of deflection repeatedly generated at the bridge due to the traveling of the railway vehicle; a filter processing step of performing filter processing on the first measurement data using a filter having a passband variably set according to the fundamental frequency to generate second measurement data; and an integration processing step of performing integration processing on the second measurement data to generate third measurement data.SELECTED DRAWING: Figure 26

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 deflection measuring device that uses an acceleration sensor attached to a railway bridge, sets the output of the acceleration sensor when the railway bridge is in an unloaded state as the zero point of acceleration, corrects the zero point of acceleration output by the acceleration sensor when the railway bridge is in a loaded state, and, after the zero point correction, suppresses drift and estimates the amount of deflection of the railway bridge by applying double integration, Bayesian estimation, Kalman filter, etc. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2019-049095 Summary of the Invention [Problem to be solved by the invention]

[0004] However, in FIG. 3C of Patent Document 1, the displacement is higher in the section where the railway bridge is in a loaded state than in the section where it is not loaded. However, it is clear that the expected displacement waveform is one in which the displacement in the section where the railway bridge is in a loaded state is lower than in the section where it is not loaded. This is similar to the result of suppressing the low-frequency signal components of the displacement waveform along with the low-frequency drift components. Therefore, the method of estimating the amount of deflection using the deflection measuring device described in Patent Document 1 suppresses the low-frequency components of the displacement waveform along with the drift, which may make it impossible to accurately estimate the actual displacement amplitude. [Means for solving the problem]

[0005] One aspect of the measurement method according to the present invention is to an observation data acquisition step of acquiring observation data output from an observation device that observes an observation point on a bridge, the observation data including a response to an action on the observation point by a railway vehicle traveling on the bridge; a fundamental frequency calculation step of calculating a fundamental frequency of deflection repeatedly occurring in the bridge due to the running of the railway vehicle based on first measurement data based on the observation data; a filtering process for filtering the first measurement data using a filter whose passband is set to be variable according to the fundamental frequency, to generate second measurement data; an integration processing step of performing integration processing on the second measurement data to generate third measurement data; Includes:

[0006] One aspect of the measuring device according to the present invention is an observation data acquisition unit that acquires observation data output from an observation device that observes an observation point on a bridge, the observation data including a response to an action on the observation point by a railway vehicle traveling on the bridge; a fundamental frequency calculation unit that calculates a fundamental frequency of deflection repeatedly occurring in the bridge due to the running of the railway vehicle based on first measurement data that is based on the observation data; a filter processing unit that performs filtering on the first measurement data using a filter whose passband is set to be variable according to the fundamental frequency, to generate second measurement data; an integration processing unit that performs integration processing on the second measurement data to generate third measurement data; Includes:

[0007] One aspect of the measurement system according to the present invention is One aspect of the measurement device; the observation device; Equipped with.

[0008] One aspect of the measurement program according to the present invention is an observation data acquisition step of acquiring observation data output from an observation device that observes an observation point on a bridge, the observation data including a response to an action on the observation point by a railway vehicle traveling on the bridge; a fundamental frequency calculation step of calculating a fundamental frequency of deflection repeatedly occurring in the bridge due to the running of the railway vehicle based on first measurement data based on the observation data; a filtering process for filtering the first measurement data using a filter whose passband is set to be variable according to the fundamental frequency, to generate second measurement data; an integration processing step of performing integration processing on the second measurement data to generate third measurement data; to be executed by the computer. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram showing an example of the configuration of a measurement system. [Figure 2] A cross-sectional view of the superstructure in Figure 1 taken along line AA. [Figure 3] FIG. 3 is an explanatory diagram of acceleration detected by an acceleration sensor. [Figure 4] FIG. 10 is a diagram showing an example of acceleration α(t) when a railway vehicle travels on a bridge. [Figure 5] A diagram showing the displacement u(t) obtained by integrating acceleration α(t) twice. [Figure 6] FIG. 10 is a diagram showing an example of a displacement waveform of a bridge when the passing times ts are different from each other. [Figure 7] FIG. 7 is a diagram showing a frequency spectrum obtained by performing a fast Fourier transform on the displacement waveform of FIG. 6. [Figure 8] FIG. 10 is a diagram showing an example of a bridge displacement waveform when the number of vehicles is different. [Figure 9] FIG. 9 is a diagram showing a frequency spectrum obtained by performing a fast Fourier transform on the displacement waveform of FIG. 8. [Figure 10] FIG. 10 is a diagram showing an example of a displacement waveform of a bridge when the bridge lengths are different from each other. [Figure 11] FIG. 11 is a diagram showing a frequency spectrum obtained by performing a fast Fourier transform on the displacement waveform of FIG. 10 . [Figure 12] FIG. 10 is a diagram showing an example of the gain frequency characteristic of a high-pass filter. [Figure 13] FIG. 10 is a diagram showing an example of acceleration α(t). [Figure 14] FIG. 14 is a diagram showing a frequency spectrum obtained by performing a fast Fourier transform on the acceleration α(t) in FIG. 13. [Figure 15] A graph showing the velocity v(t) obtained by integrating the acceleration after filtering. [Figure 16] A diagram showing the displacement u(t) obtained by integrating the filtered acceleration twice. [Figure 17] FIG. 10 is a diagram showing an example of acceleration α(t). [Figure 18] FIG. 18 is a diagram showing a frequency spectrum obtained by performing a fast Fourier transform on the acceleration α(t) in FIG. 17. [Figure 19] FIG. 10 is a diagram showing the relationship between acceleration α(t) and transit time ts. [Figure 20] FIG. 1 is a diagram showing an example of the vehicle length LC (Cm) and the distance between the axles La (aw(Cm,n)). [Figure 21] 20 is a diagram showing acceleration αLPF(t) obtained by low-pass filtering the acceleration α(t) in FIG. 19. [Figure 22] FIG. 10 is a diagram showing an example of the gain frequency characteristic of a high-pass filter. [Figure 23] FIG. 14 is a diagram showing a frequency spectrum obtained by performing a fast Fourier transform on the acceleration α(t) in FIG. 13 after filtering the acceleration α(t) using a high-pass filter. [Figure 24] 24 is a graph showing the velocity v(t) obtained by integrating the acceleration after filtering the acceleration α(t) of FIG. 13 using a high-pass filter with the characteristics of FIG. 22. [Figure 25] 23 is a graph showing the displacement u(t) obtained by integrating the acceleration twice after filtering the acceleration α(t) of FIG. 13 using a high-pass filter with the characteristics of FIG. 22. [Figure 26] FIG. 3 is a flowchart showing an example of the procedure of the measurement method according to the first embodiment. [Figure 27] FIG. 10 is a flowchart showing an example of a procedure for calculating a fundamental frequency. [Figure 28] FIG. 10 is a flowchart showing another example of the procedure of the fundamental frequency calculation step. [Figure 29]FIG. 10 is a flowchart showing another example of the procedure of the fundamental frequency calculation step. [Figure 30] FIG. 1 is a diagram showing an example of the configuration of a sensor, a measuring device, and a monitoring device. [Figure 31] FIG. 10 is a flowchart showing an example of the procedure of a measurement method according to a second embodiment. [Figure 32] FIG. 10 is a diagram showing an example of the arrangement of a measurement device according to a second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Note that the embodiments described below do not unduly limit the content of the present invention as defined in the claims. Furthermore, not all of the configurations described below are necessarily essential components of the present invention.

[0011] 1. First embodiment 1-1. Measurement system configuration Railway vehicles passing over bridges are heavy and can be measured using BWIM. BWIM stands for Bridge Weigh in Motion, and is a technology that measures the weight and number of axles of railway vehicles passing over a bridge by treating the bridge as a "scale" and measuring the deformation of the bridge. Bridges that can analyze the weight of passing railway vehicles from responses such as deformation and strain are structures where BWIM can function, and the BWIM system, which applies the physical process between the action and response on the bridge, makes it possible to measure the weight of passing railway vehicles.

[0012] Fig. 1 is a diagram showing an example of a measurement system according to this embodiment. As shown in Fig. 1, the measurement system 10 according to this embodiment includes a measurement device 1 and at least one sensor 2 provided on a bridge 5. The measurement system 10 may also include a monitoring device 3.

[0013] The bridge 5 comprises a superstructure 7 and a substructure 8. Figure 2 is a cross-sectional view of the superstructure 7 taken along line AA in Figure 1. As shown in Figures 1 and 2, the superstructure 7 includes a bridge deck 7a consisting of deck plates F, main girders G, and crossbeams (not shown), as well as bearings 7b, rails 7c, sleepers 7d, and ballast 7e. As shown in Figure 1, the substructure 8 includes piers 8a and abutments 8b. The superstructure 7 is a structure spanning either adjacent abutments 8b and piers 8a, two adjacent abutments 8b, or two adjacent piers 8a. Both ends of the superstructure 7 are located at the positions of adjacent abutments 8b and piers 8a, two adjacent abutments 8b, or two adjacent piers 8a.

[0014] When a railway vehicle 6 enters the superstructure 7 of the bridge 5, the weight of the railway vehicle 6 causes the superstructure 7 to bend, but since the railway vehicle 6 is made up of multiple cars connected together, the bending of the superstructure 7 is repeated periodically as each car passes.

[0015] The measuring device 1 and each sensor 2 are connected by, for example, a cable (not shown) and communicate with each other via a communication network such as a CAN. CAN is an abbreviation for Controller Area Network. Alternatively, the measurement device 1 and each sensor 2 may communicate via a wireless network.

[0016] Each sensor 2 outputs observation data including physical quantities that occur when the railcar 6 travels across the bridge 5. In this embodiment, each sensor 2 is an acceleration sensor, and outputs acceleration data including acceleration that occurs when the railcar 6 travels across the bridge 5. Each sensor 2 may be, for example, a quartz acceleration sensor or a MEMS acceleration sensor. MEMS is an abbreviation for Micro Electro Mechanical Systems.

[0017] In this embodiment, each sensor 2 is installed in the longitudinal center of the superstructure 7 of the bridge 5, specifically, in the longitudinal center of the main girder G. However, each sensor 2 only needs to be able to detect acceleration generated by the running of the railway vehicle 6, and its installation position is not limited to the center of the superstructure 7. If each sensor 2 were installed on the deck F of the superstructure 7, there would be a risk of it being destroyed by the running of the railway vehicle 6, and there would also be a risk that measurement accuracy would be affected by local deformation of the bridge deck 7a. Therefore, in the example of Figures 1 and 2, each sensor 2 is installed on the main girder G of the superstructure 7.

[0018] The deck F, main girders G, etc. of the superstructure 7 are deflected vertically by the load of the railway vehicle 6 passing over the bridge 5. Each sensor 2 detects the acceleration of the deflection of the deck F and main girders G due to the load of the railway vehicle 6 passing over the bridge 5.

[0019] The measuring device 1 calculates the displacement of the bridge 5 when the railway vehicle 6 passes over the bridge 5 based on the acceleration data output from each sensor 2. The displacement of the bridge 5 is specifically the displacement of the superstructure 7 that is the measurement target. The measuring device 1 is installed, for example, on an abutment 8b.

[0020] The measuring device 1 and the monitoring device 3 can communicate with each other via a communication network 4, such as a wireless mobile phone network or the Internet. The measuring device 1 transmits measurement data including the displacement of the bridge 5 when a railway vehicle 6 passes over the bridge 5 to the monitoring device 3. The monitoring device 3 stores the measurement data in a storage device (not shown), and may perform processing such as monitoring the railway vehicle 6 and determining abnormalities in the superstructure 7 based on the displacement of the bridge 5 included in the measurement data.

[0021] In this embodiment, the bridge 5 is a railway bridge, such as a steel bridge, a girder bridge, or an RC bridge, etc. RC is an abbreviation for Reinforced Concrete.

[0022] As shown in FIG. 2 , in this embodiment, an observation point R is set in association with a 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 installed on the main girder G. That is, the sensor 2 is an observation device that observes the observation point R, detects physical quantities that are responses to the actions of multiple parts of the railway vehicle 6 traveling on the bridge 5 on the observation point R, and outputs observation data including the detected physical quantities. For example, each of the multiple parts of the railway vehicle 6 is an axle or a wheel, but hereinafter, it will be assumed to be an axle. Furthermore, in this embodiment, each sensor 2 is an acceleration sensor that detects acceleration as a physical quantity. The sensor 2 may be installed at a position where it can detect the acceleration occurring at the observation point R due to the traveling of the railway vehicle 6, but it is preferable that the sensor 2 be installed at a position close to the vertical line of the observation point R.

[0023] 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.

[0024] The measurement device 1 acquires acceleration in a direction intersecting with the plane of the superstructure 7 of the bridge 5 on which the railroad vehicle 6 travels, based on the observation data output from the sensor 2. The plane of the superstructure 7 on which the railroad vehicle 6 travels is divided into an X direction, which is the direction in which the railroad vehicle 6 travels, i.e., the longitudinal direction of the superstructure 7, and a Y direction, which is the direction perpendicular to the direction in which the railroad vehicle 6 travels, i.e., the width direction of the superstructure 7. As the railway vehicle 6 travels, the observation point R bends in a direction perpendicular to the X and Y directions. Therefore, in order to accurately calculate the magnitude of the acceleration of the bending, it is desirable for the measurement device 1 to acquire the acceleration in the direction perpendicular to the X and Y directions, i.e., the Z direction which is the normal direction of the floor panel F.

[0025] 3 is a diagram illustrating acceleration detected by the sensor 2. The sensor 2 is an acceleration sensor that detects acceleration occurring in each of three mutually orthogonal axial directions.

[0026] In order to detect the acceleration of the deflection at observation point R due to the movement of the railway vehicle 6, the sensor 2 is installed so that one of the three detection axes, the x-axis, y-axis, and z-axis, intersects with the X-direction and the Y-direction. Because 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 one 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.

[0027] However, when sensor 2 is installed on the superstructure 7, the installation location may be tilted. Even if one of the three detection axes of sensor 2 is not installed in the normal direction of 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 sensor 2 is not installed in the normal direction of floor board F, measurement device 1 can correct the detection error due to the tilt of sensor 2 by using a three-axis resultant acceleration that is a combination of accelerations on the x-, y-, and z-axes. Furthermore, sensor 2 may be a one-axis acceleration sensor that detects at least acceleration occurring in a direction approximately parallel to the vertical direction or acceleration in the normal direction of floor board F.

[0028] The measurement method of this embodiment executed by the measurement device 1 will be described in detail below.

[0029] 1-2. Details of measurement method When a railway vehicle 6 travels across a bridge 5, periodic acceleration in the direction of gravitational acceleration is generated at observation point R due to the load on each axle of the railway vehicle 6. Sensor 2 detects this acceleration as acceleration α(k) in the z-axis direction and outputs acceleration data containing acceleration α(k) in a time series. k is the sample number. If the time interval between samples is ΔT, the time series of acceleration α(k) is converted to acceleration α(t) with time t as a variable, where t = kΔT. Figure 4 shows two examples of acceleration α(t) when a railway vehicle 6 travels across the superstructure 7 of a bridge 5. In Figure 4, both the solid line and the dashed line represent acceleration α(t) when the railway vehicle 6 travels at a constant speed. However, the solid line has a shorter period during which acceleration α(t) oscillates than the dashed line because the railway vehicle 6 is traveling at a faster speed.

[0030] The velocity v(t) of the displacement of bridge 5 is obtained by integrating acceleration α(t), and the displacement u(t) of bridge 5 is obtained by integrating acceleration α(t) twice. However, offset and noise components contained in acceleration α(t) cause integration errors, resulting in drift of velocity v(t) and displacement u(t). In particular, double integration increases integration errors, resulting in significant drift of displacement u(t). Figure 5 shows the displacement u(t) obtained by integrating twice each of the two accelerations α(t) shown in Figure 4. In Figure 5, the solid line represents the displacement u(t) obtained by integrating twice the acceleration α(t) shown as a solid line in Figure 4, and the dashed line represents the displacement u(t) obtained by integrating twice the acceleration α(t) shown as a dashed line in Figure 4.

[0031] The offset and noise components that cause drift are in the low frequency range, so high-pass filtering is performed on the acceleration α(t) to reduce this drift. The cutoff frequency of the high-pass filter is set to a frequency lower than the lowest frequency of the necessary signal components so that the signal components necessary for measurement, which are included in the acceleration α(t), are not reduced by the high-pass filtering. For example, the lowest frequency of the signal components necessary for measurement is the frequency of the railway The fundamental frequency f of the deflection repeatedly generated in the bridge 5 by the running of the vehicle 6 c Generally, the fundamental frequency f c is the maximum intensity signal component contained in the acceleration α(t).

[0032] Also, the fundamental frequency f c If the signal components required for measurement are the 2nd to nth harmonic signal components, which are 2 to n times the fundamental frequency f cSignal components and noise components with frequencies higher than n times α(t) are not signal components required for measurement. n is a predetermined integer greater than or equal to 2 and is set to an appropriate value in advance depending on the purpose of the measurement. For example, n may be set to 5. The natural resonant frequency of the structure of bridge 5 may result in high-frequency signal components that are not required for measurement. In order to reduce high-frequency signal components and noise components without reducing the nth-order harmonic components, low-pass filtering may be performed on acceleration α(t). When high-pass filtering and low-pass filtering are performed, band-pass filtering is performed as a result. Alternatively, band-pass filtering may be performed directly on acceleration α(t).

[0033] On the other hand, the waveform of the displacement of the bridge 5 when the railway vehicle 6 runs on the bridge 5 is the time it takes for the railway vehicle 6 to pass through the superstructure 7 of the bridge 5, which is the passing time t s , number of railcars 6 C T , bridge length L B Bridge length L B is the length of the bridge 5, which in this embodiment is the distance between the approach end and the exit end of the superstructure 7. For example, if the bridge 5 has multiple superstructures 7, the bridge length L B is the distance between the entrance end and exit end of each superstructure 7.

[0034] Figure 6 shows the transit time t s 6 shows two examples of the displacement waveform of the bridge 5 when the passing time t s 11 seconds, number of vehicles C T 12 units, bridge length L B The dashed line shows the displacement waveform when the passage time t s 15 seconds, number of vehicles C T 12 units, bridge length L B6. In addition, Fig. 7 shows the frequency spectrum obtained by fast Fourier transforming the two displacement waveforms shown in Fig. 6. In Fig. 7, the solid line is the frequency spectrum obtained by fast Fourier transforming the displacement waveform shown by the solid line in Fig. 6, and the dashed line is the frequency spectrum obtained by fast Fourier transforming the displacement waveform shown by the dashed line in Fig. 6. As shown in Fig. 6, the vibration period of the displacement waveform of the solid line is shorter than that of the dashed line, and therefore, as shown in Fig. 7, the solid line has a lower fundamental frequency f c and fundamental frequency f c The frequency is 2 to n times higher.

[0035] Figure 8 shows the number of vehicles C T 8 shows two examples of the displacement waveform of the bridge 5 when the passing time t s 11 seconds, number of vehicles C T 12 units, bridge length L B The dashed line shows the displacement waveform when the passage time t s 11 seconds, number of vehicles C T 8 units, bridge length L B 8 is a displacement waveform when the displacement distance is 12 m. Also, Fig. 9 shows the frequency spectrum obtained by fast Fourier transforming the two displacement waveforms shown in Fig. 8. In Fig. 9, the solid line is the frequency spectrum obtained by fast Fourier transforming the displacement waveform shown by the solid line in Fig. 8, and the dashed line is the frequency spectrum obtained by fast Fourier transforming the displacement waveform shown by the dashed line in Fig. 8. As shown in Fig. 8, the vibration period of the displacement waveform of the solid line is shorter than that of the dashed line, and therefore, as shown in Fig. 9, the solid line has a lower fundamental frequency f c and fundamental frequency f c The frequency is 2 to n times higher.

[0036] Figure 10 shows the bridge length L B 10 shows two examples of the displacement waveform of the bridge 5 when the passing time t s 11 seconds, number of vehicles C T 8 units, bridge length L B The dashed line shows the displacement waveform when the passage time t s 11 seconds, number of vehicles C T8 units, bridge length L B 10. In addition, Fig. 11 shows the frequency spectrum obtained by fast Fourier transforming the two displacement waveforms shown in Fig. 10. In Fig. 11, the solid line is the frequency spectrum obtained by fast Fourier transforming the displacement waveform shown by the solid line in Fig. 10, and the dashed line is the frequency spectrum obtained by fast Fourier transforming the displacement waveform shown by the dashed line in Fig. 10. As shown in Fig. 10, the dashed line has a slightly shorter vibration period than the solid line. As shown in Figure 11, the dashed line has a shorter fundamental frequency f c and fundamental frequency f c The frequency is slightly higher at 2 to n times the frequency of the

[0037] Thus, the fundamental frequency f c and fundamental frequency f c The frequency 2 to n times the passing time t s , number of vehicles C T , bridge length L B It changes depending on the bridge length L for the bridge 5 to be measured. B does not change, but if multiple different types of railway vehicles 6 can travel on the bridge 5, the passing time t s and number of vehicles C T Therefore, the expected passing time t s and number of vehicles C T The fundamental frequency f is determined by the range c It is conceivable to perform filtering using a high-pass filter or a band-pass filter so that the entire range of t is included in the pass band. s and number of vehicles C T The fundamental frequency f is determined by the range c It is conceivable to perform filtering using a low-pass filter or band-pass filter whose passband includes all of the n-fold frequency range of the signal.

[0038] As an example, in the examples of FIGS. 6, 8 and 10, the fundamental frequency f cSince f varies in the range of 0.77 Hz to 1.13 Hz, it is conceivable to perform filtering using a high-pass filter whose passband lower limit is fixed at a predetermined frequency lower than 0.77 Hz. Figure 12 shows an example of the gain-frequency characteristic of such a high-pass filter. In the example of Figure 12, the passband lower limit is between 0.4 Hz and 0.5 Hz. Assume that filtering is performed using a high-pass filter with the characteristics shown in Figure 12 for the acceleration α(t) shown in Figure 13. Figure 14 shows, with a solid line, the frequency spectrum obtained by subjecting the acceleration α(t) shown in Figure 13 to a fast Fourier transform, and with a dashed line, the frequency spectrum obtained by subjecting the acceleration α(t) shown in Figure 13 after filtering is subject to a fast Fourier transform. As shown in Figure 14, the acceleration α(t) has a fundamental frequency f c = 1.13 Hz, and the offset and noise components at frequencies lower than around 0.4 Hz have been reduced by the filter processing. Fig. 15 shows the velocity v(t) obtained by integrating the acceleration after the filter processing. Fig. 16 shows the displacement u(t) obtained by integrating the acceleration twice after the filter processing. The velocity v(t) shown in Fig. 15 drifts slightly. The displacement u(t) shown in Fig. 16 has a reduced amount of drift compared to the displacement u(t) shown in Fig. 5, i.e., the displacement u(t) obtained by integrating the acceleration α(t) twice, but it cannot be said that the reduction is sufficient. The drift of the velocity v(t) shown in Fig. 15 and the displacement u(t) shown in Fig. 16 is due to the expected fundamental frequency f c The lower limit frequency of the passband is fixed so that the lowest frequency in the range of c This occurs because offset components and noise components in the frequency band between

[0039] Similarly, the assumed fundamental frequency f cEven if filtering is performed using a band-pass filter with a fixed lower limit frequency in the passband according to the lowest frequency in the range of c If filtering is performed using a low-pass filter or band-pass filter in which the upper limit frequency of the passband is fixed according to the highest frequency in the range of frequencies n times the acceleration (t), the high-frequency noise components contained in the acceleration (t) may not be sufficiently reduced.

[0040] Therefore, in this embodiment, in order to sufficiently reduce the offset component and noise component contained in the acceleration α(t), the measurement device 1 uses the fundamental frequency f c Calculate the fundamental frequency f c The acceleration α(t) is filtered using a filter whose passband is set to vary depending on the fundamental frequency f c There are three possible methods for calculating this.

[0041] fundamental frequency f c In the first calculation method, the measurement device 1 first calculates the acceleration α(t) at a high speed. FIG. 17 shows an example of acceleration α(t). FIG. 18 shows a frequency spectrum obtained by performing a fast Fourier transform on the acceleration α(t) shown in FIG. 17. Then, the measurement device 1 determines the lowest frequency among a plurality of frequencies corresponding to the respective peaks in the calculated frequency spectrum as the fundamental frequency f c In the example of Figure 18, the fundamental frequency f c This gives a calculated value of 0.77Hz.

[0042] According to this first calculation method, the measurement device 1 performs a fast Fourier transform, which increases the calculation load, but the fundamental frequency f c can be accurately calculated.

[0043] fundamental frequency f cIn the second calculation method, the measurement device 1 first calculates the passing time t when the railway vehicle 6 passes over the bridge 5 based on the acceleration α(t). s Figure 19 shows the relationship between acceleration α(t) and transit time t s As shown in FIG. 19, the measurement device 1 determines the time of the first negative peak of the acceleration α(t) as the entry time t i The time of the last negative peak of acceleration α(t) is calculated as the exit time t when the railway vehicle 6 exits the bridge 5. o Then, the measurement device 1 calculates the approach time t i From the exit time t o Time to pass through time t s It is calculated as follows.

[0044]

number

[0045] Next, the measuring device 1 calculates the transit time t s and environmental information including the dimensions of the railway vehicle 6 and the bridge 5, which have been created in advance, to calculate the fundamental frequency f c The environmental information includes the bridge length L B The environmental information also includes, for example, the number of railcars 6 C as the dimensions of the railcars 6. T , the length L of each railcar 6 C (C m ), the number of axles in each vehicle a T (C m ) and the distance between the axles of each vehicle La(a w (C m ,n)) is included. C m is the vehicle number, and the length of each vehicle L C (C m ) starts with C m The distance between the two ends of the th vehicle is the number of axles in each vehicle. T (C m ) starts with C m n is the axle number of each vehicle, and 1≦n≦a T (Cm ) The distance between the axles of each vehicle is La(a w (C m ,n)) is C from the beginning when n=1. m When n≧2, it is the distance between the n-1th axle from the front and the nth axle. m Length of the th vehicle L C (C m ) and the distance between the axles La(a w (C m ,n)) is shown below. The dimensions of the railway vehicle 6 can be measured by known methods. A database of the dimensions of railway vehicles 6 passing over the bridge 5 may be created in advance, and the dimensions of the relevant vehicle may be referenced based on the time of passing.

[0046] The measuring device 1 calculates the transit time t s and the number of vehicles included in the environmental information, C T , the length of each vehicle L C =L C (C m ) and bridge length L B Based on this, the fundamental frequency f c In equation (2), the sum of the distance from the front end of the railway vehicle 6 to the front axle of the leading vehicle and the distance from the rearmost axle of the rearmost vehicle to the rear end of the railway vehicle 6 is set to 4.1 m, but the measurement device 1 may also calculate this sum from environmental information.

[0047]

number

[0048] According to this second calculation method, the measurement device 1 does not need to perform a fast Fourier transform, so the fundamental frequency f c can be calculated with low load.

[0049] fundamental frequency f c In the third calculation method, the measurement device 1 first calculates the number of periods T of the acceleration α(t) based on the acceleration α(t), similarly to the second calculation method. nand the time t when railcar 6 passed through bridge 5. s Calculate the transit time t s The calculation method is the fundamental frequency f c For example, the measurement device 1 calculates the acceleration α(t) by low-pass filtering. LPF (t) and calculate the acceleration α LPF Number of positive peaks in (t) P p or negative peak number P n FIG. 21 shows the acceleration α(t) obtained by low-pass filtering the acceleration α(t) shown in FIG. LPF (t) is shown by a solid line. In FIG. 21, the acceleration α(t) is also shown by a dashed line. In the example of FIG. 21, the acceleration α LPF Number of positive peaks in (t) P p is 9, and the acceleration α LPF Number of negative peaks in (t) P n is 10. The measurement device 1 counts the number of positive peaks P p or negative peak number P n From equation (3), the number of periods T n Calculate.

[0050]

number

[0051] Then, the measurement device 1 calculates the number of periods T n and transit time t s and based on the fundamental frequency f c Specifically, the measurement device 1 calculates the number of periods T n The transit time t s By dividing by, we get the fundamental frequency f c Calculate.

[0052]

number

[0053] According to this third calculation method, the measurement device 1 does not need to perform a fast Fourier transform, so the fundamental frequency f ccan be calculated with low load.

[0054] Next, the measurement device 1 calculates the fundamental frequency f c Based on the above, a filter having a variable passband is generated. For example, the filter may be a high-pass filter or a band-pass filter. The band-pass filter may be configured by a high-pass filter and a low-pass filter. When the filter is a high-pass filter, the measurement device 1 generates a filter having a passband that is variable. c higher than half the frequency of the fundamental frequency f c That is, the measurement device 1 generates a filter having a fundamental frequency f c It passes the signal components above and at least the fundamental frequency f c For example, as shown in Figure 14, we generate a filter that attenuates the signal components with a frequency of 1 / 2 or less of the fundamental frequency f of the acceleration α(t) in Figure 13. c Since the frequency is 1.13 Hz, the measurement device 1 generates, for example, a high-pass filter whose cutoff frequency is higher than half of 1.17 Hz but lower than 1.17 Hz. An example of the gain-frequency characteristic of this high-pass filter is shown by a solid line in Figure 22. Note that in Figure 22, the gain-frequency characteristic of the high-pass filter shown in Figure 12 is also shown by a dashed line.

[0055] In addition, when the filter is a band-pass filter, the first cutoff frequency of the measuring device 1 is set to a fundamental frequency f c higher than half the frequency of the fundamental frequency f c The second cutoff frequency is lower than the fundamental frequency f c higher than n times the frequency of the fundamental frequency f c The measurement device 1 generates a filter having a frequency lower than n+1 times the fundamental frequency f c or more and the fundamental frequency f c It passes signal components with frequencies up to n times the fundamental frequency fc Signal components with frequencies less than half of the fundamental frequency f c Generates a filter that attenuates signal components with frequencies greater than or equal to n+1 times the input frequency.

[0056] Next, the measurement device 1 performs filtering on the acceleration α(t) using the generated filter. In FIG. 23, the solid line shows the frequency spectrum obtained by fast Fourier transforming the acceleration after filtering the acceleration α(t) shown in FIG. 13 using the high-pass filter with the characteristics shown by the solid line in FIG. 22. Note that FIG. 23 also shows, by dashed line, the frequency spectrum obtained by fast Fourier transforming the acceleration after filtering the acceleration α(t) shown in FIG. 13 using the high-pass filter with the characteristics shown by the dashed line in FIG. 22. As shown in FIG. 23, filtering the acceleration α(t) using the high-pass filter with the characteristics shown by the solid line in FIG. 22 attenuates offset components and noise components in the low frequency range more significantly than filtering using the high-pass filter with the characteristics shown by the dashed line in FIG. 22.

[0057] The measurement device 1 then performs integration on the acceleration obtained by filtering to calculate the velocity v(t) and displacement u(t). FIG. 24 shows, with a solid line, the velocity v(t) obtained by integrating the acceleration after filtering the acceleration α(t) shown in FIG. 13 using a high-pass filter with the characteristics shown by the solid line in FIG. 22. Note that FIG. 24 also shows, with a dashed line, the velocity v(t) obtained by integrating the acceleration after filtering the acceleration α(t) shown in FIG. 13 using a high-pass filter with the characteristics shown by the dashed line in FIG. 22. As shown in FIG. 24, the velocity v(t) shown by the solid line has less drift than the velocity v(t) shown by the dashed line. FIG. 25 shows, with a solid line, the displacement u(t) obtained by twice integrating the acceleration after filtering the acceleration α(t) shown in FIG. 13 using a high-pass filter with the characteristics shown by the solid line in FIG. 22. In addition, in Fig. 25, the displacement u(t) obtained by integrating the acceleration twice after filtering the acceleration α(t) shown in Fig. 13 using a high-pass filter with the characteristics shown by the dashed line in Fig. 22 is also shown by a dashed line. As shown in Fig. 25, the displacement u(t) shown by the solid line has a greater reduction in drift than the displacement u(t) shown by the dashed line.

[0058] In this manner, in this embodiment, the measurement device 1 detects the fundamental frequency f of the acceleration α(t) included in the observation data. c By filtering the acceleration α(t) using a filter with an appropriate passband set according to the acceleration, and then integrating the filtered acceleration, the drift of the velocity v(t) and displacement u(t) can be significantly reduced.

[0059] 1-3. Measurement procedure 26 is a flow chart showing an example of the procedure of the measurement method according to the first embodiment. In this embodiment, the measurement device 1 executes the procedure shown in FIG.

[0060] 26, first, in the observation data acquisition step S10, the measurement device 1 acquires observation data output from a sensor 2, which is an observation device. The observation data includes responses to the actions of multiple parts of a railway vehicle 6 traveling on a bridge 5 on an observation point R. In this embodiment, the sensor 2 is an acceleration sensor provided on the bridge 5, and the observation data includes acceleration as the response.

[0061] Next, in a fundamental frequency calculation step S20, the measurement device 1 calculates the fundamental frequency f of the deflection repeatedly occurring in the bridge 5 due to the running of the railway vehicle 6 based on the first measurement data based on the acceleration data, which is the observation data acquired in step S10. c The first measurement data may be the acceleration data itself, or may be data obtained by performing a predetermined process such as low-pass filtering on the acceleration data. An example of the procedure for the fundamental frequency calculation step S20 will be described later.

[0062] Next, in the filter generation step S30, the measurement device 1 generates the basic frequency f c Based on the fundamental frequency f c The measurement device 1 generates a filter whose passband is set variably according to the fundamental frequency f. For example, the filter may be a high-pass filter or a band-pass filter. The band-pass filter may be configured by a high-pass filter and a low-pass filter. When the filter is a high-pass filter, the measurement device 1 generates a filter whose passband is set variably according to the fundamental frequency f. c and the fundamental frequency f c Furthermore, when the filter is a band-pass filter, the measurement device 1 generates a filter having a first cutoff frequency lower than the fundamental frequency f c and the fundamental frequency f c The second cutoff frequency is lower than the fundamental frequency f c higher than n times the fundamental frequency f cThis generates a filter whose frequency is lower than n+1 times the frequency of the filter, where n is a predetermined integer.

[0063] Next, in a filtering process step S40, the measurement device 1 performs filtering on the first measurement data using the filter generated in step S30 to generate second measurement data. That is, the measurement device 1 performs filtering on the first measurement data using the high-pass filter or band-pass filter generated in step S30.

[0064] Next, in an integration process step S50, the measurement device 1 performs integration on the second measurement data generated in step S40 to generate third measurement data. For example, the second measurement data is acceleration data, and the measurement device 1 may integrate the second measurement data to generate velocity data as the third measurement data, or may integrate the second measurement data twice to generate displacement data as the third measurement data.

[0065] Next, in a measurement data output step S60, the measurement device 1 outputs measurement data including the third measurement data calculated in step S50 to the monitoring device 3. Specifically, the measurement device 1 transmits the measurement data to the monitoring device 3 via the communication network 4. The measurement data may further include first measurement data, second measurement data, etc.

[0066] Then, the measuring device 1 repeats the processes of steps S10 to S60 until the measurement is completed in step S70.

[0067] 27 is a flowchart showing an example of the procedure of the fundamental frequency calculation step S20 of FIG. 26. The flowchart of FIG. 27 shows the fundamental frequency f c This corresponds to the procedure of the first calculation method.

[0068] 27, first, in step S201, the measurement device 1 calculates the frequency spectrum of the first measurement data based on the observation data. For example, the measurement device 1 may calculate the frequency spectrum by performing a fast Fourier transform on the first measurement data.

[0069] Then, in step S202, the measurement device 1 calculates the fundamental frequency f c For example, the measurement device 1 calculates the fundamental frequency f by determining the lowest frequency among a plurality of frequencies corresponding to the plurality of peaks in the frequency spectrum. c It may be calculated as:

[0070] 28 is a flowchart showing another example of the procedure of the fundamental frequency calculation step S20 of FIG. 26. The flowchart of FIG. 28 shows the fundamental frequency f c This corresponds to the procedure of the second calculation method.

[0071] As shown in FIG. 28, first, in step S211, the measurement device 1 calculates a passing time t s For example, the measurement device 1 calculates the time of the first negative peak when the railway vehicle 6 passes over the bridge 5 in the first measurement data. time t i The time of the last negative peak is calculated as the advance time t o As shown in the previous equation (1), the approach time t i From the exit time t o Time to pass through time t s It may be calculated as:

[0072] Then, in step S212, the measuring device 1 calculates the passing time t s and the number of railcars 6 included in the pre-created environmental information, C T , the length of each railcar 6 is the vehicle length L C (C m ) and the length of bridge 5, L B Based on the fundamental frequency f c For example, the measurement device 1 calculates the fundamental frequency f c may be calculated.

[0073] 29 is a flowchart showing another example of the procedure of the fundamental frequency calculation step S20 of FIG. 26. The flowchart of FIG. 29 shows the fundamental frequency f c This corresponds to the procedure of the third calculation method.

[0074] As shown in FIG. 29, first, in step S221, the measurement device 1 calculates the number of periods T of the response to the action of the railway vehicle 6 traveling on the bridge 5 on the observation point R based on the first measurement data based on the observation data. n For example, the measurement device 1 calculates the number of positive peaks P when the railway vehicle 6 travels over the bridge 5 in the first measurement data. p or negative peak number P n Count the number of periods T n may be calculated.

[0075] Next, in step S222, the measurement device 1 calculates the passing time t s For example, the measurement device 1 calculates the time of the first negative peak when the railway vehicle 6 travels over the bridge 5 as the approach time t i The time of the last negative peak is calculated as the advance time t o As shown in the previous equation (1), the approach time t i From the exit time t o Time to pass through time t s It may be calculated as:

[0076] Then, in step S223, the measurement device 1 calculates the number of periods T of the response calculated in step S221. n and the transit time t calculated in step S222 s and based on the fundamental frequency f c Specifically, the measurement device 1 calculates the number of periods T n The transit time t s By dividing by, we get the fundamental frequency f c Calculate.

[0077] 1-4. Configuration of sensors, measuring devices and monitoring devices 30 is a diagram showing an example configuration of the sensor 2, the measuring device 1, and the monitoring device 3. As shown in FIG. 30, the sensor 2 includes a communication unit 21, an acceleration sensor 22, a processor 23, and a storage unit 24.

[0078] 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.

[0079] The acceleration sensor 22 detects acceleration occurring in each of the three axial directions.

[0080] 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.

[0081] The communication unit 21 transmits the observation data 242 stored in the storage unit 24 to the measurement device 1 under the control of the processor 23 .

[0082] As shown in FIG. 30, the measurement device 1 includes a first communication unit 11, a second communication unit 12, a storage unit 13, and a processor .

[0083] The first communication unit 11 receives observation data 242 from the sensor 2 and outputs the received observation data 242 to the processor 14.

[0084] The storage unit 13 is a memory that stores programs, data, etc. for the processor 14 to perform calculation processing and control processing. The storage unit 13 also stores various programs, data, etc. for the processor 14 to realize predetermined application functions. The processor 14 may also receive various programs, data, etc. via the communication network 4 and store them in the storage unit 13.

[0085] The processor 14 generates the measurement data 134 based on the observation data 242 received by the first communication unit 11, and stores the generated measurement data 134 in the storage unit 13.

[0086] In this embodiment, the processor 14 executes the measurement program 131 stored in the storage unit 13, thereby functioning as an observation data acquisition unit 141, a fundamental frequency calculation unit 142, a filter generation unit 143, a filter processing unit 144, an integration processing unit 145, and a measurement data output unit 146. That is, the processor 14 includes the observation data acquisition unit 141, the fundamental frequency calculation unit 142, the filter generation unit 143, the filter processing unit 144, the integration processing unit 145, and the measurement data output unit 146.

[0087] The observation data acquisition unit 141 acquires the observation data 242 received by the first communication unit 11 and stores it in the storage unit 13 as observation data 133. That is, the observation data acquisition unit 141 performs the processing of the observation data acquisition step S10 in Fig. 26. In this embodiment, the observation data 133 is acceleration data.

[0088] The fundamental frequency calculation unit 142 calculates the fundamental frequency f of the deflection repeatedly occurring in the bridge 5 due to the running of the railway vehicle 6 based on the first measurement data based on the observation data 133 acquired by the observation data acquisition unit 141 and stored in the storage unit 13. c The first measurement data may be the acceleration data itself, which is the observation data 133, or may be data obtained by performing a predetermined process such as low-pass filtering on the acceleration data. For example, the fundamental frequency calculation unit 142 calculates the frequency spectrum of the first measurement data, and calculates the fundamental frequency f c Alternatively, the fundamental frequency calculation unit 142 may calculate the passing time t s Calculate the transit time t s and the number C of railroad vehicles 6 included in the environmental information 132 created in advance and stored in the storage unit 13. T, the length of each railcar 6 is the vehicle length L C (C m ) and the length of bridge 5, L B Based on the number of periods of the response, T n and transit time t s and the number of periods of the calculated response, T n and transit time t s and based on the fundamental frequency f c That is, the fundamental frequency calculation unit 142 performs the processing of fundamental frequency calculation step S20 in Fig. 26, specifically, the processing of steps S201 and S202 in Fig. 27, the processing of steps S211 and S212 in Fig. 28, or the processing of steps S221, S222, and S223 in Fig. 29.

[0089] The filter generation unit 143 generates the fundamental frequency f calculated by the fundamental frequency calculation unit 142. c The filter generating unit 143 generates a filter with a variable passband based on the fundamental frequency f. For example, the filter may be a high-pass filter or a band-pass filter. The band-pass filter may be configured with a high-pass filter and a low-pass filter. When the filter is a high-pass filter, the filter generating unit 143 generates a filter with a variable passband based on the fundamental frequency f. c and the fundamental frequency f c Furthermore, the filter generating unit 143 generates a filter having a lower frequency than the band pass filter. If the filter is a filter, the first cutoff frequency is the fundamental frequency f c and the fundamental frequency f c The second cutoff frequency is lower than the fundamental frequency f c higher than n times the fundamental frequency f c 26. In this case, the filter generating unit 143 generates a filter having a frequency lower than n+1 times the frequency of the filter having a frequency ...

[0090] The filter processing unit 144 generates second measured data by performing filtering on the first measured data using the filter generated by the filter generation unit 143. The filter processing unit 144 performs filtering on the first measured data using the high-pass filter or band-pass filter generated by the filter generation unit 143. That is, the filter processing unit 144 performs the processing of the filtering step S40 in FIG. 26 .

[0091] The integration processing unit 145 generates third measurement data by performing integration processing on the second measurement data generated by the filter processing unit 144. For example, the second measurement data may be acceleration data, and the integration processing unit 145 may integrate the second measurement data to generate velocity data as the third measurement data, or may integrate the second measurement data twice to generate displacement data as the third measurement data. That is, the integration processing unit 145 performs the processing of the integration processing step S50 in FIG. 26.

[0092] The third measurement data calculated by the integration processing unit 145 is stored in the storage unit 13 as at least a part of the measurement data 134. The measurement data 134 may further include the first measurement data, the second measurement data, and so on.

[0093] The measurement data output unit 146 reads out the measurement data 134 stored in the storage unit 13 and outputs the measurement data 134 to the monitoring device 3. Specifically, under the control of the measurement data output unit 146, the second communication unit 12 transmits the measurement data 134 stored in the storage unit 13 to the monitoring device 3 via the communication network 4. That is, the measurement data output unit 146 performs the processing of the measurement data output step S60 in FIG. 26 .

[0094] In this way, the measurement program 131 is a program that causes the measurement device 1, which is a computer, to execute each procedure of the flowchart shown in FIG.

[0095] As shown in FIG. 30, 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.

[0096] The communication unit 31 receives the measurement data 134 from the measurement device 1 and outputs the received measurement data 134 to the processor 32 .

[0097] 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.

[0098] The operation unit 34 outputs operation data corresponding to an operation by the 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.

[0099] 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.

[0100] The processor 32 acquires the measurement data 134 received by the communication unit 31, and based on the acquired measurement data 134, evaluates the changes over time in the passing speed of the railway vehicle 6 and the displacement of the bridge 5 to generate evaluation information, and displays the generated evaluation information on the display unit 33.

[0101] In this embodiment, the processor 32 functions as a measurement data acquisition 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 acquisition unit 321 and the monitoring unit 322.

[0102] The measurement data acquisition unit 321 acquires the measurement data 134 received by the communication unit 31, and adds the acquired measurement data 134 to the measurement data sequence 352 stored in the storage unit 35.

[0103] The monitoring unit 322 evaluates the passing speed of the railway vehicle 6 based on the measurement data sequence 352 stored in the memory unit 35, and also statistically evaluates changes in the displacement of the bridge 5 over time. The monitoring unit 322 then generates evaluation information indicating the evaluation results and displays the generated evaluation information on the display unit 33. The user can monitor the passing speed of the railway vehicle 6 and the state of the bridge 5 based on the evaluation information displayed on the display unit 33.

[0104] The monitoring unit 322 may perform processes such as monitoring the railway vehicle 6 and determining abnormalities in the bridge 5 based on the measurement data sequence 352 stored in the storage unit 35.

[0105] 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. The measuring device 1 also 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.

[0106] The functions of the processors 14, 23, and 32 may be realized by individual hardware components, or may be realized by integrated hardware components. For example, the processors 14, 23, and 32 may include hardware components, and the hardware components may include at least one of a circuit for processing digital signals and a circuit for processing analog signals. The processors 14, 23, and 32 may be a CPU, a GPU, a DSP, or the like. CPU is an abbreviation for Central Processing Unit, and GPU is an abbreviation for Graphics Processing Unit. DSP is an abbreviation for Digital Signal Processor. The processors 14, 23, and 32 may be configured as custom ICs such as ASICs to realize the functions of each section, or the functions of each section may be realized by a CPU and an ASIC. ASIC is an abbreviation for Application Specific Integrated Circuit, and IC is an abbreviation for Integrated Circuit. be.

[0107] The storage units 13, 24, and 35 are configured by, for example, various types of IC memory such as ROM, flash ROM, and RAM, as well as recording media such as hard disks and memory cards. ROM is an abbreviation for Read Only Memory, RAM is an abbreviation for Random Access Memory, and IC is an abbreviation for Integrated Circuit. The storage units 13, 24, and 35 include non-volatile information storage devices that are computer-readable devices or media, and various programs, data, and the like may be stored in the information storage devices. The information storage devices may be optical disks such as DVDs and CDs, hard disk drives, or various types of memory such as card-type memories and ROMs.

[0108] 30 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 The monitoring device 3 receives a plurality of pieces of observation data 242 transmitted from a plurality of sensors 2, generates a plurality of pieces of measurement data 134, and transmits the data to the monitoring device 3. The monitoring device 3 also receives the plurality of pieces of measurement data 134 transmitted from the measuring device 1, and monitors the state of the bridge 5 based on the received plurality of pieces of measurement data 134.

[0109] 1-5.Effects As described above, in the measurement method of the first embodiment, the measurement device 1 measures the fundamental frequency f of the deflection repeatedly occurring in the bridge 5 due to the running of the railway vehicle 6. cThe measurement device 1 generates second measurement data by filtering the first measurement data based on the observation data using a filter whose passband is set to be variable depending on the time t s , number of vehicles C T , bridge length L B The fundamental frequency f c Even if the offset error and noise components change, the second measurement data can be generated by using an appropriate high-pass filter or band-pass filter in response to the change. Therefore, this measurement method can reduce drift caused by the measurement device 1 integrating the second measurement data.

[0110] For example, the measurement device 1 has a cutoff frequency of 1 / f c and the fundamental frequency f c By filtering the first measurement data using a high-pass filter having a frequency lower than the fundamental frequency f c Therefore, the measurement device 1 can reduce drift and low-frequency noise components that occur when integrating the second measurement data.

[0111] Furthermore, for example, the measurement device 1 may be configured such that the first cutoff frequency is a fundamental frequency f c and the fundamental frequency f c The second cutoff frequency is lower than the fundamental frequency f c higher than n times the fundamental frequency f c By filtering the first measurement data using a band-pass filter having a frequency lower than n+1 times the fundamental frequency f c The noise components with frequencies less than half of the fundamental frequency f c Therefore, the measurement device 1 can generate second measurement data in which noise components at frequencies equal to or greater than n+1 times the fundamental frequency fc The signal component and its second to nth harmonic signal components are targeted for measurement, and drift, low frequency noise components, and high frequency noise components that occur by integrating the second measurement data can be reduced.

[0112] In particular, the measurement device 1 can reduce drift and low-frequency noise components that occur in the third measurement data, which is velocity data or displacement data, by integrating the second measurement data, which is acceleration data.

[0113] 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 components as those in the first embodiment being given the same reference numerals and explanations that overlap with those in the first embodiment being omitted or simplified.

[0114] Fig. 31 is a flowchart showing an example of the procedure of the measurement method of the second embodiment. In Fig. 31, the same steps as in Fig. 26 are given the same reference numerals. In this embodiment, the measurement device 1 executes the procedure shown in Fig. 31.

[0115] 31, first, the measurement device 1 performs an observation data acquisition step S10, and then performs a fundamental frequency calculation step S20. The processes of the observation data acquisition step S10 and the fundamental frequency calculation step S20 are the same as those in the first embodiment, and therefore, description thereof will be omitted.

[0116] Next, the measurement device 1 performs a filter selection step S32 instead of the filter generation step S30 in Fig. 26. In the filter selection step S32, the measurement device 1 selects the fundamental frequency f c and filter information, which is information on a plurality of filters having different passbands created in advance, to select a filter to be used in filtering the first measurement data from the plurality of filters.

[0117] For example, the filter information is information that indicates the correspondence between a plurality of fundamental frequencies and the coefficient values ​​of a plurality of filters with different passbands. The plurality of filters may be FIR filters, and their orders may be the same. FIR is an abbreviation for Finite Impulse Response. Each of the multiple filters may be a high-pass filter having a cutoff frequency higher than half the fundamental frequency and lower than the fundamental frequency. Alternatively, each of the multiple filters may be a band-pass filter having a first cutoff frequency higher than half the fundamental frequency and lower than the fundamental frequency, and a second cutoff frequency higher than n times the fundamental frequency and lower than n+1 times the fundamental frequency, where n is a predetermined integer. The band-pass filter may be composed of a high-pass filter and a low-pass filter. The measuring device 1 refers to the filter information and calculates the calculated fundamental frequency f c In this way, the measurement device 1 can select a filter that corresponds to the fundamental frequency closest to the fundamental frequency f c Based on the fundamental frequency f c A filter with a variable passband is selected depending on the input signal.

[0118] Next, in a filtering process step S40, the measurement device 1 performs filtering on the first measurement data using the filter selected in step S30 to generate second measurement data. That is, the measurement device 1 performs filtering on the first measurement data using the high-pass filter or band-pass filter selected in step S30.

[0119] Next, the measurement device 1 performs an integration process step S50, and further performs a measurement data output process S60. The integration process step S50 and the measurement data output process S60 are the same as those in the first embodiment, and therefore a description thereof will be omitted.

[0120] Then, the measuring device 1 repeats the processes of steps S10 to S60 until the measurement is completed in step S70.

[0121] The configurations and functions of the sensor 2 and the monitoring device 3 in the second embodiment are the same as those in the first embodiment, and therefore are not shown in the figures. Fig. 32 is a diagram showing an example of the configuration of the measuring device 1 in the second embodiment.

[0122] 32, the measurement device 1 in the second embodiment, like the first embodiment, includes a first communication unit 11, a second communication unit 12, a storage unit 13, and a processor 14. The functions of the first communication unit 11, the second communication unit 12, and the storage unit 13 are the same as those in the first embodiment, and therefore description thereof will be omitted.

[0123] In the second embodiment, the processor 14 executes the measurement program 131 stored in the storage unit 13 to function as an observation data acquisition unit 141, a fundamental frequency calculation unit 142, a filter processing unit 144, an integration processing unit 145, a measurement data output unit 146, and a filter selection unit 147. That is, the processor 14 includes the observation data acquisition unit 141, the fundamental frequency calculation unit 142, the filter processing unit 144, the integration processing unit 145, the measurement data output unit 146, and the filter selection unit 147. The functions of the observation data acquisition unit 141, the fundamental frequency calculation unit 142, the integration processing unit 145, and the measurement data output unit 146 are the same as those in the first embodiment, and therefore a description thereof will be omitted. Note that the observation data acquisition unit 141 corresponds to the observation data acquisition unit 141 shown in FIG. 31. 31. The fundamental frequency calculation unit 142 performs the process of fundamental frequency calculation step S20 in FIG. 31, specifically, the processes of steps S201 and S202 in FIG. 27, the processes of steps S211 and S212 in FIG. 28, or the processes of steps S221, S222, and S223 in FIG. 29. The integration processing unit 145 performs the process of integration processing step S50 in FIG. 31. The measurement data output unit 146 performs the process of measurement data output step S60 in FIG. 31.

[0124] The filter selection unit 147 selects the fundamental frequency f calculated by the fundamental frequency calculation unit 142. cBased on filter information 135, which is information on a plurality of filters with different passbands that has been created in advance and stored in the memory unit 13, a filter to be used for filtering the first measurement data based on the observation data is selected from the plurality of filters.

[0125] For example, the filter information 135 is information that represents the correspondence between a plurality of fundamental frequencies and the coefficient values ​​of a plurality of filters having different passbands. The plurality of filters may be FIR filters, and may have the same order. Each of the plurality of filters may be a high-pass filter whose cutoff frequency is higher than half the fundamental frequency and lower than the fundamental frequency. Alternatively, each of the plurality of filters may be a band-pass filter whose first cutoff frequency is higher than half the fundamental frequency and lower than the fundamental frequency, and whose second cutoff frequency is higher than n times the fundamental frequency and lower than n+1 times the fundamental frequency, where n is a predetermined integer. The band-pass filter may be composed of a high-pass filter and a low-pass filter. The filter selection unit 147 refers to the filter information 135 and selects the fundamental frequency f calculated by the fundamental frequency calculation unit 142. c In this way, the filter selection unit 147 may select a filter corresponding to a fundamental frequency closest to the fundamental frequency f c Based on the fundamental frequency f c A filter with a variable passband is selected depending on the input signal.

[0126] The filter processing unit 144 generates second measured data by performing filtering on the first measured data using the filter selected by the filter selection unit 147. The filter processing unit 144 performs filtering on the first measured data using the high-pass filter or band-pass filter selected by the filter selection unit 147. That is, the filter processing unit 144 performs the processing of the filtering step S40 in FIG. 31 .

[0127] Other functions of the measurement device 1 in the second embodiment are the same as those in the first embodiment, and therefore description thereof will be omitted.

[0128] According to the measurement method of the second embodiment described above, the same effects as those of the measurement method of the first embodiment can be obtained.

[0129] 3. Variations The present invention is not limited to the present embodiment, and various modifications are possible within the scope of the present invention.

[0130] For example, in each of the above embodiments, each sensor 2 is provided on the main girder G of the superstructure 7, but it may also be provided on the surface or interior of the superstructure 7, the underside of the deck F, the pier 8a, etc.

[0131] In addition, in each of the above embodiments, the sensor 2, which is the observation device, is an acceleration sensor that outputs acceleration data, but the observation device may also be a velocity sensor. When the observation device is a velocity sensor, the measurement device 1 may perform a filter process similar to that in each of the above embodiments on the first measurement data based on the velocity data, which is the observation data output from the velocity sensor, and then perform an integration process to calculate the displacement.

[0132] 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 combined as appropriate.

[0133] 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 that replace non-essential parts of the configurations described in the embodiments. The present invention also includes configurations that achieve the same effects or purposes as the configurations described in the embodiments. The present invention also includes configurations that add publicly known technology to the configurations described in the embodiments.

[0134] The following can be derived from the above-described embodiment and modifications.

[0135] One aspect of the measurement method is an observation data acquisition step of acquiring observation data output from an observation device that observes an observation point on a bridge, the observation data including a response to an action on the observation point by a railway vehicle traveling on the bridge; a fundamental frequency calculation step of calculating a fundamental frequency of deflections repeatedly occurring in the bridge due to the running of the railway vehicle based on first measurement data based on the observation data; a filtering process for filtering the first measurement data using a filter whose passband is set to be variable according to the fundamental frequency, to generate second measurement data; an integration processing step of performing integration processing on the second measurement data to generate third measurement data; Includes.

[0136] This measurement method generates second measurement data by filtering first measurement data based on observation data using a filter whose passband is set to be variable according to the fundamental frequency of the repeated deflections of the bridge caused by the passage of railway vehicles. Therefore, even if the fundamental frequency changes depending on the time it takes for a railway vehicle to pass over the bridge, the number of railway vehicles on the bridge, the bridge length, etc., this measurement method can generate second measurement data in which offset errors and noise components have been effectively attenuated by using an appropriate filter according to these factors. Therefore, this measurement method can reduce drift caused by integrating the second measurement data.

[0137] One aspect of the measurement method is The filter may further include a filter generating step of generating the filter in which the passband is set variably based on the fundamental frequency.

[0138] One aspect of the measurement method is The method may include a filter selection step of selecting the filter to be used for the filtering process from the plurality of filters based on the fundamental frequency and information on a plurality of filters having different passbands stored in a storage unit.

[0139] In one aspect of the measurement method, the filter is a high-pass filter, The cutoff frequency of the filter may be higher than half the fundamental frequency and lower than the fundamental frequency.

[0140] According to this measurement method, it is possible to generate second measurement data in which noise components at frequencies at or below half the fundamental frequency are attenuated. Therefore, by integrating the second measurement data, This can reduce drift and low-frequency noise components caused by the

[0141] In one aspect of the measurement method, the filter is a bandpass filter, a first cutoff frequency of the filter is higher than half the fundamental frequency and lower than the fundamental frequency; For a given integer n greater than or equal to 2, the second cutoff frequency of the filter, which is higher than the first cutoff frequency, may be higher than n times the fundamental frequency and lower than n+1 times the fundamental frequency.

[0142] This measurement method can generate second measurement data in which noise components at frequencies at least half the fundamental frequency or less are attenuated, thereby reducing drift and low-frequency noise components that occur when integrating the second measurement data. Also, this measurement method can generate second measurement data in which noise components at frequencies at least n+1 times the fundamental frequency are attenuated, thereby reducing high-frequency noise components that occur when integrating the second measurement data, with the fundamental frequency signal component and its second- to n-th-order harmonic signal components being the measurement targets.

[0143] In one aspect of the measurement method, The observation device may be an acceleration sensor provided on the bridge.

[0144] In one aspect of the measurement method, the second measurement data is acceleration data, In the integration process, the second measurement data may be integrated to generate velocity data as the third measurement data, or the second measurement data may be integrated twice to generate displacement data as the third measurement data.

[0145] According to this measurement method, by integrating the second measurement data, which is acceleration data, drift and low-frequency noise components occurring in the velocity data or displacement data can be reduced.

[0146] In one aspect of the measurement method, The fundamental frequency calculation step calculating a frequency spectrum of the first measurement data; calculating the fundamental frequency based on the frequency spectrum; may include:

[0147] According to this measurement method, although the calculation load is high because a fast Fourier transform is performed, the fundamental frequency can be calculated accurately.

[0148] In one aspect of the measurement method, The fundamental frequency calculation step calculating a passing time, which is the time required for the railway vehicle to pass over the bridge, based on the first measurement data; calculating the fundamental frequency based on the passing time, the number of railroad vehicles, the length of each railroad vehicle, and the length of the bridge, which are included in pre-created environmental information; may include:

[0149] This measurement method does not require a fast Fourier transform, so the fundamental frequency can be measured at a low load. It can be calculated as follows.

[0150] In one aspect of the measurement method, The fundamental frequency calculation step calculating the number of periods of the response based on the first measurement data; calculating a passing time, which is the time required for the railway vehicle to pass over the bridge, based on the first measurement data; calculating the fundamental frequency based on the number of periods of the response and the transit time; may include:

[0151] According to this measurement method, since there is no need to perform a fast Fourier transform, the fundamental frequency can be calculated with a low load.

[0152] One aspect of the measurement device is an observation data acquisition unit that acquires observation data output from an observation device that observes an observation point on a bridge, the observation data including a response to an action on the observation point by a railway vehicle traveling on the bridge; a fundamental frequency calculation unit that calculates a fundamental frequency of deflection repeatedly occurring in the bridge due to the running of the railway vehicle based on first measurement data that is based on the observation data; a filter processing unit that performs filtering on the first measurement data using a filter whose passband is set to be variable according to the fundamental frequency, to generate second measurement data; an integration processing unit that performs integration processing on the second measurement data to generate third measurement data; Includes:

[0153] This measurement device generates second measurement data by filtering first measurement data based on observation data using a filter with a variable passband set according to the fundamental frequency of the repeated deflections of the bridge caused by the passage of railway vehicles. Therefore, even if the fundamental frequency changes depending on the time it takes for a railway vehicle to pass over the bridge, the number of railway vehicles, the bridge length, etc., this measurement device can generate second measurement data in which offset errors and noise components have been effectively attenuated using an appropriate filter according to these factors. Therefore, this measurement device can reduce drift caused by integrating the second measurement data.

[0154] One aspect of the measurement system is One aspect of the measurement device; the observation device; Equipped with.

[0155] One aspect of the measurement program is an observation data acquisition step of acquiring observation data output from an observation device that observes an observation point on a bridge, the observation data including a response to an action on the observation point by a railway vehicle traveling on the bridge; a fundamental frequency calculation step of calculating a fundamental frequency of deflections repeatedly occurring in the bridge due to the running of the railway vehicle based on first measurement data based on the observation data; a filtering process for filtering the first measurement data using a filter whose passband is set to be variable according to the fundamental frequency, to generate second measurement data; an integration processing step of performing integration processing on the second measurement data to generate third measurement data; to be executed by the computer.

[0156] In this measurement program, the computer generates second measurement data by filtering the first measurement data based on observation data using a filter with a variable passband set according to the fundamental frequency of the repeated deflections of the bridge caused by the passage of railway vehicles. Therefore, even if the fundamental frequency changes depending on the time it takes for a railway vehicle to pass over the bridge, the number of railway vehicles, the bridge length, etc., the computer can generate second measurement data in which offset errors and noise components are effectively attenuated by using an appropriate filter according to those factors. Therefore, according to this measurement program, the computer can reduce drift caused by integrating the second measurement data. [Explanation of symbols]

[0157] 1...measuring device, 2...sensor, 3...monitoring device, 4...communication network, 5...bridge, 6...railway 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...memory unit, 14...processor, 21...communication unit, 22...acceleration sensor, 23...processor, 24...memory unit, 31...communication unit, 32...processor, 33...display unit, 3 4...operation unit, 35...storage unit, 131...measurement program, 132...environmental information, 133...observation data, 134...measurement data, 135...filter information, 141...observation data acquisition unit, 142...fundamental frequency calculation unit, 143...filter generation unit, 144...filter processing unit, 145...integration processing unit, 146...measurement data output unit, 147...filter selection unit, 241...observation program, 242...observation data, 321...measurement data acquisition unit, 322...monitoring unit, 351...monitoring program, 352...measurement data string

Claims

1. an observation data acquisition step of acquiring observation data output from an observation device that observes an observation point on a bridge, the observation data including a response to an action on the observation point by a railway vehicle traveling on the bridge; a fundamental frequency calculation step of calculating a fundamental frequency of deflections repeatedly occurring in the bridge due to the running of the railway vehicle based on first measurement data based on the observation data; a filtering process for filtering the first measurement data using a filter whose passband is set to be variable according to the fundamental frequency, to generate second measurement data; an integration processing step of performing integration processing on the second measurement data to generate third measurement data; Measurement methods, including:

2. In claim 1, a filter generating step of generating the filter, the passband of which is set to be variable, based on the fundamental frequency.

3. In claim 1, a filter selection step of selecting the filter to be used for the filtering process from the plurality of filters based on the fundamental frequency and information on a plurality of filters having different passbands stored in a storage unit.

4. In claim 1, the filter is a high-pass filter, A measurement method in which the cutoff frequency of the filter is higher than half the fundamental frequency and lower than the fundamental frequency.

5. In claim 1, the filter is a bandpass filter, a first cutoff frequency of the filter is higher than half the fundamental frequency and lower than the fundamental frequency; A measurement method, wherein, for a predetermined integer n greater than or equal to 2, a second cutoff frequency of the filter that is higher than the first cutoff frequency is higher than a frequency that is n times the fundamental frequency and lower than a frequency that is n+1 times the fundamental frequency.

6. In claim 1, The observation device is an acceleration sensor provided on the bridge.

7. In claim 1, the second measurement data is acceleration data, The measurement method, wherein the integration processing step integrates the second measurement data to generate velocity data as the third measurement data, or integrates the second measurement data twice to generate displacement data as the third measurement data.

8. In claim 1, The fundamental frequency calculation step calculating a frequency spectrum of the first measurement data; calculating the fundamental frequency based on the frequency spectrum; Measurement methods, including:

9. In claim 1, The fundamental frequency calculation step calculating a passing time, which is the time required for the railway vehicle to pass over the bridge, based on the first measurement data; calculating the fundamental frequency based on the passing time, the number of railroad vehicles, the length of each railroad vehicle, and the length of the bridge, which are included in pre-created environmental information; Measurement methods, including:

10. In claim 1, The fundamental frequency calculation step calculating the number of periods of the response based on the first measurement data; calculating a passing time, which is the time required for the railway vehicle to pass over the bridge, based on the first measurement data; calculating the fundamental frequency based on the number of periods of the response and the transit time; Measurement methods, including:

11. an observation data acquisition unit that acquires observation data output from an observation device that observes an observation point on a bridge, the observation data including a response to an action on the observation point by a railway vehicle traveling on the bridge; a fundamental frequency calculation unit that calculates a fundamental frequency of deflections repeatedly occurring in the bridge due to the running of the railway vehicle based on first measurement data that is based on the observation data; a filter processing unit that performs filtering on the first measurement data using a filter whose passband is set to be variable according to the fundamental frequency, to generate second measurement data; an integration processing unit that performs integration processing on the second measurement data to generate third measurement data; 2. A measuring device comprising:

12. The measurement device according to claim 11; the observation device; A measurement system equipped with

13. an observation data acquisition step of acquiring observation data output from an observation device that observes an observation point on a bridge, the observation data including a response to an action on the observation point by a railway vehicle traveling on the bridge; a fundamental frequency calculation step of calculating a fundamental frequency of deflection repeatedly occurring in the bridge due to the running of the railway vehicle based on first measurement data based on the observation data; a filtering process for filtering the first measurement data using a filter whose passband is set to be variable according to the fundamental frequency, to generate second measurement data; an integration processing step of performing integration processing on the second measurement data to generate third measurement data; A measurement program that causes a computer to execute the following.

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