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

Abstract

The invention provides a measurement method, a measurement device, a measurement system, and a measurement program product, which can reduce drift generated by integration processing of data based on observation data when a railway vehicle travels on a bridge. A measurement method includes: an observation data acquisition step in which observation data including a response to an action of a railway vehicle traveling on a bridge on an observation point, which is output from an observation device that observes the observation point of the bridge, is acquired; a fundamental frequency calculation step for calculating, from first measurement data based on the observation data, a fundamental frequency of deflection repeatedly generated on the bridge due to the travel of the railway vehicle; a filter processing step for generating second measurement data by performing filter processing on the first measurement data using a filter in which a passband is variably set in accordance with the fundamental frequency; and an integration processing step for performing integration processing on the second measurement data to generate third measurement data.

Description

Technical Field

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

[0002] Patent document 1 describes the following deflection measuring device: an acceleration sensor installed on a railway bridge and the output of the acceleration sensor when the railway bridge is in an unloaded state are set as the zero point of acceleration, the zero point of the acceleration output by the acceleration sensor when the railway bridge is in a loaded state is corrected, and after the zero point correction, quadratic integration, Bayesian estimation, Kalman filter, etc. are applied to suppress drift and estimate the deflection of the railway bridge.

[0003] Patent Document 1: Japanese Patent Application Publication No. 2019-049095

[0004] However, in Patent Document 1 Figure 3 In Figure C, the result shows that the displacement increases during the loaded section of the railway bridge compared to the unloaded section. However, the expected displacement waveform is clearly one in which the displacement decreases during the loaded section compared to the unloaded section. This is similar to the result of suppressing both the low-frequency signal components of the displacement waveform and the low-frequency drift components. Therefore, the deflection estimation method described in Patent Document 1 suppresses the low-frequency components of the displacement waveform along with the drift, potentially preventing a high-precision estimate of the actual displacement amplitude. Summary of the Invention

[0005] One embodiment of the measurement method of the present invention includes the following steps: an observation data acquisition step, which acquires observation data output from an observation device at an observation point of an observation bridge, wherein the observation data includes a response to the action of a railway vehicle traveling on the bridge on the observation point; a fundamental frequency calculation step, which calculates the fundamental frequency of the deflection repeatedly generated on the bridge due to the travel of the railway vehicle based on first measurement data based on the observation data; a filtering processing step, which uses a filter with a passband variably set according to the fundamental frequency to filter the first measurement data to generate second measurement data; and an integration processing step, which integrates the second measurement data to generate third measurement data.

[0006] One embodiment of the measuring device of the present invention includes: an observation data acquisition unit that acquires observation data output from an observation device at an observation point of an observation bridge, the observation data including a response to an action of a railway vehicle traveling on the bridge on the observation point; a fundamental frequency calculation unit that calculates, based on first measurement data based on the observation data, the fundamental frequency of deflection repeatedly generated on the bridge due to the travel of the railway vehicle; a filtering processing unit that performs filtering processing on the first measurement data using a filter whose passband is variably set according to the fundamental frequency to generate second measurement data; and an integration processing unit that performs integration processing on the second measurement data to generate third measurement data.

[0007] One embodiment of a measurement system of the present invention includes one embodiment of the measurement device and the observation device.

[0008] One embodiment of the measurement program of the present invention causes a computer to execute the following processes: an observation data acquisition process, which acquires observation data output from an observation device at an observation point of an observation bridge, wherein the observation data includes a response to the action of a railway vehicle traveling on the bridge on the observation point; a fundamental frequency calculation process, which calculates the fundamental frequency of the deflection repeatedly generated on the bridge due to the travel of the railway vehicle based on first measurement data based on the observation data; a filtering processing process, which uses a filter with a passband variably set according to the fundamental frequency to filter the first measurement data to generate second measurement data; and an integration processing process, which integrates the second measurement data to generate third measurement data. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 It is a diagram showing a configuration example of a measurement system.

[0010] Figure 2 It is cut along the AA line Figure 1 Cross-sectional view of the upper structure.

[0011] Figure 3 This is a diagram illustrating acceleration detected by an acceleration sensor.

[0012] Figure 4 1 is a diagram showing an example of acceleration α(t) when a railway vehicle travels on a bridge.

[0013] Figure 5 Graph showing displacement u(t) obtained by quadratically integrating acceleration α(t).

[0014] Figure 6 is the time t s Graphs showing examples of bridge displacement waveforms under different conditions.

[0015] Figure 7 It shows the Figure 6 Graph of the frequency spectrum obtained by fast Fourier transform of the displacement waveform.

[0016] Figure 8 1 and 2 are diagrams showing examples of displacement waveforms of bridges when the number of vehicles is different.

[0017] Figure 9 It shows the Figure 8 Graph of the frequency spectrum obtained by fast Fourier transform of the displacement waveform.

[0018] Figure 10 This is a diagram showing an example of a displacement waveform of a bridge when the bridge lengths are different from each other.

[0019] Figure 11 It shows the Figure 10 Graph of the frequency spectrum obtained by fast Fourier transform of the displacement waveform.

[0020] Figure 12 It is a diagram showing an example of the gain-frequency characteristic of a high-pass filter.

[0021] Figure 13 This is a diagram showing an example of acceleration α(t).

[0022] Figure 14 It shows the Figure 13 Graph of the frequency spectrum obtained by fast Fourier transform of the acceleration α(t).

[0023] Figure 15 Graph showing velocity v(t) obtained by integrating filtered acceleration.

[0024] Figure 16 Graph showing displacement u(t) obtained by quadratically integrating filtered acceleration.

[0025] Figure 17 This is a diagram showing an example of acceleration α(t).

[0026] Figure 18 It shows the Figure 17 Graph of the frequency spectrum obtained by fast Fourier transform of the acceleration α(t).

[0027] Figure 19 is the relationship between acceleration α(t) and time t s A diagram of the relationship.

[0028] Figure 20 Is the length L of the vehicle C (C m ) and the distance between the axles La(a w (Cm , a diagram of an example of n)).

[0029] Figure 21 It shows the Figure 19 The acceleration α(t) is low-pass filtered to obtain the acceleration α LPF (t) of the graph.

[0030] Figure 22 It is a diagram showing an example of the gain-frequency characteristic of a high-pass filter.

[0031] Figure 23 The high-pass filter is used to Figure 13 Graph of the frequency spectrum obtained by fast Fourier transforming the acceleration after filtering the acceleration α(t).

[0032] Figure 24 Is shown for use Figure 22 The characteristics of the high-pass filter Figure 13 Graph of velocity v(t) obtained by integrating the filtered acceleration α(t).

[0033] Figure 25 Is shown for use Figure 22 The characteristics of the high-pass filter Figure 13 Graph showing the displacement u(t) obtained by quadratically integrating the filtered acceleration α(t).

[0034] Figure 26 This is a flowchart showing an example of the steps of the measurement method according to the first embodiment.

[0035] Figure 27 This is a flowchart showing an example of the procedure of the fundamental frequency calculation process.

[0036] Figure 28 This is a flowchart showing another example of the procedure of the fundamental frequency calculation process.

[0037] Figure 29 This is a flowchart showing another example of the procedure of the fundamental frequency calculation process.

[0038] Figure 30 It is a diagram showing a configuration example of a sensor, a measuring device, and a monitoring device.

[0039] Figure 31 This is a flowchart showing an example of the procedure of the measurement method according to the second embodiment.

[0040] Figure 32 It is a diagram showing a configuration example of a measuring device in the second embodiment.

[0041] Label Description

[0042] 1: Measuring device; 2: Sensor; 3: Monitoring device; 4: Communication network; 5: Bridge; 6: Railway vehicle; 7: Superstructure; 7a: Bridge deck; 7b: Support member; 7c: Track; 7d: Sleeper; 7e: Ballast; F: Floor; G: Main beam; 8: Substructure; 8a: Pier; 8b: Abutment; 10: Measuring system; 11: First communication unit; 12: Second communication unit; 13: Storage unit; 14: Processor; 21: Communication unit; 22: Acceleration sensor; 23: Processor; 24: Storage unit; 31: Communication unit; 32: Processor; 33: Display Display unit; 34: 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 sequence. DETAILED DESCRIPTION

[0043] Hereinafter, preferred embodiments of the present invention will be described in detail using the accompanying drawings. Furthermore, the embodiments described below do not unduly limit the content of the present invention as described in the claims. Furthermore, the structures described below may not all be essential components of the present invention.

[0044] 1. First Implementation

[0045] 1-1. Measurement system structure

[0046] Railway vehicles passing over bridges are heavy, and this can be measured using BWIM. BWIM, short for Bridge Weight in Motion, uses the bridge as a "weighing scale" to measure the weight of passing railway vehicles, the number of axles, and other information by measuring the bridge's deformation. Bridges that can analyze the weight of passing railway vehicles based on responses such as deformation and strain are the structures where BWIM functions. By applying the physical processes between the action and response of a bridge, the BWIM system can measure the weight of passing railway vehicles.

[0047] Figure 1 FIG. 1 is a diagram showing an example of a measurement system according to this embodiment. Figure 1 As shown, the measurement system 10 of this embodiment includes a measurement device 1 and at least one sensor 2 installed on a bridge 5. In addition, the measurement system 10 may also include a monitoring device 3.

[0048] The bridge 5 is composed of a superstructure 7 and a substructure 8 . Figure 2It is along Figure 1 The cross-sectional view of the upper structure 7 is cut along the AA line. Figure 1 and Figure 2 As shown, the upper structure 7 includes a bridge deck 7a composed of a floor F, a main beam G, a cross beam (not shown), etc., a support 7b, a track 7c, a sleeper 7d and a ballast 7e. Figure 1 As shown, the substructure 8 includes piers 8a and abutments 8b. The superstructure 7 is a structure that is installed between adjacent abutments 8b and piers 8a, between two adjacent abutments 8b, or between two adjacent piers 8a. The ends of the superstructure 7 are located between adjacent abutments 8b and piers 8a, between two adjacent abutments 8b, or between two adjacent piers 8a.

[0049] When the railway vehicle 6 enters the superstructure 7 of the bridge 5, the superstructure 7 bends due to the load of the railway vehicle 6. However, since the railway vehicle 6 is composed of multiple vehicles connected together, the bending of the superstructure 7 is repeated periodically as each vehicle passes.

[0050] The measuring device 1 and each sensor 2 are connected by a cable (not shown), for example, and communicate via a communication network such as CAN. CAN is an abbreviation for Controller Area Network. Alternatively, the measuring device 1 and each sensor 2 may communicate via a wireless network.

[0051] Each sensor 2 outputs observation data containing physical quantities generated when the railway vehicle 6 travels on the bridge 5. In this embodiment, each sensor 2 is an acceleration sensor and outputs acceleration data containing the acceleration generated when the railway vehicle 6 travels on the bridge 5. Each sensor 2 can be, for example, a quartz acceleration sensor or a MEMS acceleration sensor. MEMS stands for Micro Electro Mechanical Systems.

[0052] In this embodiment, each sensor 2 is installed in the center of the length direction of the superstructure 7 of the bridge 5, specifically, in the center of the length direction of the main beam G. However, as long as each sensor 2 can detect the acceleration generated by the movement of the railway vehicle 6, its installation position is not limited to the center of the superstructure 7. In addition, if each sensor 2 is installed on the floor F of the superstructure 7, it may be damaged by the movement of the railway vehicle 6. In addition, the measurement accuracy may be affected by the local deformation of the bridge deck 7a. Therefore, in Figure 1 and Figure 2 In the example, each sensor 2 is provided on the main beam G of the superstructure 7 .

[0053] The floor F and main beams G of the superstructure 7 are bent in the vertical direction by the load of railway vehicles 6 passing through the bridge 5. Each sensor 2 detects the acceleration of the bending of the floor F and main beams G caused by the load of railway vehicles 6 passing through the bridge 5.

[0054] The measuring device 1 calculates the displacement of the bridge 5 when the railway vehicle 6 passes through 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 to be measured. The measuring device 1 is installed on the abutment 8b, for example.

[0055] The measuring device 1 and the monitoring device 3 can communicate via a communication network 4 such as a mobile phone wireless network or the Internet. The measuring device 1 transmits measurement data, including the displacement of the bridge 5 when the railway vehicle 6 passes through the bridge 5, to the monitoring device 3. The monitoring device 3 may also store this measurement data in a storage device (not shown) and, for example, perform other processes 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.

[0056] In the present embodiment, the bridge 5 is a railway bridge, and may be, for example, a steel bridge, a bridge, an RC bridge, etc. RC is an abbreviation of Reinforced-Concrete.

[0057] like Figure 2 As shown in FIG. 1 , in this embodiment, the observation point R is set corresponding to the sensor 2. Figure 2 In this example, observation point R is set at a position on the surface of superstructure 7, located vertically above sensor 2 installed on main girder G. Specifically, sensor 2 is an observation device that observes observation point R, detects responses, or physical quantities, to the effects of various parts of railway vehicle 6 traveling on bridge 5 on observation point R, and outputs observation data containing the detected physical quantities. For example, the various parts of railway vehicle 6 may be axles or wheels, but axles will be used below. Furthermore, in this embodiment, each sensor 2 is an acceleration sensor that detects acceleration as a physical quantity. Sensor 2 can be installed at any location capable of detecting the acceleration generated at observation point R by the travel of railway vehicle 6, but is preferably installed at a position perpendicular to observation point R.

[0058] In addition, the number and location of the sensors 2 are not limited to Figure 1 and Figure 2 The examples shown are capable of various modifications.

[0059] Based on the observation data output from the sensor 2, the measuring device 1 obtains acceleration in a direction intersecting the surface of the superstructure 7 of the bridge 5 on which the railway vehicle 6 travels. The surface of the superstructure 7 on which the railway vehicle 6 travels is defined by the X direction, which is the longitudinal direction of the superstructure 7, and the Y direction, which is perpendicular to the direction of travel of the railway vehicle 6 and is the width direction of the superstructure 7. As the railway vehicle 6 travels, the observation point R deflects in directions perpendicular to the X and Y directions. Therefore, in order to accurately calculate the magnitude of the acceleration caused by this deflection, the measuring device 1 preferably obtains acceleration in the Z direction, which is perpendicular to the X and Y directions and is the normal direction of the floor F.

[0060] Figure 3 2 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 perpendicular axial directions.

[0061] In order to detect the deflection acceleration of the observation point R caused by the travel of the railway vehicle 6, the sensor 2 is installed so that one of the three detection axes, namely the x-axis, the y-axis, and the z-axis, is oriented in a direction intersecting the X and Y directions. Since the observation point R deflects in a direction perpendicular to the X and Y directions, in order to accurately detect the deflection acceleration, the sensor 2 is ideally installed so that one axis is aligned with the Z direction, which is perpendicular to the X and Y directions, i.e., the direction normal to the floor F.

[0062] However, when sensor 2 is installed on superstructure 7, the installation location may be tilted. In measuring device 1, even if one of the three detection axes of sensor 2 is not aligned with the normal direction of floor F but is positioned approximately in the normal direction, the error is small and negligible. Furthermore, even if one of the three detection axes of sensor 2 is not aligned with the normal direction of floor F, measuring device 1 can correct the detection error caused by the tilt of sensor 2 using a three-axis composite acceleration that combines the accelerations of the x-axis, y-axis, and z-axis. Alternatively, sensor 2 may be a uniaxial 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 F.

[0063] Hereinafter, the measurement method of this embodiment executed by the measurement device 1 will be described in detail.

[0064] 1-2. Details of measurement method

[0065] When a railway vehicle 6 travels on bridge 5, the loads on each axle of railway vehicle 6 generate periodic acceleration in the direction of gravity at observation point R. Sensor 2 detects this acceleration as acceleration α(k) in the z-axis direction and outputs acceleration data containing this acceleration α(k) in a time series. k is the sample number. If the sample interval is ΔT, the time series of acceleration α(k) is converted to acceleration α(t) with time t as the variable. Figure 4 Two examples of the acceleration α(t) of a railway vehicle 6 when traveling on the superstructure 7 of a bridge 5 are shown. Figure 4 In FIG. 1 , both the solid line and the dotted line represent the acceleration α(t) when the railway vehicle 6 travels at a constant travel speed. However, the travel speed of the railway vehicle 6 is higher than that of the dotted line, so the period of vibration of the acceleration α(t) is shorter.

[0066] By integrating the acceleration α(t), we obtain the velocity v(t) of the bridge 5, and by quadratically integrating the acceleration α(t), we obtain the displacement u(t) of the bridge 5. However, due to the offset and noise components contained in the acceleration α(t), integration errors occur, causing the velocity v(t) and displacement u(t) to drift. In particular, quadratically integrating increases the integration error, causing the displacement u(t) to drift significantly. Figure 5 Show the Figure 4 The displacement u(t) is obtained by performing quadratic integration of the two accelerations α(t) shown. Figure 5 In the middle, the solid line is Figure 4 The displacement u(t) is obtained by quadratically integrating the acceleration α(t) shown by the solid line. The dotted line is Figure 4 The acceleration α(t) shown by the dashed line in the middle is integrated twice to obtain the displacement u(t).

[0067] Since the offset and noise components that cause drift are in the low-frequency domain, the acceleration α(t) is subjected to high-pass filtering to reduce this drift. To prevent the signal components required for measurement contained in the acceleration α(t) from being reduced by the high-pass filtering, the cutoff frequency of the high-pass filter is set to a frequency lower than the lowest frequency of the required signal components. For example, the lowest frequency of the signal components required for measurement is the fundamental frequency f of the deflection repeatedly generated on the bridge 5 by the movement of the railway vehicle 6. c . Usually, the fundamental frequency f c It is the signal component with the maximum intensity contained in the acceleration α(t).

[0068] In addition, when the fundamental frequency f c When the frequency of 2 to n times the fundamental frequency f is used as the signal component required for measurement, the higher harmonic signal component of 2 to n times the fundamental frequency f is used as the signal component required for measurement. cSignal components and noise components with frequencies n times higher than the acceleration α(t) are not signal components required for measurement. n is a specified integer greater than 2, and is preset to an appropriate value according to the purpose of measurement. For example, n can be set to 5. The inherent resonant frequency of the structure of the bridge 5 may become a signal component in the high-frequency domain that is not a signal component required for measurement. In order to reduce the signal component or noise component in the high-frequency domain without reducing the n-th harmonic component, the acceleration α(t) may also be subjected to low-pass filtering. When high-pass filtering and low-pass filtering are performed, the result is subjected to band-pass filtering. Alternatively, the acceleration α(t) may be directly subjected to band-pass filtering.

[0069] On the other hand, the waveform of the displacement of the bridge 5 when the railway vehicle 6 travels on the bridge 5 is determined by the time required for the railway vehicle 6 to pass through the superstructure 7 of the bridge 5, that is, the passing time t s 、Number of railway vehicles 6 C T , bridge length L B etc. Bridge length L B is the length of the bridge 5, and in this embodiment is the distance between the entrance end and the entrance end of the superstructure 7. For example, when the bridge 5 has multiple superstructures 7, the bridge length L B It is the distance between the entry ends of each superstructure 7.

[0070] Figure 6 Shows the passing time t s Two examples of displacement waveforms of Bridge 5 under different conditions. Figure 6 In the figure, the solid line is through time t s 11 seconds, number of vehicles C T There are 12 bridges and the bridge length is L B The displacement waveform when the value is 12m, the dotted line is the displacement waveform when the value is 12m. s 15 seconds, number of vehicles C T There are 12 bridges and the bridge length is L B The displacement waveform is 12m. In addition, Figure 7 Show the Figure 6 The frequency spectrum is obtained by performing fast Fourier transform on the two displacement waveforms shown. Figure 7 In the middle, the solid line is Figure 6 The spectrum obtained by fast Fourier transform of the displacement waveform shown in the solid line, and the dotted line is Figure 6 The frequency spectrum is obtained by performing fast Fourier transform on the displacement waveform shown by the dotted line. Figure 6 As shown in the figure, the vibration period of the displacement waveform of the solid line is shorter than that of the dotted line. Figure 7 As shown, the fundamental frequency f of the solid line c , fundamental frequency f c The frequency of 2 to n times is higher than the dotted line.

[0071] Figure 8 Shows the number of vehicles C T Two examples of displacement waveforms of Bridge 5 under different conditions. Figure 8 In the figure, the solid line is through time t s 11 seconds, number of vehicles C T There are 12 bridges and the bridge length is L B The displacement waveform when the value is 12m, the dotted line is the displacement waveform when the value is 12m. s 11 seconds, number of vehicles C T There are 8 bridges and the bridge length is L B The displacement waveform is 12m. In addition, Figure 9 Show the Figure 8 The frequency spectrum is obtained by performing fast Fourier transform on the two displacement waveforms shown. Figure 9 In the middle, the solid line is Figure 8 The spectrum obtained by fast Fourier transform of the displacement waveform shown in the solid line, and the dotted line is Figure 8 The frequency spectrum is obtained by performing fast Fourier transform on the displacement waveform shown by the dotted line. Figure 8 As shown in the figure, the vibration period of the displacement waveform of the solid line is shorter than that of the dotted line. Figure 9 As shown, the fundamental frequency f of the solid line c , fundamental frequency f c The frequency of 2 to n times is higher than the dotted line.

[0072] Figure 10 Shows the bridge length L B Two examples of displacement waveforms of Bridge 5 under different conditions. Figure 10 In the figure, the solid line is through time t s 11 seconds, number of vehicles C T There are 8 bridges and the bridge length is L B The displacement waveform when the value is 12m, the dotted line is the displacement waveform when the value is 12m. s 11 seconds, number of vehicles C T There are 8 bridges and the bridge length is L B The displacement waveform at 24m. Figure 11 Show the Figure 10 The frequency spectrum is obtained by performing fast Fourier transform on the two displacement waveforms shown. Figure 11 In the middle, the solid line is Figure 10 The spectrum obtained by fast Fourier transform of the displacement waveform shown in the solid line, and the dotted line is Figure 10 The frequency spectrum is obtained by performing fast Fourier transform on the displacement waveform shown by the dotted line. Figure 10 As shown in the figure, the vibration period of the dotted line displacement waveform is slightly shorter than that of the solid line. Figure 11 As shown, the fundamental frequency f of the dotted line c , fundamental frequency f cThe frequency of 2 to n times is slightly higher than the solid line.

[0073] Thus, the fundamental frequency f c , fundamental frequency f c The frequency of 2 to n times is determined by the time t s 、Number of vehicles C T , bridge length L B For the bridge 5 to be measured, the bridge length L B does not change, but when a plurality of different types of railway vehicles 6 can travel on the bridge 5, the passing time t s 、Number of vehicles C T Therefore, it is considered to use the assumed passing time t of the bridge 5 as the measurement object. s 、Number of vehicles C T The range determines the fundamental frequency f c Similarly, consider using the assumed passing time t of the bridge 5 as the measurement object. s 、Number of vehicles C T The range determines the fundamental frequency f c The filtering process is performed by a low-pass filter or a band-pass filter in which the frequency range of n times is entirely included in the passband.

[0074] As an example, in Figure 6 、 Figure 8 as well as Figure 10 In the example, the fundamental frequency f c Since the frequency fluctuates within the range of 0.77 Hz to 1.13 Hz, it is conceivable to perform filtering using a high-pass filter whose lower limit of the passband is fixed to a predetermined frequency lower than 0.77 Hz. Figure 12 An example of the gain-frequency characteristic of such a high-pass filter is shown in FIG. Figure 12 In the example, the lower limit of the passband is between 0.4Hz and 0.5Hz. Figure 13 The acceleration α(t) shown is used Figure 12 The case where a high-pass filter with the characteristics shown is used for filtering. Figure 14 In the figure, the solid line represents Figure 13 The frequency spectrum obtained by fast Fourier transform of the acceleration α(t) shown in the figure is represented by the dotted line. Figure 13 The acceleration α(t) shown in FIG is filtered and then the acceleration is subjected to fast Fourier transform to obtain the spectrum. Figure 14 As shown, the acceleration α(t) has a fundamental frequency f c = The basic signal component of 1.13 Hz is filtered to reduce the offset component and noise component at frequencies lower than 0.4 Hz. Figure 15The velocity v(t) obtained by integrating the filtered acceleration is shown. Figure 16 The displacement u(t) obtained by quadratically integrating the filtered acceleration is shown. Figure 15 The velocity v(t) shown drifts slightly. In addition, Figure 16 The displacement u(t) shown is related to Figure 5 Compared with the displacement u(t) shown, that is, the displacement u(t) obtained by quadratically integrating the acceleration α(t), the drift amount is reduced, but it cannot be said to be sufficiently reduced. Figure 15 The speed v(t) shown, Figure 16 The drift of the displacement u(t) shown is due to the following reasons: by using the assumed fundamental frequency f c The low frequency of the range is included in the passband, and the low frequency of the passband is fixed by the high-pass filter. The low frequency included in the acceleration α(t) is the same as the fundamental frequency f. c The offset components and noise components in the frequency bands between the two signals are not reduced.

[0075] Similarly, the fundamental frequency f is assumed to be c In the case of a bandpass filter that fixes the lower limit frequency of the passband by the lowest frequency in the range of , the drift of the velocity v(t) and displacement u(t) may not be sufficiently reduced. c In the case of filtering processing using a low-pass filter or a band-pass filter that fixes the upper limit frequency of the passband at the highest frequency in the range of n times the frequency, the high-frequency noise components included in the acceleration α(t) may not be sufficiently reduced.

[0076] Therefore, in this embodiment, in order to sufficiently reduce the offset component and noise component included in the acceleration α(t), the measuring device 1 calculates the fundamental frequency f of the acceleration α(t). c , using the fundamental frequency f c The filter with a variably set passband performs filtering processing on the acceleration α(t). As the fundamental frequency f c There are three methods to calculate .

[0077] At the fundamental frequency f c In the first calculation method, first, the measuring device 1 performs fast Fourier transform on the acceleration α(t) to calculate the frequency spectrum. Figure 17 An example of acceleration α(t) is shown. Figure 18 Show the Figure 17 Then, the measuring device 1 calculates the lowest frequency among the multiple frequencies corresponding to the multiple peaks in the calculated spectrum as the fundamental frequency f c .exist Figure 18 In the example, 0.77 Hz is calculated as the fundamental frequency f c .

[0078] According to the first calculation method, the measuring device 1 performs fast Fourier transform, so the calculation load is high, but the fundamental frequency f can be accurately calculated. c .

[0079] At the fundamental frequency f c In the second calculation method, first, the measuring device 1 calculates the passing time t of the railway vehicle 6 passing through the bridge 5 based on the acceleration α(t). s . Figure 19 Shows the acceleration α(t) and the time t s The relationship between Figure 19 As shown, the measuring device 1 calculates the time of the first negative peak value of the acceleration α(t) as the bridge entry time t i Calculate the time of the last negative peak value of acceleration α(t) as the time t when the railway vehicle 6 leaves the bridge 5 o Furthermore, the measuring device 1 calculates the time from the bridge entry time t as in formula (1). i Time to exit the bridge t o The time until the end is taken as the passing time t s .

[0080] [Mathematical formula 1]

[0081] t s =t o -t i …(1)

[0082] Next, the measuring device 1 calculates the passing time t s and the pre-made environmental information including the size of the railway vehicle 6 and the size of the bridge 5, calculate the fundamental frequency f c The environmental information includes the length of the bridge 5, namely the bridge length L B As the size of the bridge 5. In addition, as the size of the railway vehicle 6, the environmental information includes, for example, the number of vehicles C of the railway vehicle 6 T , the length L of each vehicle of the railway vehicle 6 C (C m ), the number of axles of each vehicle a T (C m ) and the distance La(a) between the axles of each vehicle w (C m , n)). C m is the vehicle number, the length of each vehicle L C (C m ) is the first C from the front mThe distance between the two ends of each vehicle. The number of axles of each vehicle is a T (C m ) is the first C from the front m The number of axles of each vehicle. n is the axle number of each vehicle, 1≤n≤a T (C m The distance between the axles of each vehicle is La (a w (C m , n)) when n=1 is the Cth from the front m The distance between the front end of a vehicle and the first axle from the front, and when n≥2, the distance between the n-1th axle and the nth axle from the front. Figure 20 The C section of the railway vehicle 6 is shown. m Vehicle length L C (C m ) and the distance between the axles La(a w (C m , n)) is an example. The dimensions of the railway vehicle 6 can be measured by a known method. Alternatively, a database of the dimensions of railway vehicles 6 that have passed through the bridge 5 may be created in advance, and the dimensions of the corresponding vehicles may be referenced based on the time of passage.

[0083] The measuring device 1 is based on the calculated transit time t s , the number of vehicles C included in the environmental information T , the length of each vehicle L C =L C (C m ) and the bridge length L B , calculate the fundamental frequency f by formula (2) c In addition, in formula (2), the sum of the distance from the front end of the railway vehicle 6 to the front axle of the front vehicle and the distance from the last axle of the last vehicle to the rear end of the railway vehicle 6 is set to 4.1 m, but the measurement device 1 can also calculate this sum based on environmental information.

[0084] [Mathematical formula 2]

[0085]

[0086] According to the second calculation method, the measuring device 1 does not need to perform fast Fourier transform, and thus can calculate the fundamental frequency f with a low load. c .

[0087] At the fundamental frequency f c In the third calculation method, first, the measuring device 1 calculates the number of cycles T of the acceleration α(t) based on the acceleration α(t) in the same manner as in the second calculation method. n and the passing time t of the railway vehicle 6 passing through the bridge 5 s Through time ts The calculation method and fundamental frequency f c The second calculation method is the same as that of . For example, the measuring device 1 calculates the acceleration α obtained by low-pass filtering the acceleration α(t). LPF (t), for acceleration α LPF The number of positive peaks P of (t) p or negative peak number P n Count. Figure 21 The solid line indicates Figure 19 The acceleration α(t) shown is obtained by low-pass filtering. LPF (t). Figure 21 In , the acceleration α(t) is also represented by a dotted line. Figure 21 In the example, the acceleration α LPF The number of positive peaks P of (t) p is 9, acceleration α LPF The number of negative peaks P of (t) n The measuring device 1 is based on the number of positive peaks P p Or the negative peak number P n , calculate the cycle number T by formula (3) n .

[0088] [Mathematical formula 3]

[0089] T n =P p -1=P n -2…(3)

[0090] Then, the measuring device 1 calculates the number of cycles T based on the calculated number of cycles T. n With the passing time t s , calculate the fundamental frequency f c Specifically, as shown in equation (4), the measuring device 1 converts the number of cycles T n Divide by the passage time t s , from which we can calculate the fundamental frequency f c .

[0091] [Formula 4]

[0092]

[0093] According to the third calculation method, the measuring device 1 does not need to perform fast Fourier transform, and thus can calculate the fundamental frequency f with a low load. c .

[0094] Next, the measuring device 1 calculates the fundamental frequency f based on any one of the first to third calculation methods. c, generating a filter with a variably set passband. For example, the filter may be a high-pass filter or a band-pass filter. The band-pass filter may also be composed of a high-pass filter and a low-pass filter. In the case where the filter is a high-pass filter, the measuring device 1 generates a filter with a cutoff frequency higher than the fundamental frequency f c 1 / 2 of the fundamental frequency f c That is, the measuring device 1 generates a filter that makes at least the fundamental frequency f c The above signal components pass through, and at least the fundamental frequency f c A filter that attenuates signal components with frequencies below 1 / 2. Figure 14 As shown, Figure 13 The fundamental frequency f of the acceleration α(t) c Since 1.13 Hz is used, the measuring device 1 generates a high-pass filter having a cutoff frequency higher than a frequency of 1 / 2 of 1.17 Hz and lower than 1.17 Hz, for example. Figure 22 An example of the gain frequency characteristic of the high-pass filter is shown by a solid line in FIG. Figure 22 middle, Figure 12 The gain-frequency characteristic of the high-pass filter shown is also indicated by a dotted line.

[0095] In addition, when the filter is a bandpass filter, the measuring device 1 generates a first cutoff frequency higher than the fundamental frequency f c 1 / 2 of the fundamental frequency f c , the second cutoff frequency is higher than the fundamental frequency f c n times the frequency and lower than the fundamental frequency f c n is a predetermined integer. That is, the measuring device 1 generates a filter that makes at least the fundamental frequency f c Above and fundamental frequency f c The signal components with frequencies below n times of the fundamental frequency f are passed through, and at least the fundamental frequency f is c Signal components with frequencies below 1 / 2 and fundamental frequency f c A filter that attenuates signal components with frequencies greater than n+1 times the value of the filter.

[0096] Next, the measuring device 1 performs filtering processing on the acceleration α(t) using the generated filter. Figure 23 In the figure, the solid line indicates the Figure 22 The high-pass filter with the solid line characteristic is Figure 13 The acceleration α(t) shown in FIG is filtered and then the acceleration is fast Fourier transformed to obtain the spectrum. Figure 23 In the figure, dotted lines are also used to indicate the Figure 22 The high-pass filter characteristic shown by the dotted line is Figure 13The acceleration α(t) shown in FIG is filtered and then the acceleration is subjected to fast Fourier transform to obtain the spectrum. Figure 23 As shown, use Figure 22 Compared to filtering the acceleration α(t) with a high-pass filter with the characteristic shown by the dotted line, Figure 22 When the high-pass filter having the characteristic indicated by the solid line in the middle performs filtering processing on the acceleration α(t), the offset components and noise components in the low-frequency domain are significantly attenuated.

[0097] Then, the measuring device 1 integrates the acceleration obtained by filtering to calculate the velocity v(t) and displacement u(t). Figure 24 In the figure, the solid line indicates the use of Figure 22 The high-pass filter characteristic shown by the solid line is Figure 13 The acceleration α(t) shown in FIG is filtered and then the acceleration is integrated to obtain the velocity v(t). Figure 24 In the use Figure 22 The high-pass filter characteristic shown by the dotted line is Figure 13 The velocity v(t) obtained by integrating the acceleration after filtering the acceleration α(t) shown in FIG. is also represented by a dotted line. Figure 24 As shown in FIG, the velocity v(t) represented by the solid line has a reduced drift compared to the velocity v(t) represented by the dashed line. Figure 25 In the figure, the solid line indicates the use of Figure 22 The high-pass filter characteristic shown by the solid line is Figure 13 The displacement u(t) is obtained by performing a quadratic integration of the acceleration after filtering the acceleration α(t) shown in FIG. Figure 25 In the example, dotted lines are also used to indicate the use of Figure 22 The high-pass filter characteristic shown by the dotted line is Figure 13 The acceleration α(t) shown in FIG is filtered and then the displacement u(t) is obtained by performing a quadratic integration of the acceleration. Figure 25 As shown, the displacement u(t) shown by the solid line has a significantly reduced drift compared to the displacement u(t) shown by the dotted line.

[0098] Thus, in this embodiment, the measuring device 1 uses the fundamental frequency f of the acceleration α(t) included in the observation data. c By filtering the acceleration α(t) with a filter having an appropriately set passband and integrating the filtered acceleration, the drift of the velocity v(t) and the displacement u(t) can be significantly reduced.

[0099] 1-3. Measurement method steps

[0100] Figure 26This is a flowchart showing an example of the steps of the measurement method of the first embodiment. Figure 26 Steps shown.

[0101] like Figure 26 As shown, first, in observation data acquisition step S10, measurement device 1 acquires observation data output from sensor 2, serving as an observation device. The observation data includes responses to the effects of various parts of railway vehicle 6 traveling on bridge 5 on observation point R. In this embodiment, sensor 2 is an acceleration sensor installed on bridge 5, and the observation data includes acceleration as the response.

[0102] Next, in the fundamental frequency calculation step S20, the measuring device 1 calculates the fundamental frequency f of the deflection repeatedly generated on the bridge 5 due to the travel of the railway vehicle 6 based on the first measurement data based on the acceleration data obtained 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 a low-pass filter on the acceleration data. An example of the steps of the fundamental frequency calculation step S20 will be described later.

[0103] Next, in the filter generation step S30, the measurement device 1 generates a filter based on the fundamental frequency f calculated in step S20. c , generate according to the fundamental frequency f c A filter with a variably set passband. For example, the filter may be a high-pass filter or a band-pass filter. A band-pass filter may also be composed of a high-pass filter and a low-pass filter. When the filter is a high-pass filter, the measuring device 1 generates a signal with a cutoff frequency higher than the base frequency f. c 1 / 2 of the fundamental frequency f c In addition, when the filter is a bandpass filter, the measuring device 1 generates a first cutoff frequency higher than the fundamental frequency f c The frequency of 1 / 2 is higher than the fundamental frequency f c Low, second cutoff frequency is lower than the fundamental frequency f c The frequency is n times higher than the fundamental frequency f c A filter having a frequency that is n+1 times lower than the frequency of the filter. n is a predetermined integer.

[0104] Next, in the filtering process S40, the measuring device 1 filters the first measurement data using the filter generated in the process S30 to generate second measurement data. That is, the measuring device 1 filters the first measurement data using the high-pass filter or band-pass filter generated in the process S30.

[0105] Next, in an integration process step S50, the measurement device 1 integrates the second measurement data generated in step S40 to generate third measurement data. For example, if the second measurement data is acceleration data, 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.

[0106] Next, in the measurement data output step S60, the measurement device 1 outputs the 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 also include the first measurement data, the second measurement data, and the like.

[0107] Then, the measuring device 1 repeats the processing of steps S10 to S60 until the measurement is completed in step S70 .

[0108] Figure 27 It shows Figure 26 This is a flowchart of an example of the steps of the fundamental frequency calculation step S20. Figure 27 The flowchart is equivalent to the aforementioned fundamental frequency f c The first step of the calculation method.

[0109] like Figure 27 As shown, first, in step S201, the measurement device 1 calculates a spectrum of first measurement data based on the observation data. For example, the measurement device 1 may perform a fast Fourier transform on the first measurement data to calculate the spectrum.

[0110] Then, in step S202, the measuring device 1 calculates the fundamental frequency f based on the spectrum calculated in step S201. c For example, the measuring device 1 may calculate the lowest frequency among the multiple frequencies corresponding to the multiple peaks in the spectrum as the fundamental frequency f c .

[0111] Figure 28 It shows Figure 26 This is a flowchart of another example of the steps of the fundamental frequency calculation step S20. Figure 28 The flowchart is equivalent to the fundamental frequency f c Steps of the second calculation method.

[0112] like Figure 28 As shown, first, in step S211, the measuring device 1 calculates the passing time t based on the first measurement data based on the observation data. s For example, the measuring device 1 may also calculate the time of the first negative peak when the railway vehicle 6 travels on the bridge 5 in the first measurement data as the bridge entry time ti , calculate the time of the last negative peak as the time of leaving the bridge t o , as in the previous formula (1), calculate the time from entering the bridge t i Time to exit the bridge t o The time is taken as the passing time t s .

[0113] Then, in step S212, the measuring device 1 calculates the passing time t s , the number C of railway vehicles 6 included in the pre-created environmental information T , the length of each vehicle of the railway vehicle 6, that is, the vehicle length L C (C m ) and the length of bridge 5, i.e., the bridge length L B , calculate the fundamental frequency f c For example, the measuring device 1 can also calculate the fundamental frequency f by using the above formula (2): c .

[0114] Figure 29 It shows Figure 26 This is a flowchart of another example of the steps of the fundamental frequency calculation step S20. Figure 29 The flowchart is equivalent to the fundamental frequency f c Steps of the third calculation method.

[0115] like Figure 29 As shown, first, in step S221, the measuring device 1 calculates the number of cycles T of the response of the railway vehicle 6 traveling on the bridge 5 to the observation point R based on the first measurement data based on the observation data. n For example, the measuring device 1 may also calculate the number of positive peaks P when the railway vehicle 6 travels on the bridge 5 in the first measurement data. p Or the negative peak number P n Count and calculate the number of cycles T using the previous formula (3) n .

[0116] Next, in step S222, the measuring device 1 calculates the transit time t based on the first measurement data. s For example, the measuring device 1 may also calculate the time of the first negative peak when the railway vehicle 6 travels on the bridge 5 in the first measurement data as the bridge entry time t i , calculate the time of the last negative peak as the time of leaving the bridge t o , as in the previous formula (1), calculate the time from entering the bridge t i Time to exit the bridge t o The time is taken as the passing time t s .

[0117] Then, in step S223, the measuring device 1 calculates the number of cycles T of the response calculated in step S221. n and the transit time t calculated in step S222 s To calculate the fundamental frequency f c Specifically, the measuring device 1 calculates the cycle number T as shown in the above formula (4). n Divide by the passage time t s , to calculate the fundamental frequency f c .

[0118] 1-4. Structure of Sensors, Measuring Devices, and Monitoring Devices

[0119] Figure 30 1 is a diagram showing a configuration example of the sensor 2, the measuring device 1, and the monitoring device 3. Figure 30 As shown, the sensor 2 includes a communication unit 21 , an acceleration sensor 22 , a processor 23 , and a storage unit 24 .

[0120] The storage unit 24 is a memory that stores various programs, data, and the like used by the processor 23 to perform calculations and control processes. The storage unit 24 also stores programs, data, and the like used by the processor 23 to implement predetermined application functions.

[0121] The acceleration sensor 22 detects acceleration generated in each of the three-axis directions.

[0122] The processor 23 controls the acceleration sensor 22 by executing the observation program 241 stored in the storage unit 24, generates observation data 242 based on the acceleration detected by the acceleration sensor 22, and stores the generated observation data 242 in the storage unit 24. In this embodiment, the observation data 242 is acceleration data.

[0123] 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 .

[0124] like Figure 30 As shown, the measurement device 1 includes a first communication unit 11 , a second communication unit 12 , a storage unit 13 , and a processor 14 .

[0125] The first communication unit 11 receives the observation data 242 from the sensor 2 and outputs the received observation data 242 to the processor 14 .

[0126] The storage unit 13 is a memory that stores programs, data, and the like used by the processor 14 to perform calculations and control processes. The storage unit 13 also stores various programs, data, and the like used by the processor 14 to implement predetermined application functions. Furthermore, the processor 14 may receive various programs, data, and the like via the communication network 4 and store them in the storage unit 13.

[0127] 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 .

[0128] In this 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 generation unit 143, a filter processing unit 144, an integration processing unit 145, and a measurement data output unit 146. Specifically, 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.

[0129] 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 the observation data 133. Figure 26 In the present embodiment, the observation data 133 is acceleration data.

[0130] The fundamental frequency calculation unit 142 calculates the fundamental frequency f of the deflection repeatedly generated on the bridge 5 due to the travel 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 as the observation data 133, or may be data obtained by performing a predetermined process such as a low-pass filter process on the acceleration data. For example, the fundamental frequency calculation unit 142 may calculate the frequency spectrum of the first measurement data and calculate the fundamental frequency f based on the calculated frequency spectrum. c Alternatively, the fundamental frequency calculation unit 142 may calculate the passing time t based on the first measurement data. s , based on the calculated transit time t s and the number C of railway vehicles 6 included in the environmental information 132 generated in advance and stored in the storage unit 13. T , the length of each vehicle of the railway vehicle 6, that is, the vehicle length L C (C m ) and the length of bridge 5, i.e., the bridge length L B , calculate the number of cycles T of the response n and through time t s , according to the calculated response cycle number T n and through time t s , calculate the fundamental frequency f c That is, the fundamental frequency calculation unit 142 performs Figure 26 The fundamental frequency calculation step S20 in the process is specifically performed Figure 27The processing of steps S201 and S202, Figure 28 The processing of steps S211 and S212 or Figure 29 Processing of steps S221, S222, and S223.

[0131] The filter generation unit 143 generates a filter based on the fundamental frequency f calculated by the fundamental frequency calculation unit 142. c , generating a filter with a variably set passband. For example, the filter may be a high-pass filter or a band-pass filter. The band-pass filter may also be composed of a high-pass filter and a low-pass filter. In the case where the filter is a high-pass filter, the filter generation unit 143 generates a filter with a cutoff frequency greater than the fundamental frequency f c The frequency of 1 / 2 is higher than the fundamental frequency f c In addition, when the filter is a bandpass filter, the filter generation unit 143 generates a filter with a first cutoff frequency higher than the fundamental frequency f. c 1 / 2 of the fundamental frequency f c , the second cutoff frequency is higher than the fundamental frequency f c n times the frequency and lower than the fundamental frequency f c The filter generating unit 143 generates a filter with a frequency n+1 times that of the filter. n is a predetermined integer. Figure 26 The processing of the filter generation step S30 in .

[0132] The filter processing unit 144 performs a filter process on the first measurement data using the filter generated by the filter generation unit 143 to generate the second measurement data. The filter processing unit 144 performs a filter process on the first measurement 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 a filter process on the first measurement data. Figure 26 The processing of the filtering process step S40 in .

[0133] The integration processing unit 145 performs integration processing on the second measurement data generated by the filter processing unit 144 to generate third measurement data. 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 perform a second integration on the second measurement data to generate displacement data as the third measurement data. Figure 26 The processing of the integration processing step S50 in .

[0134] 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 the like.

[0135] The measurement data output unit 146 reads 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 sends 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 Figure 26 The processing of the measurement data output step S60 in .

[0136] Thus, the measurement program 131 is a program that causes the measurement device 1 as a computer to execute Figure 26 The flowchart shows the procedures for each step.

[0137] like Figure 30 As shown, 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 .

[0138] 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 .

[0139] The display unit 33 displays various information under the control of the processor 32. The display unit 33 may be, for example, a liquid crystal display or an organic EL display. EL is the abbreviation for Electro Luminescence.

[0140] The operation unit 34 outputs operation data corresponding to the user's operation to the processor 32. The operation unit 34 may be, for example, an input device such as a mouse, a keyboard, or a microphone.

[0141] The storage unit 35 is a memory that stores various programs, data, and the like used by the processor 32 to perform calculations and control processes. The storage unit 35 also stores programs and data for causing the processor 32 to implement predetermined application functions.

[0142] The processor 32 acquires the measurement data 134 received by the communication unit 31 , evaluates the temporal changes in the passing speed of the railway vehicle 6 and the displacement of the bridge 5 based on the acquired measurement data 134 , generates evaluation information, and displays the generated evaluation information on the display unit 33 .

[0143] In the present embodiment, the processor 32 functions as the measurement data acquisition unit 321 and the monitoring unit 322 by executing the 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.

[0144] 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 .

[0145] The monitoring unit 322 evaluates the transit speed of the railway vehicle 6 based on the measurement data sequence 352 stored in the storage unit 35 and statistically evaluates the temporal change in the displacement of the bridge 5. 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 transit speed of the railway vehicle 6 and the condition of the bridge 5 based on the evaluation information displayed on the display unit 33.

[0146] The monitoring unit 322 may perform processes such as monitoring the railway vehicle 6 and determining abnormality of the bridge 5 based on the measurement data sequence 352 stored in the storage unit 35 .

[0147] Furthermore, based on the operation data output from the operation unit 34, the processor 32 transmits information for adjusting the operating conditions of the measurement device 1 and the sensor 2 to the measurement device 1 via the communication unit 31. The measurement device 1 adjusts its operating conditions based on the information received via the second communication unit 12. Furthermore, the measurement device 1 transmits the 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 sensor 2 adjusts its operating conditions based on the information received via the communication unit 21.

[0148] In addition, the processor 14, the processor 23, and the processor 32 may implement the functions of each part through separate hardware, or may implement the functions of each part through integrated hardware. For example, the processor 14, the processor 23, and the processor 32 include hardware, and the hardware may include at least one of a circuit for processing digital signals and a circuit for processing analog signals. The processors 14, 23, and 32 may also be a CPU, a GPU, or a DSP, etc. CPU is the abbreviation of Central Processing Unit, GPU is the abbreviation of Graphics Processing Unit, and DSP is the abbreviation of Digital Signal Processor. In addition, the processor 14, the processor 23, and the processor 32 may be constructed as a customized IC such as ASIC and implement the functions of each part, or may implement the functions of each part through a CPU and an ASIC. ASIC is the abbreviation of Application Specific Integrated Circuit, and IC is the abbreviation of Integrated Circuit.

[0149] Furthermore, the storage units 13, 24, and 35 may be composed of, for example, various IC memories such as ROM, flash ROM, and RAM, as well as recording media such as hard disks and memory cards. ROM stands for Read Only Memory, RAM stands for Random Access Memory, and IC stands for Integrated Circuit. The storage units 13, 24, and 35 include non-volatile information storage devices, which are computer-readable devices or media. Various programs and data may also be stored in these information storage devices. The information storage devices may also be optical disks such as DVDs and CDs, hard disk drives, card-type memories, or various other types of memory such as ROMs.

[0150] In addition, although Figure 30 Although only one sensor 2 is shown in the figure, multiple sensors 2 may each generate observation data 242 and transmit the data to the measuring device 1. In this case, the measuring device 1 receives the multiple observation data 242 transmitted from the multiple sensors 2, generates multiple measurement data 134, and transmits the data to the monitoring device 3. The monitoring device 3 also receives the multiple measurement data 134 transmitted from the measuring device 1 and monitors the state of the bridge 5 based on the received multiple measurement data 134.

[0151] 1-5. Effects

[0152] As described above, in the measurement method of the first embodiment, the measurement device 1 uses the fundamental frequency f according to the deflection repeatedly generated on the bridge 5 due to the travel of the railway vehicle 6. c The filter with a variably set passband is used to filter the first measurement data based on the observation data to generate the second measurement data. Therefore, in the measurement method of the first embodiment, even if the fundamental frequency f c Due to the time t s 、Number of vehicles C T , bridge length L B The measurement device 1 can also use appropriate high-pass filters or band-pass filters to generate second measurement data with offset errors and noise components effectively attenuated. Therefore, according to this measurement method, it is possible to reduce drift caused by the measurement device 1 performing integration processing on the second measurement data.

[0153] For example, the measuring device 1 can measure the frequency by using the ratio of the cut-off frequency to the fundamental frequency f c The frequency of 1 / 2 is higher than the fundamental frequency f c The first measurement data is filtered by a high-pass filter with a low frequency, and the base frequency f is generated. cTherefore, the measurement device 1 can reduce drift and low-frequency noise components generated by integrating the second measurement data.

[0154] In addition, for example, the measuring device 1 uses the first cutoff frequency to compare with the fundamental frequency f c The frequency of 1 / 2 is higher than the fundamental frequency f c Low, second cutoff frequency is lower than the fundamental frequency f c The frequency is n times higher than the fundamental frequency f c The first measurement data is filtered by a band-pass filter with a frequency that is n+1 times lower than the fundamental frequency f. c The noise components with frequencies below 1 / 2 of the fundamental frequency are attenuated, and the fundamental frequency f is c Therefore, the measuring device 1 can reduce the fundamental frequency f to c The signal component and its second to nth order harmonic signal components are taken as measurement objects, and drift, low-frequency noise components and high-frequency noise components generated by integrating the second measurement data are reduced.

[0155] In particular, the measurement device 1 can reduce drift and low-frequency noise components generated in the third measurement data, which are velocity data or displacement data, by performing integration processing on the second measurement data, which are acceleration data.

[0156] 2. Second Implementation

[0157] Hereinafter, regarding the second embodiment, the same components as those of the first embodiment are denoted by the same reference numerals, and the description overlapping with that of the first embodiment is omitted or simplified, and the description will focus on the differences from the first embodiment.

[0158] Figure 31 This is a flowchart showing an example of the steps of the measurement method of the second embodiment. Figure 31 In, with Figure 26 The same steps are marked with the same reference numerals. Figure 31 Steps shown.

[0159] like Figure 31 As shown, the measurement device 1 first 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 of the first embodiment, and therefore their description is omitted.

[0160] Next, the measuring device 1 performs the filter selection step S32 instead of Figure 26In the filter selection step S32, the measurement device 1 selects the filter based on the fundamental frequency f calculated in step S20. c The filter information is information of a plurality of filters having different passbands prepared in advance, that is, filter information, and a filter to be used for filtering the first measurement data is selected from the plurality of filters.

[0161] For example, the filter information is information indicating the correspondence between multiple fundamental frequencies and coefficient values ​​of multiple filters with different passbands. The multiple filters are FIR filters, and their orders may be the same. FIR is the abbreviation of Finite Impulse Response. The multiple filters may also be high-pass filters whose cutoff frequencies are higher than 1 / 2 of the fundamental frequency and lower than the fundamental frequency. In addition, the multiple filters may also be band-pass filters whose first cutoff frequency is higher than 1 / 2 of 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. n is a predetermined integer. The band-pass filter may also be composed of a high-pass filter and a low-pass filter. The measuring device 1 may also refer to the filter information and select a filter closest to the calculated fundamental frequency f. c Thus, the measuring device 1 is based on the fundamental frequency f c , choose according to the fundamental frequency f c A filter with a variably set passband.

[0162] Next, in the filtering process S40, the measuring device 1 filters the first measurement data using the filter selected in the process S30 to generate second measurement data. That is, the measuring device 1 filters the first measurement data using the high-pass filter or band-pass filter selected in the process S30.

[0163] Next, the measurement device 1 performs an integration process step S50 and further performs a measurement data output step S60. The processes of the integration process step S50 and the measurement data output step S60 are the same as those of the first embodiment, and therefore their description is omitted.

[0164] Then, the measuring device 1 repeats the processing of steps S10 to S60 until the measurement is completed in step S70 .

[0165] The configuration and function of the sensor 2 and the monitoring device 3 in the second embodiment are the same as those in the first embodiment, and therefore, illustration thereof is omitted. Figure 32 It is a diagram showing a configuration example of a measuring device 1 in the second embodiment.

[0166] like Figure 32As shown, the measuring device 1 in the second embodiment includes a first communication unit 11, a second communication unit 12, a storage unit 13, and a processor 14, similarly to the first embodiment. 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 their description is omitted.

[0167] In the second 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 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 their description is omitted. In addition, the observation data acquisition unit 141 performs Figure 31 The observation data acquisition step S10 is performed. In addition, the fundamental frequency calculation unit 142 performs Figure 31 The fundamental frequency calculation step S20 is specifically performed Figure 27 The processing of steps S201 and S202, Figure 28 The processing of steps S211 and S212 or Figure 29 In addition, the integral processing unit 145 performs the processing of steps S221, S222, and S223. Figure 31 In addition, the measurement data output unit 146 performs the integration processing step S50. Figure 31 The measurement data output step S60 is performed.

[0168] The filter selection unit 147 selects the fundamental frequency f calculated by the fundamental frequency calculation unit 142 based on the fundamental frequency f c The filter information 135 , which is information of a plurality of filters having different passbands and is prepared and stored in advance in the storage unit 13 , is used to select a filter used for filtering the first measurement data based on the observation data from among the plurality of filters.

[0169] For example, the filter information 135 is information indicating the correspondence between multiple fundamental frequencies and coefficient values ​​of multiple filters having different passbands. The multiple filters are FIR filters, and their orders may be the same. The multiple filters may also be high-pass filters whose cutoff frequencies are higher than 1 / 2 of the fundamental frequency and lower than the fundamental frequency. In addition, the multiple filters may also be band-pass filters whose first cutoff frequency is higher than 1 / 2 of 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. n is a predetermined integer. The band-pass filter may also be composed of a high-pass filter and a low-pass filter. The filter selection unit 147 may also refer to the filter information 135 and select the fundamental frequency f calculated by the fundamental frequency calculation unit 142 that is closest to the fundamental frequency. c Thus, the filter selection unit 147 selects the filter corresponding to the fundamental frequency f based on the fundamental frequency f. c , choose according to the fundamental frequency f c A filter with a variably set passband.

[0170] The filter processing unit 144 performs a filter process on the first measurement data using the filter selected by the filter selection unit 147 to generate the second measurement data. The filter processing unit 144 performs a filter process on the first measurement data using the high-pass filter or the band-pass filter selected by the filter selection unit 147. That is, the filter processing unit 144 performs a filter process on the first measurement data. Figure 31 The processing of the filtering process step S40 in .

[0171] The other functions of the measuring device 1 in the second embodiment are the same as those in the first embodiment, and therefore description thereof will be omitted.

[0172] According to the measuring method of the second embodiment described above, the same operational effects as those of the measuring method of the first embodiment can be obtained.

[0173] 3. Modifications

[0174] The present invention is not limited to the present embodiment, and various modifications can be implemented within the scope of the gist of the present invention.

[0175] For example, in the above-mentioned embodiments, each sensor 2 is respectively provided on the main beam G of the upper structure 7, but may be provided on the surface or inside of the upper structure 7, on the lower surface of the floor F, on the pier 8a, or the like.

[0176] In the above-described embodiments, the sensor 2 serving as the observation device is an acceleration sensor that outputs acceleration data. However, the observation device may also be a velocity sensor. In the case where the observation device is a velocity sensor, the measurement device 1 may calculate displacement by performing a filtering process similar to the above-described embodiments on the first measurement data based on the velocity data outputted from the velocity sensor, followed by an integration process.

[0177] The above-mentioned embodiment and modification examples are merely examples and are not limiting. For example, the embodiments and modification examples may be appropriately combined.

[0178] The present invention includes structures that are substantially the same as the structures described in the embodiments, such as structures having the same functions, methods, and results, or structures having the same purposes and effects. In addition, the present invention includes structures that replace non-essential parts of the structures described in the embodiments. In addition, the present invention includes structures that have the same effects as the structures described in the embodiments, or structures that can achieve the same purposes. In addition, the present invention includes structures that add known technologies to the structures described in the embodiments.

[0179] The following contents are derived from the above-mentioned embodiment and modification examples.

[0180] One form of the measurement method includes the following steps: an observation data acquisition step of acquiring observation data output from an observation device at an observation point of an observation bridge, wherein the observation data 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 the fundamental frequency of deflection repeatedly generated on the bridge due to the travel of the railway vehicle based on first measurement data based on the observation data; a filtering processing step of filtering the first measurement data using a filter whose passband is variably set according to the fundamental frequency to generate second measurement data; and an integration processing step of integrating the second measurement data to generate third measurement data.

[0181] In this measurement method, second measurement data is generated by filtering first measurement data based on observation data using a filter whose passband is variably set according to the fundamental frequency of the deflection repeatedly generated by railway vehicles on the bridge. Therefore, even if the fundamental frequency varies depending on factors such as the time it takes for railway vehicles to pass through the bridge, the number of railway vehicles on the bridge, and the length of the bridge, this measurement method can generate second measurement data that effectively attenuates offset errors and noise components using an appropriate filter. Consequently, this measurement method can reduce drift caused by integrating the second measurement data.

[0182] One aspect of the measurement method may include a filter generating step of generating the filter in which the passband is variably set based on the fundamental frequency.

[0183] One aspect of the measurement method may include a filter selection step of selecting the filter used for the filtering process from among the plurality of filters having different fundamental frequencies and passbands stored in a storage unit.

[0184] In one embodiment of the measurement method, the filter may be a high-pass filter, and a cutoff frequency of the filter may be higher than a frequency of 1 / 2 of the fundamental frequency and lower than the fundamental frequency.

[0185] According to this measurement method, second measurement data can be generated in which noise components having frequencies equal to or less than 1 / 2 of the fundamental frequency are attenuated. Therefore, drift and low-frequency noise components generated by integrating the second measurement data can be reduced.

[0186] In one embodiment of the measurement method, the filter may be a bandpass filter, a first cutoff frequency of the filter may be higher than a frequency of 1 / 2 of the fundamental frequency and lower than the fundamental frequency, and for a specified integer n greater than 2, a second cutoff frequency of the filter which is higher than the first cutoff frequency may be higher than a frequency of n times the fundamental frequency and lower than a frequency of n+1 times the fundamental frequency.

[0187] This measurement method generates second measurement data after attenuating noise components at frequencies less than or equal to half the fundamental frequency, thereby reducing drift and low-frequency noise components generated by integrating the second measurement data. Furthermore, this measurement method generates second measurement data after attenuating noise components at frequencies greater than or equal to n+1 times the fundamental frequency, thereby focusing on the fundamental frequency signal component and its higher harmonic signal components from the second to the nth order as measurement targets, reducing high-frequency noise components generated by integrating the second measurement data.

[0188] In one aspect of the measurement method, the observation device may be an acceleration sensor installed on the bridge.

[0189] In one embodiment of the measurement method, the second measurement data may be acceleration data, and in the integration processing step, 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.

[0190] According to this measurement method, by performing integration processing on the second measurement data, which is acceleration data, it is possible to reduce drift and low-frequency noise components generated in velocity data or displacement data.

[0191] In one embodiment of the measurement method, the fundamental frequency calculation step may include the steps of: calculating a frequency spectrum of the first measurement data; and calculating the fundamental frequency based on the frequency spectrum.

[0192] According to this measurement method, although the calculation load is high due to the fast Fourier transform, the fundamental frequency can be accurately calculated.

[0193] In one aspect of the measurement method, the fundamental frequency calculation step may include the following steps: calculating the time required for the railway vehicle to pass through the bridge, i.e., the passing time, based on the first measurement data; and calculating the fundamental frequency based on the passing time and the number of railway vehicles, the length of each railway vehicle, and the length of the bridge included in pre-produced environmental information.

[0194] According to this measurement method, fast Fourier transform is not required, and thus the fundamental frequency can be calculated with a low load.

[0195] In one aspect of the measurement method, the fundamental frequency calculation process may include the following processes: calculating the number of cycles of the response based on the first measurement data; calculating the time required for the railway vehicle to pass through the bridge, i.e., the passing time, based on the first measurement data; and calculating the fundamental frequency based on the number of cycles of the response and the passing time.

[0196] According to this measurement method, fast Fourier transform is not required, and thus the fundamental frequency can be calculated with a low load.

[0197] One embodiment of a measuring device includes: an observation data acquisition unit that acquires observation data output from an observation device at an observation point of an observation bridge, the observation data including a response to an action of a railway vehicle traveling on the bridge on the observation point; a fundamental frequency calculation unit that calculates, based on first measurement data based on the observation data, the fundamental frequency of deflection repeatedly generated on the bridge due to the travel of the railway vehicle; a filtering unit that performs filtering processing on the first measurement data using a filter whose passband is variably set according to the fundamental frequency to generate second measurement data; and an integration processing unit that performs integration processing on the second measurement data to generate third measurement data.

[0198] This measurement device generates second measurement data by filtering first measurement data based on observation data using a filter whose passband is variably set according to the fundamental frequency of deflection repeatedly generated by railway vehicles traveling on the bridge. Therefore, even if the fundamental frequency varies depending on factors such as the time it takes for railway vehicles to pass through the bridge, the number of railway vehicles on the bridge, and the length of the bridge, the measurement device can use an appropriate filter to generate second measurement data that effectively attenuates offset errors and noise components. Consequently, this measurement device can reduce drift caused by integrating the second measurement data.

[0199] One embodiment of a measurement system includes one embodiment of the measurement device and the observation device.

[0200] One method of a measurement program causes a computer to execute the following steps: an observation data acquisition step for acquiring observation data output from an observation device at an observation point for observing a bridge, wherein the observation data includes a response to an action of a railway vehicle traveling on the bridge on the observation point; a fundamental frequency calculation step for calculating, based on first measurement data based on the observation data, the fundamental frequency of deflection repeatedly generated on the bridge due to the travel of the railway vehicle; a filtering processing step for filtering the first measurement data using a filter whose passband is variably set according to the fundamental frequency to generate second measurement data; and an integration processing step for integrating the second measurement data to generate third measurement data.

[0201] In this measurement program, a computer generates second measurement data by filtering first measurement data based on observational data using a filter whose passband is variably set according to the fundamental frequency of the deflection repeatedly generated by railway vehicles on the bridge. Therefore, even if the fundamental frequency varies depending on factors such as the time it takes for railway vehicles to pass through the bridge, the number of railway vehicles on the bridge, and the length of the bridge, the computer can use an appropriate filter to generate second measurement data that effectively attenuates offset errors and noise components. Consequently, this measurement program can reduce drift caused by integrating the second measurement data.

Claims

1. A measurement method comprising the following steps: The observation data acquisition step acquires observation data output from an observation device at an observation point for observing the bridge, wherein: The observation data 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, based on first measurement data based on the observation data, a fundamental frequency of deflection repeatedly generated on the bridge due to the travel of the railway vehicle; a filtering step of filtering the first measurement data using a filter whose passband is variably set according to the fundamental frequency to generate second measurement data; as well as The integration processing step performs integration processing on the second measurement data to generate third measurement data.

2. The measurement method according to claim 1, wherein: The measurement method includes a filter generation step of generating the filter in which the passband is variably set based on the fundamental frequency.

3. The measurement method according to claim 1, wherein: The measurement method includes a filter selection step of selecting the filter used for the filtering process from among a plurality of filters having different fundamental frequencies and passbands stored in a storage unit based on information about the plurality of filters.

4. The measurement method according to claim 1, wherein: The filter is a high-pass filter, The cutoff frequency of the filter is higher than a frequency of 1 / 2 of the fundamental frequency and lower than the fundamental frequency.

5. The measurement method according to claim 1, wherein: The filter is a bandpass filter, The first cut-off frequency of the filter is higher than 1 / 2 of the fundamental frequency and lower than the fundamental frequency, For a predetermined integer n equal to or greater than 2, a second cutoff frequency of the filter higher than the first cutoff frequency is higher than a frequency n times the fundamental frequency and lower than a frequency n+1 times the fundamental frequency. The measuring device according to claim 1 , wherein: The observation device is an acceleration sensor installed on the bridge.

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

8. The measurement method according to claim 1, wherein: The fundamental frequency calculation process includes the following steps: calculating a frequency spectrum of the first measurement data; and The fundamental frequency is calculated based on the frequency spectrum.

9. The measurement method according to claim 1, wherein: The fundamental frequency calculation process includes the following steps: calculating, based on the first measurement data, a time required for the railway vehicle to pass through the bridge, i.e., a passing time; as well as The fundamental frequency is calculated based on the passing time, the number of the railway vehicles, the length of each railway vehicle, and the length of the bridge, which are included in pre-created environmental information.

10. The measurement method according to claim 1, wherein: The fundamental frequency calculation process includes the following steps: calculating the number of cycles of the response based on the first measurement data; calculating a time required for the railway vehicle to pass through the bridge, that is, a passing time, based on the first measurement data; and The fundamental frequency is calculated based on the number of cycles of the response and the transit time.

11. A measuring device comprising: an observation data acquisition unit that acquires observation data output from an observation device observing an observation point of a bridge, the observation data including a response to an action of a railway vehicle traveling on the bridge on the observation point; a fundamental frequency calculation unit for calculating a fundamental frequency of deflection repeatedly generated on the bridge due to travel of the railway vehicle based on first measurement data based on the observation data; a filter processing unit configured to perform a filter process on the first measurement data using a filter having a passband variably set according to the fundamental frequency, thereby generating second measurement data; as well as The integration processing unit performs integration processing on the second measurement data to generate third measurement data. 12 . A measurement system comprising the measurement device according to claim 11 and the observation device.

13. A measurement program product causing a computer to execute the following steps: The observation data acquisition step acquires observation data output from an observation device at an observation point for observing the bridge, wherein: The observation data 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, based on first measurement data based on the observation data, a fundamental frequency of deflection repeatedly generated on the bridge due to the travel of the railway vehicle; a filtering step of filtering the first measurement data using a filter whose passband is variably set according to the fundamental frequency to generate second measurement data; as well as The integration processing step performs integration processing on the second measurement data to generate third measurement data.

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