Time acquisition method, time acquisition device, time acquisition system, and time acquisition program

Abstract

To acquire entry time and exit time by simple processing.SOLUTION: Time is acquired through a data acquisition step for acquiring time sequence data which indicates the time change of displacement of a structure on the basis of the physical quantity generated at a prescribed observation point in the structure in response to the travel of a formation movable body formed by one or more movable bodies, a removal step for removing a vibration component included in the time sequence data, and a time acquisition step for acquiring the entry time at which the formation movable body enters the structure and the exit time at which the formation movable body leaves the structure on the basis of the time sequence data after removal of the vibration component.SELECTED DRAWING: Figure 16

Description

[Technical field]

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

[0002] In recent years, many social infrastructures have deteriorated with age, and there is a demand for methods to diagnose the condition of structures that make up social infrastructure, such as railway bridges. Patent Document 1 discloses a method for investigating the structural performance of railway bridges that makes it possible to appropriately investigate and evaluate the structural performance of bridges by using observed data on the acceleration response of the bridge when a train is running. The method for investigating the structural performance of railway bridges in Patent Document 1 is characterized in that it formulates a theoretical analysis model of the dynamic response of a railway bridge when a train is running, with the train as a moving load train and the bridge as a simple beam, measures the acceleration of the bridge when a train is running, and estimates unknown parameters of the theoretical analysis model from the acceleration data by inverse analysis. Furthermore, Patent Document 2 discloses a method for determining the impact coefficient (dynamic response component) of a bridge by using the vehicle vertical acceleration response of a running train when passing over the bridge. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 6543863 [Patent Document 2] Patent No. 6467304 Summary of the Invention [Problem to be solved by the invention]

[0004] In Patent Document 1, time-related information, such as the time when a vehicle passes over a bridge and the time when the axle enters the bridge, is used as a parameter of the theoretical analysis model. When analyzing time-series data of physical quantities occurring at an observation point of a structure, such as analyzing a model of displacement when a vehicle enters a bridge, the time when the train moves onto the structure and the time when it leaves the structure are often obtained. The time when the train moves onto the structure and the time when it leaves the structure are preferably obtained by simple processing. [Means for solving the problem]

[0005] A time acquisition method for solving the above problem includes an acquisition step of acquiring time series data including physical quantities that occur at a predetermined observation point on a structure as a response to a formed mobile body, which is composed of one or more moving bodies, moving through the structure, a removal step of removing vibration components contained in the time series data, and a time acquisition step of acquiring an entry time at which the formed mobile body enters the structure and an exit time at which the formed mobile body exits the structure based on the time series data after the vibration components have been removed.

[0006] A time acquisition device for solving the above problem comprises a data acquisition unit that acquires time series data including physical quantities that occur at a predetermined observation point on a structure as a response to a formed mobile body, which is made up of one or more moving bodies, moving through the structure, a removal unit that removes vibration components contained in the time series data, and a time acquisition unit that acquires the entry time at which the formed mobile body enters the structure and the exit time at which the formed mobile body exits the structure based on the time series data after the vibration components have been removed.

[0007] A time acquisition system for solving the above problem is a time acquisition system comprising a time acquisition device and a sensor, wherein the time acquisition device comprises a data acquisition unit that acquires time series data including physical quantities measured via the sensor, the physical quantities being generated at a predetermined observation point on a structure in response to a formed mobile body, which is made up of one or more moving bodies, moving through the structure, the physical quantities being measured via the sensor, a removal unit that removes vibration components contained in the time series data, and a time acquisition unit that acquires the entry time at which the formed mobile body enters the structure and the exit time at which the formed mobile body exits the structure based on the time series data after the vibration components have been removed.

[0008] A program for solving the above problem causes a computer to execute an acquisition step of acquiring time series data including physical quantities that occur at a predetermined observation point on a structure as a response to a train of one or more moving bodies moving through the structure, a removal step of removing vibration components contained in the time series data, and a time acquisition step of acquiring the entry time at which the train of one or more moving bodies enters the structure and the exit time at which the train of one or more moving bodies exits the structure based on the time series data after the vibration components have been removed. [Brief description of the drawings]

[0009] [Figure 1] FIG. 2 is a block diagram showing the configuration of a time acquisition system. [Diagram 2] FIG. [Diagram 3] FIG. 1 is a diagram showing the dimensions of a unit bridge girder. [Figure 4] FIG. 1 is a diagram showing the dimensions of a railway vehicle. [Diagram 5] FIG. 2 is a diagram showing an overview of a unit bridge girder. [Figure 6] FIG. 13 is a diagram illustrating the bending moment in a unit bridge girder. [Figure 7] FIG. 13 is a diagram showing an overview of the deflection of a unit girder due to wheels. [Figure 8] FIG. 1 is a diagram showing an overview of deflection of a unit bridge girder due to a railway vehicle. [Figure 9] FIG. 1 is a diagram showing an overview of deflection of a unit bridge girder due to a railway train. [Figure 10] FIG. 13 is a diagram showing the deflection of a unit bridge girder due to each railway vehicle. [Figure 11] FIG. 13 is a diagram showing an FFT result of time series data. [Figure 12] FIG. 13 is a diagram showing the amount of deflection after high-pass filter processing. [Figure 13] FIG. 1 is a diagram illustrating a configuration of a time acquisition system. [Figure 14] FIG. 13 is a diagram showing time series data of displacement of a unit bridge girder. [Figure 15] FIG. 13 is a diagram showing an FFT result of time series data. [Figure 16] FIG. 13 is a diagram showing time-series data that has been subjected to low-pass filtering. [Figure 17] 11A and 11B are diagrams for explaining a process of deriving an entry time and an exit time. [Figure 18] FIG. 13 is a diagram showing time-series data in which low-pass filtering has been performed on time-series data including drift noise. [Figure 19] 13 is a flowchart showing a derivation process. [Figure 20] FIG. 13 is a diagram showing an example of measurement of time-series data including drift noise. [Figure 21] FIG. 13 is a diagram showing time-series data that has been subjected to high-pass filtering. [Figure 22] FIG. 13 shows a correction curve produced by polarity reversal. [Figure 23] FIG. 13 is a diagram showing a straight line on which a correction curve is based. [Figure 24] FIG. 13 is a diagram illustrating generation of a correction curve. [Diagram 25] FIG. 13 is a diagram illustrating an example of a correction curve. [Figure 26] FIG. 13 is a diagram showing time series data U(k) generated by a correction curve. [Figure 27] FIG. 13 is a diagram showing a plurality of provisionally set threshold values. [Figure 28]It is a diagram showing the time difference between the time change of the amount of deflection obtained with a temporarily set threshold value and the time series data. [Figure 29] It is a diagram showing a method for calculating a threshold value. [Diagram 30] It is a diagram showing an example of the time differentiation of time series data. [Diagram 31] It is a diagram showing an example of the time differentiation of time series data. [Diagram 32] It is a diagram showing gain frequency characteristics.

Mode for Carrying Out the Invention

[0010] Here, embodiments of the present invention will be described in the following order. (1) Configuration of the time acquisition system: (1-1) Outline of the time acquisition system: (1-2) Deflection model: (1-3) Verification experiment: (1-4) Details of elements: (2) Derivation process: (3-1) Second embodiment: (3-2) Third embodiment: (3-3) Fourth embodiment: (4) Other embodiments:

[0011] (1) Configuration of the time acquisition system: (1-1) Outline of the time acquisition system: FIG. 1 is a block diagram showing an example of the configuration of a time acquisition system 10 according to the present embodiment. The time acquisition system 10 is a system that derives the number of railway vehicles included in a railway train 6 based on time-series data including physical quantities at a predetermined observation point on a bridge 5 over which a railway train 6 formed of one or more railway vehicles moves. The railway train 6 is an example of a formation moving body. Each of the railway vehicles included in the railway train 6 is an example of a moving body. The bridge 5 is an example of a structure over which a moving body moves. Each railway vehicle of the railway train 6 moves on the bridge 5 via wheels provided on the axles. As shown in FIG. 1, the time acquisition system 10 includes a measurement device 1, at least one sensor device 2 provided in the superstructure 7 of the bridge 5, and a server device 3.

[0012] The measurement device 1 calculates the displacement of the deflection of the superstructure 7 due to the running of the railway train 6 (displacement in the vertical direction) based on the acceleration data, which is the physical quantity output from each sensor device 2. The measurement device 1 is installed, for example, on the abutment 8b. The measurement device 1 and the server device 3 can communicate with each other via a communication network 4 such as a wireless network of a mobile phone and the Internet. The measurement device 1 transmits information on the displacement of the superstructure 7 due to the running of the railway train 6 to the server device 3. The server device 3 derives the number of railway vehicles formed in the railway train 6 based on the transmitted displacement data.

[0013] In this embodiment, the bridge 5 is a railway bridge, such as a steel bridge, a girder bridge, or an RC bridge. RC is an abbreviation for Reinforced Concrete. In this embodiment, the bridge 5 is a structure to which BWIM (Bridge Weigh In Motion) can be applied. BWIM is a technology that measures the weight and number of axles of a moving object passing over a bridge by treating the bridge as a "scale" and measuring the deformation of the bridge. A bridge that can analyze the weight of a moving object passing over the bridge from the response of the deformation and strain of the bridge is considered to be a structure to which BWIM can be applied. Therefore, the weight of a moving object moving over the bridge can be measured by a BWIM system that applies a physical process between the action on the bridge and the response. The weight of the moving object is measured by measuring the correlation coefficient between the displacement and the load in advance, and deriving the load of the moving object passing over the bridge using the correlation coefficient from the measurement result of the displacement of the bridge when the moving object passes over the bridge.

[0014] The bridge 5 includes a superstructure 7, which is a portion on which the moving body moves, and a substructure 8 that supports the superstructure 7. FIG. 2 is a cross-sectional view of the superstructure 7 cut along the line AA in FIG. 1. As shown in FIGS. 1 and 2, the superstructure 7 includes a bridge deck 7a including a deck F, a main girder G, a cross girder (not shown), a support 7b, a rail 7c, a sleeper 7d, and a ballast 7e. As shown in FIG. 1, the substructure 8 includes a pier 8a and an abutment 8b. The superstructure 7 is a structure that is bridged between adjacent abutments 8b and pier 8a, between two adjacent abutments 8b, or between two adjacent piers 8a. Hereinafter, the abutments 8b and pier 8a are collectively referred to as a support portion. In this embodiment, a support that is a pair of support portions and a portion of the bridge girder of the superstructure 7 that is bridged between the pair of support portions are collectively referred to as a bridge girder. In other words, a simple beam-like structure with both ends supported by two supports is considered to be one bridge girder. Therefore, the bridge 5 shown in Figure 1 includes two bridge girders. Below, each bridge girder included in the bridge 5 is referred to as a unit bridge girder.

[0015] The measuring device 1 and the sensor device 2 are connected, for example, by wire or wirelessly, and communicate with each other via a communication network such as a CAN (Controller Area Network). The sensor device 2 is used to measure a predetermined physical quantity used to derive a displacement (deflection) at an observation point set on the superstructure 7. In this embodiment, this predetermined physical quantity is acceleration. In addition, in this embodiment, the sensor device 2 is installed at this observation point. In addition, the sensor device 2 includes an acceleration sensor such as a quartz acceleration sensor or a MEMS (Micro Electro Mechanical Systems) acceleration sensor. In this embodiment, the sensor device 2 outputs acceleration data for deriving the displacement of the superstructure 7 due to the movement of the railway train 6, which is a moving body at the observation point, but of course, the sensor device 2 may output a physical quantity other than acceleration. In addition, the sensor device 2 may output data that directly indicates the displacement, or may output data that indirectly indicates the displacement. In any case, it is sufficient that time-series data of the displacement of the structure can be obtained based on the physical quantity output from the sensor device 2.

[0016] In this embodiment, the sensor device 2 is installed in the center of the superstructure 7 in the longitudinal direction, specifically, in the center of the main girder G in the longitudinal direction. However, the sensor device 2 only needs to detect the acceleration for calculating the displacement of the superstructure 7, and the installation position is not limited to the center of the superstructure 7. If the sensor device 2 is installed on the deck F of the superstructure 7, it may be destroyed by the running of the railway train 6, and the measurement accuracy may be affected by local deformation of the bridge deck 7a. Therefore, in the example of FIG. 1 and FIG. 2, the sensor device 2 is installed on the main girder G of the superstructure 7. The deck F and main girder G of the superstructure 7 are deflected in the vertical direction by the load of the railway train 6 running on the superstructure 7. Each sensor device 2 measures the acceleration of the deflection of the deck F and main girder G due to the load of the railway train 6 running on the superstructure 7.

[0017] (1-2) Deflection model: Here, we will explain the model of bridge deflection when a train moves across one unit bridge girder. Here, the model is information such as a formula that shows the correspondence between predetermined information and the estimated results. In the following, the number of railroad cars (number of vehicles) in the train moving across the bridge is defined as N. The approach time, which is the time when the train approaches the unit bridge girder, is defined as t i Here, the entry of a train into a unit bridge girder is defined as the entry of train C 1 The wheel of the front axle of the first railcar (the first railcar from the front of the railcar train) enters the unit girder. In the following, the exit time, which is the time when the railcar exits the unit girder, is defined as t o Here, the exit of a train from a unit bridge girder is defined as the exit of a train C N The wheel of the rearmost axle of the last train (the last train of the train) has left the unit girder. In the following, the period during which the train passes through the unit girder (time t i From time t o The period until s In the following, N, t i , t o , t s These are compiled together and used as observation information.

[0018] In the following, the bridge length, which is the length of a unit bridge girder in the direction of travel of a moving object in the direction of travel of a railway train, is defined as L B The distance from the end of the unit bridge girder in the longitudinal direction toward the direction in which the train is approaching to the observation point is defined as L x In Figure 3, B and L x In the following, the end of the unit girder in the longitudinal direction that is on the side where the train is approaching is referred to as the approach end. In addition, in the following, the end of the unit girder in the longitudinal direction that is on the side where the train is leaving is referred to as the exit end. In addition, the vehicle length, which is the length of the mth train from the front in the direction of travel, is defined as L C (m). In the following, L C (1)~L C (N) to L C The mth train car from the front of the train is called Cm Also, railroad car C m The number of axles in r (m). In the following, r (1)~a r (N) to a r In the following, we will refer to the railcar C m in a r (m) axles of railcar C m Starting from the top, the first axis, the second axis, the third axis, ..., a r (m) axis and distance.

[0019] In addition, railcar C m The distance from the front end of the vehicle in the direction of travel to the leading axle (axle 1) is L a (a w (m, 1)), where a w (α, β) indicates the β-th axle from the leading axle of the α-th railcar in the railcar train. m The distance between the n-1 axis (n: an integer of 2 or more) and the n axis in a (a w (m, n)). That is, for β of 2 or more, L a (a w (α, β)) is a railway train C α The distance between the β axis and the (β-1) axis in a (a w (α, 1)) is a railway train C α 1 axle and railroad train C α In the following, L a (a w (1, 1))~L a (a w (N, a r (N))) to L a Collectively referred to as L a Each indicates the position of the corresponding axle on the corresponding railcar. For example, L a (a w (m, 1)) is a railcar C m At L from the tip a (a w (m, 1)) behind the axis. Also, La (a w (m, 2)) is the railcar C m In this case, from axis 1 to L a (a w This indicates that there are two axes behind the distance (m, 2).

[0020] Here, the train is made up of rail cars with the same four-axle configuration: r (m) (m = 1, 2, . . . , N) is 4. m L in C (m), L a (a w (m, 1)), L a (a w (m, 2)), L a (a w (m, 3)), L a (a w (m, 4)). In the following, L B , L x , L C , a r , L a These are summarized as environmental information.

[0021] t s As shown in the following equation (1), t o and i It is calculated as the difference between

number

[0022] In addition, the total number of wheels of a railway train, T ar is calculated using the following equation (2).

number

[0023] From the first axle of the leading railcar C1, the m-th railcar C m The distance to the n-axis is D wa (a w (m, n)). D wa (aw (m, n)) is calculated from the following equation (3).

number

[0024] From one axle of the front railcar C1 to the rear railcar C N The last axis a r The distance to (N) is D wa (a w (N, a r (N))). D wa (a w (N, a r Using the formula (N)), the average speed of a train passing through a unit bridge girder v a is expressed as the following equation (4).

number

[0025] From equations (3) and (4), the following equation (5) holds.

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[0026] Next, we will explain the deflection that occurs in a unit girder when a load is applied to it. Figure 5 shows a schematic diagram of a unit girder. Figure 5 shows the situation when load P is applied to the bridge. Here, the distance between the position where load P is applied to the unit girder and the approach end is represented as a. Furthermore, the distance between the position where load P is applied to the unit girder and the exit end is represented as b. In this case, the bending moment at the position where load P is applied to the unit girder is expressed by the following equation (6).

number

[0027] Figure 6 shows the bending moment at each position of a unit girder due to load P. As shown in Figure 6, the bending moment generated in a unit girder due to load P is zero at the approaching end, and increases proportionally as it approaches the position where load P is applied from the approaching end, reaching the value shown by equation (6) at the position where load P is applied. In addition, the bending moment generated in a unit girder due to load P decreases proportionally as it approaches the exit end from the position where load P is applied, reaching zero at the exit end. Therefore, the bending moment at any position X in a unit girder is expressed by the following equation (7).

number

[0028] In equation (7), x indicates the distance from the entrance end to position X in the traveling direction of the railway train. Moreover, Ha in equation (7) is a value expressed by the following equation (8).

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[0029] The relationship between the deflection w of a unit girder at any position X and the bending moment is expressed by the following equation (9).

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[0030] In equation (9), θ is the angle between the horizontal line and the deflected unit girder at position X. The following equation (10) holds true from equations (7) and (9).

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[0031] By integrating both sides of equation (10) twice with respect to x, the following equation (11) is obtained, which represents the deflection w at the position X.

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[0032] g1 and g2 in equation (11) are constant terms. Here, the unit girder is supported at the entrance end and exit end, so there is no deflection at the entrance end and exit end. That is, in equation (11), x=0 and x=L B In this case, both sides are 0. Therefore, g1 and g2 are expressed as the following equations (12) and (13).

number

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[0033] From equations (11), (12), and (13), the following equation (14) expressing the deflection w at position X can be obtained.

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[0034] When load P is applied to the center of the unit girder in the longitudinal direction, the maximum deflection of the unit girder due to the application of load P occurs at the center of the unit girder in the longitudinal direction. This maximum deflection is called w 0.5l As, w 0.5l If the load P is applied to the center of the unit girder in the longitudinal direction, a = b = 0.5L. B In addition, the position X for which the deflection is to be calculated is the center of the unit girder in the longitudinal direction, so x = 0.5L B In this case, x<=a, so from equation (8), H a = 0. x = 0.5L B , a=b=0.5L B , H a By substituting =0 into equation (14), the deflection w 0.5l The following equation (15) is obtained.

number

[0035] w 0.5lis used to normalize the deflection at any position in the unit girder expressed by equation (14).

[0036] When the position of the load P is closer to the entry end than the position X, that is, when x>a, from the formula (8), H a =1, and equation (14) can be expressed as the following equation (16).

number

[0037] a=L B Let r be the real number between 0 and 1. b=L B -a, so b=L B (1-r). In equation (16), a = L B r, b=L B Substituting (1-r), w 0.5l Normalizing by dividing by x, we get the normalized deflection w at position X for x>a. std The following equation (17) is obtained.

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[0038] Similarly, when the position of the load P is on the retreat end side of the position X, that is, when x<=a, from equation (8), H a =0, and equation (14) can be expressed as the following equation (18).

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[0039] a=L B Let r be the real number between 0 and 1. b=L B -a, so b=L B (1-r). In equation (18), a = L B r, b=L B Substituting (1-r), w 0.5l Normalizing by dividing by x<=a gives the normalized deflection w at position X.std The following equation (19) is obtained.

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[0040] In equations (17) and (19), x is L x By substituting, the normalized deflection w at the deflection observation point std is expressed as a function of r as shown in the following equation (20).

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[0041] The function R(r) in equation (20) is the function shown in equation (21) below.

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[0042] Here, using equations (20) and (21), any one axle a w We will determine the function that shows the time change of the deflection caused at the observation point by the load applied to the bridge through the wheel (m, n). First, let t be the time it takes for the wheel of one axle of a railway train to move from the approach end to the observation point. xn Far away. xn L x and v a From this, it can be calculated using the following equation (22).

number

[0043] The time it takes for one wheel of a railway train to cross a unit bridge girder, i.e., from the approach end to the exit end, is defined as t ln Far away. ln L B and v a From this, it can be calculated using the following equation (23).

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[0044] Also, the n-axis a of the m-th railcar of the railcar train w The time when the wheel (m, n) reaches the entry end is t o Let (m, n). t o (m, n) is t i and v a and D wa (a w (m, n)), it can be calculated using the following equation (24).

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[0045] From equation (22), L x is expressed as the following equation (25).

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[0046] Also, from equation (23), L B is expressed as the following equation (26).

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[0047] Axle A w The position (m, n) is the load position. Therefore, axle a w The position of (m, n) is a=L from the entrance end to the exit end. B The position is the distance r. Also, if the variable indicating time is t, then a at time t is w The distance from the entrance of (m, n) is o is equal to the distance traveled by the railcar from (m, n) to time t. Therefore, the following equation (27) holds true.

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[0048] From equation (27), r is expressed as follows:

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[0049] Using equations (25), (26), and (28), L in equations (20) and (21) x , L B , r is replaced by axle a w The function w of the following equation (29) is used as a model showing the time change of the deflection caused at the observation point by the load applied to the unit girder through the wheel (m, n). std (a w (m, n), t) are obtained. The function R(t) in equation (29) is the function shown in equation (30) below.

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number

[0050] Observation information and environmental information (t i , t o , N, L B , L x , L C (1)~L C (N), a r (1)~a r (N), L a (a w (1, 1))~L a (a w (N, a r If (N)))) is known, then we can use this information to find w std (a w (m, n), t) is obtained. For example, t i , t o Using equation (1), t s t s ,N,a r , L a , L C From this, using equation (5), v a is obtained. v a and L B and L xFrom this, using equations (22) and (23), t xn , t ln is obtained. L a , L C , ti, using equations (3) and (24), t o (m, n) is found. Then, the calculated t xn , t ln , t o By substituting (m, n) into equations (29) and (30), we obtain the function w of t. std (a w (m, n), t) are found.

[0051] w std (a w An example of the change in the amount of deflection at the observation points indicated by (m, n), t) is shown in FIG. 7. The horizontal axis of the graph in FIG. 7 indicates time, and the vertical axis indicates the amount of deflection. m According to the movement of a r A pair of wheels on each of the (m) axles moves along the unit bridge girder. m A function C as a model showing the time change in the deflection caused at the observation point by the movement of std (m, t) is the w std (a w This is calculated as the sum of (m, n), t) using the following equation (31).

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[0052] a r When (m) is 4, i.e., railcar C m If it has a four-axis configuration, the function C std The change in the amount of deflection at the observation point indicated by (m, t) is shown in FIG. 8. The horizontal axis of the graph in FIG. 8 indicates time, and the vertical axis indicates the amount of deflection. The solid line graph in FIG. 8 indicates the amount of deflection. std (m, t), and each dotted line graph represents w std (a w (m, n), t) are shown.

[0053] In addition, N railcars move along the unit bridge girder in response to the movement of the railcar. Therefore, we use a function T std (t) is the C for each railcar std As a sum of (m, t), it is obtained as shown in the following equation (32).

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[0054] If N is 16, i.e., if a train has 16 railcars, then the function T std FIG. 9 shows the change in the amount of deflection at the observation point indicated by (t). The horizontal axis of the graph in FIG. 9 indicates time, and the vertical axis indicates the amount of deflection. The solid line in FIG. 9 indicates the time std (t), and each dotted line graph represents the C std (m, t) are shown. As shown in the graph in Fig. 9, the waveform is the sum of the deflections of each passing train, and it can be seen that vibrations occur with a period in which successive trains pass over the unit girder. This concludes the explanation of the model of deflection in a unit girder.

[0055] (1-3) Verification experiment: The inventors have determined that the deflection amount T std (t) was calculated. That is, N=4, t i =7.21[seconds], t o =8.777[seconds], t s = 1.567 [seconds], L B = 25 [m], L x = 12.5 [m], L C = 25[m], a r For each m=4, m=1~N, L a (a w (m, 1)) = 2.5 [m], m = 1 to N, L a (a w (m, 2)) = 2.5 [m], m = 1 to N, L a (a w(m, 3)) = 15 [m], m = 1 to N, L a (a w (m, 4)) = 2.5 [m].

[0056] The amount of deflection at this time T std (t) is shown in FIG. 10. The horizontal axis of the graph in FIG. 10 indicates time, and the vertical axis indicates the amount of deflection. std By performing a fast Fourier transform (FFT) on (t), T std The intensity of each frequency component in (t) was calculated. T std The results of FFT for (t) are shown in FIG. 11. The horizontal axis of the graph in FIG. 11 indicates frequency, and the vertical axis indicates the intensity of the corresponding frequency component. The inventors then calculated T std From the FFT result of (t), std The fundamental frequency F of (t) f Here, the fundamental frequency is the frequency of the lowest frequency component contained in the signal. std From the FFT result of (t), excluding the side lobes caused by the window function used in the FFT, the peak corresponding to the lowest frequency was identified, and the identified peak was determined as the fundamental frequency. In the example of FIG. 11, as shown in the area surrounded by the dashed line, two side lobe peaks caused by the window function used in the FFT are observed in the range of less than 2 Hz. Among the peaks excluding these peaks, the inventors identified the peak in the area surrounded by the dotted line as the lowest frequency peak, and determined the frequency corresponding to the identified peak as the fundamental frequency F. f The inventors obtained the fundamental frequency of 3.1 Hz from the graph in FIG. 11. The inventors obtained the passing period t s The fundamental frequency F f The wave number ν of was calculated using the following equation (33).

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[0057] In this case, ν=1.567×3.1=4.8577. Here, the number of rail cars N of the moving rail train is 4. The inventors calculate the fundamental frequency F f The inventors have found that the wave number ν of the fundamental frequency F f It was found that it can be calculated using the following equation (34), where ν is the wave number ν minus 1 rounded to an integer. The round function returns the rounded value of its argument. Furthermore, the method of rounding to an integer is not limited to rounding, and other methods such as rounding down or rounding up may be used depending on the characteristics of the unit bridge girder being observed.

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[0058] The number of railcars N may be obtained by various methods other than Equation (34). For example, std If high-pass filtering is performed on (t) to retain the vibration of the unit bridge girder caused by the movement of the railroad vehicle, the number of railroad vehicles N can be obtained from the number of peaks. The amount of deflection after high-pass filtering is T std_hp (t) and T std The graph in FIG. 12 shows the time on the horizontal axis and the amount of deflection on the vertical axis. The solid line in FIG. std_hp (k), and the dotted graph shows T std (t) is shown.

[0059] As shown in FIG. 12, the number of upwardly convex peaks (6) and the number of downwardly convex peaks (5) are different, so the number of railcars N can be obtained by increasing or decreasing a predetermined number for each peak. For example, a configuration can be adopted in which the number of upwardly convex peaks minus 2 is considered to be the number of railcars, 4, or a configuration can be adopted in which the number of downwardly convex peaks minus 1 is considered to be the number of railcars, 4. Of course, the number of railcars N can be obtained by various other methods, for example, the number of railcars N can be obtained by calculating the number of railcars N based on the passing period t sand the time required for each railcar to pass.

[0060] (1-4) Element details Here, the measurement device 1, the sensor device 2, and the server device 3 of the time acquisition system 10 will be described in detail with reference to FIG. 13. In this embodiment, the environmental information for each of the railway train 6 and the unit bridge girder is known. B , the distance L from the entry end of the unit bridge girder to the observation point x , the length L of each rail car of the rail train 6 c , the number of axles in each rail car of the rail train 6 a r , L of each rail car of rail train 6 a is known. Specifically, L B = 25 [m], L x = 12.5m, L c (1)~L c (N) = 25 [m], a r (1)~a r (N) each = 4, L a (a w (1, 1))~L a (a w (N, 1)) = 2.5 [m], L a (a w (1, 2))~L a (a w (N, 2)) = 2.5 [m], L a (a w (1, 3))~L a (a w (N, 3)) respectively = 15 [m], L a (a w (1, 4))~L a (a w (N, 4)) = 2.5 [m] respectively.

[0061] In the present embodiment, the time acquisition system 10 acquires the observation information (the number N of railcars in the railcar train 6, the approach time t i , the exit time t when the train 6 exits the unit bridge girder o, the period during which the train 6 passes through the unit bridge girder is t s ) is derived based on data measured by the measurement device 1. The number N of railcars in the railcar train 6 is 16, but observations are performed assuming that the number N is unknown.

[0062] The measuring device 1 measures the deflection at the observation point via the sensor device 2. In this embodiment, the measuring device 1 is installed on the bridge abutment 8b, but may be installed at another location. The measuring device 1 includes a control unit 100, a storage unit 110, and a communication unit 120. The control unit 100 includes a processor such as a central processing unit (CPU), a read only memory (ROM), a random access memory (RAM), etc. The control unit 100 realizes each function of the measuring device 1 by expanding various programs recorded in the ROM, etc., into the RAM and executing them via the CPU. The storage unit 110 stores various programs, data on measured deflections, etc. The communication unit 120 includes a circuit used for wired or wireless communication with an external device.

[0063] The sensor device 2 detects acceleration as a predetermined physical quantity at an observation point. The sensor device 2 includes a control unit 200, an acceleration sensor 210, a storage unit 220, and a communication unit 230. The control unit 200 includes a processor such as a CPU, a ROM, a RAM, etc. The control unit 200 realizes each function of the sensor device 2 by expanding various programs recorded in the ROM, etc., into the RAM and executing them via the CPU.

[0064] The acceleration sensor 210 is an acceleration sensor such as a quartz acceleration sensor or a MEMS acceleration sensor capable of detecting acceleration occurring in each of three mutually perpendicular axial directions. In this embodiment, the acceleration sensor 210 is arranged so that one axis is parallel to the vertical direction in order to detect acceleration in the vertical direction with higher accuracy. However, there are cases where the installation location of the sensor device 2 on the upper structure 7 is tilted. Even if one of the three detection axes of the acceleration sensor 210 is not aligned with the vertical direction, the measurement device 1 detects acceleration in the vertical direction by combining the acceleration of the three axes.

[0065] The control unit 200 of the sensor device 2 periodically detects the vertical acceleration at the observation point on the bridge 5 via the acceleration sensor 210, and transmits the detected acceleration data to the measurement device 1. The control unit 100 of the measurement device 1 measures the vertical deflection of the bridge 5 at the observation point at the detection time of the acceleration based on the acceleration data transmitted from the sensor device 2. In this embodiment, the control unit 100 obtains the vertical deflection of the bridge 5 at the observation point by integrating the acceleration indicated by the data transmitted from the sensor device 2 twice with respect to time. Then, the control unit 100 transmits the measured deflection data to the server device 3. In this embodiment, the sensor device 2 detects acceleration at a predetermined period ΔT. Therefore, the measurement device 1 measures time series data of the deflection at a period of ΔT (data indicating the change in displacement of the structure over time).

[0066] The server device 3 derives the number of rail cars included in the rail train 6 based on the deflection at the observation point measured by the measurement device 1. The server device 3 includes a control unit 300, a memory unit 310, and a communication unit 320. The control unit 300 includes a processor such as a CPU, a ROM, a RAM, etc. The control unit 300 implements the functions of a data acquisition unit 301, a removal unit 302, a time acquisition unit 303, and a number acquisition unit 304 by expanding various programs recorded in the ROM, etc., into the RAM and executing them via the CPU. The memory unit 310 stores various programs, data on the detected deflection, etc. The communication unit 320 includes a circuit used for wired or wireless communication with an external device.

[0067] The data acquisition unit 301 is a function that acquires time series data of deflection occurring at the observation point as a response to the movement of the railway train 6 over each unit bridge girder of the bridge 5. The control unit 300 acquires time series data of deflection occurring at the observation point from the measurement device 1 using the function of the data acquisition unit 301. Hereinafter, the time series data of deflection acquired by the function of the data acquisition unit 301 is referred to as u(k). Here, k is a variable indicating which observation is the number when the amount of deflection is periodically observed at the observation point. FIG. 14 shows an example of u(k). The horizontal axis of the graph in FIG. 14 indicates time, and the vertical axis indicates the amount of deflection. Here, time t=kΔT. That is, since the detection of acceleration is performed at ΔT of time resolution, the time series data of deflection is data for discrete time. Therefore, below, the time series data u(k) is expressed as a function of the variable k corresponding to time.

[0068] The removal unit 302 has a function of removing vibration components contained in the time series data u(k). The control unit 300 uses the function of the removal unit 302 to perform low-pass filtering on the time series data u(k) to remove the vibration components. The low-pass filtering may be performed in various ways, and in this embodiment, the low-pass filtering is performed by calculating a moving average of the time series data u(k). In this embodiment, in order to determine the moving average interval, the fundamental frequency F f is used.

[0069] Therefore, the control unit 300 performs an FFT on u(k) using the function of the removal unit 302. The results of performing an FFT on u(k) shown in FIG. 14 are shown in FIG. 15. The horizontal axis of the graph in FIG. 15 indicates frequency, and the vertical axis indicates the intensity of the corresponding frequency component. Then, the control unit 300 detects peaks from the FFT results. Of the detected peaks, the control unit 300 identifies a peak corresponding to the minimum frequency excluding side lobe peaks caused by the influence of the window function used in the FFT. The control unit 300 calculates the frequency corresponding to the identified peak as the fundamental frequency F of u(k). fIn the example of FIG. 15, the derived fundamental frequency F f is 3.01Hz.

[0070] Then, the control unit 300 calculates the fundamental frequency F for u(k) as follows: f First, the control unit 300 performs low-pass filtering to attenuate the fundamental frequency F f Based on this, F is calculated based on the following formula (35): f By deriving the inverse of f Derive.

number

[0071] Furthermore, the control unit 300 determines the derived T f Based on the predetermined period ΔT, the interval k is calculated using equation (36). mf Derive.

number

[0072] Furthermore, the control unit 300 calculates the interval k for each value of u(k). mf The moving average of u(k) is obtained based on equation (37) and a low-pass filter is applied to u(k). Note that this low-pass filter has a fundamental frequency F f An FIR filter or the like that attenuates the above components may also be used.

number

[0073] The time acquisition unit 303 acquires the time series data u after the vibration component has been removed. lp (k) is a function to acquire the entry time when the train moving body enters the structure and the exit time when the train moving body exits the structure. In other words, when the low-pass filter processing is performed as described above, the control unit 300 acquires, by the function of the time acquisition unit 303, u lp From (t), the predetermined threshold value C L Identify two consecutive data that sandwich u lp Two consecutive data of (t) are C L By sandwiching, I mean u lp The range between the values ​​of two consecutively measured displacement data included in (t), that is, the range between the smaller value of these displacement data and the larger value, L Indicates that the value is included.

[0074] This threshold C L is the value of the deflection that occurs in the unit girder when a train approaches the unit girder. For example, it is the value of the deflection at the observation point of the unit girder when a train is positioned so that the wheels of the front axle of the train are located near the approach end. In addition, this threshold C L may be other values ​​as long as it is possible to detect the entry of a train into a unit girder. For example, it may be the deflection of the observation point of the unit girder when a predetermined weight is applied near the entry end. In addition, the threshold value C L may be a predetermined percentage (e.g., 10%, 1%, etc.) of the maximum deflection amount at the observation point of the unit bridge girder when a train passes through the unit bridge girder. L u lp It may be set to the value of any of the data included in (t).

[0075] In FIG. 16, u lp (t) and the threshold value C L are shown. The horizontal axis of the graph in FIG. 16 indicates time (t = kΔT), and the vertical axis indicates the amount of deflection. The solid line graph in FIG. 16 shows u lp (t), and the dotted line graph shows u(t). In the portion surrounded by the dotted circle in FIG. 16, u lp (k) and the threshold value C L intersect. Also, in FIG. 17, an enlarged view of the intersection point of u lp (t) and C L (the left dotted circle portion in the graph of FIG. 16) is shown. The horizontal axis of the graph in FIG. 17 indicates time, and the vertical axis indicates the amount of deflection. Each black dot in FIG. 17 shows the discrete value data included in u lp (t). In the example of FIG. 17, it is shown that the data k - 1 and the data k included in u lp (t) sandwich the threshold value C L .

[0076] The control unit 300 specifies the later of the two times corresponding to two consecutive data sandwiching the specified C L . In the example of FIG. 17, the control unit 300 specifies the time kΔT corresponding to the data k. In the example of FIG. 16, for the data in the right dotted circle portion in FIG. 16 as well, the control unit 300 specifies two consecutive data sandwiching C L , and specifies the later of the two times corresponding to the specified two data. That is, the control unit 300 specifies the time kΔT corresponding to the data k on each of the entry time side and the exit time side. As a result, the data k and the time kΔT are acquired on each of the entry time side and the exit time side.

[0077] The control unit 300 acquires the earlier (smaller) value of the obtained k as the data k i corresponding to the entry time t i of the railway train 6 to the unit bridge girder. Also, the control unit 300 acquires the later (larger) value of the obtained k value as the data k o corresponding to the exit time t o of the railway train 6 from the unit bridge girder. Also, the control unit 300 calculates the passing period ts k corresponds to s k s =k o -k i In addition, t s =k s ΔT,t o =k o ΔT,t i =k i ΔT.

[0078] In the example of FIG. 16, the control unit 300 i Multiplying by ΔT gives the approach time t i =7.115[s], and data k o By multiplying by ΔT, the exit time t o = 12.805 [s]. Also, the variable k s Multiplying by ΔT gives the transit period t s = 5.69 [s]. As described above, the control unit 300 obtains u lp By using (k) to obtain the approach time, exit time, and passage period, the influence of vibration components higher than the fundamental frequency can be reduced, and the approach time, exit time, and passage period can be obtained more accurately.

[0079] In this manner, in this embodiment, the control unit 300 lp C included in (t) L The later of the two times corresponding to the two consecutive data points that sandwich t is the entry time t i , approach time t o However, the control unit 300 acquires other times as the entry time t i , approach time t o For example, the control unit 300 may acquire the lp From (t), the predetermined threshold value C L Two consecutive data pieces that sandwich the entry time t are identified, and a time that is included in the period after one of the times corresponding to the two identified data pieces and before the other time is defined as the entry time t i and approach time t oIn the example of FIG. 17, the control unit 300 acquires the time after the time (k-1)ΔT corresponding to the data k-1 and before the time kΔT corresponding to the data k (for example, the time (k-1)ΔT, u lp (t) and C L The time corresponding to the intersection point of t i The control unit 300 may also acquire the information as u lp A curve is obtained by interpolating each data point in (t), and the obtained curve and C L The time corresponding to the intersection with i , t o It may be calculated as:

[0080] Also, u lp C in (t) L For two consecutive data that sandwich the L For example, in the example of FIG. 17, the value of data k is C L In this case, the control unit 300 sets C L A pair of data equal to and the previous data, and C L The data set that is equal to the data set and the data set that is one step behind are selected as C L In the example of FIG. 17, data k may be selected as two consecutive data that sandwich C L If it is equal to, the control unit 300 L As two consecutive data pieces sandwiching a data k-1, a data k pair and a data k pair and a data k+1 pair are selected. The control unit 300 defines a time within a period between two times corresponding to two data pieces included in the selected pair as t i Or t o It may be obtained as.

[0081] In this embodiment, the control unit 300 lp The time associated with any of the data included in (t) is referred to as the entry time t i , exit time t o As a result, the control unit 300 acquires the entry time t i , exit time t ou corresponding to each measurement time of the ΔT interval including lp The data of (t) is lp By referring to (t), the control unit 300 can easily obtain and utilize the information. lp The time that is not associated with any of the data included in (t) is called the entry time t i , exit time t o If you get it as t i , t o u corresponding to each measurement time of the ΔT interval including lp (t) data into the original u lp The control unit 300 obtains u(t) by attenuating vibration components at or above the fundamental frequency. lp By obtaining the approach and exit times using (t), the influence of vibration components higher than the fundamental frequency can be reduced, and the approach and exit times can be obtained more accurately.

[0082] In u(k) shown in Fig. 14, high-frequency vibrations mainly correspond to the deflection caused in the unit girder by the weight of the railway vehicle, but more complex vibrations may be superimposed depending on the specifications of the unit girder and the railway vehicle. An example of such vibration is resonance. In other words, if the vibrations caused in the unit girder by the passage of a railway train approximate the natural frequency of the unit girder, the natural frequency or a harmonic vibration of the natural frequency may be excited in the unit girder by the passage of the railway train.

[0083] In FIG. 18, the time series data u(k) when resonance occurs is indicated by a dotted line. In FIG. 18, the horizontal axis is time, the vertical axis is the amount of deflection, and the time series data u(k) is indicated by a dotted line. Even when complex vibrations are superimposed in this way, the control unit 300 can accurately obtain the entry time and exit time by removing the vibrations through low-pass filter processing using the function of the removal unit 302. For example, the control unit 300 can obtain the time series data u(k) after low-pass filter processing by processing equations (35) to (37). lp In FIG. 18, the time series data ulp (k) is shown by a solid line. Note that the natural frequency of a unit bridge girder and the frequency of the deflection caused in the unit bridge girder by the weight of the railway vehicle may differ. Therefore, when the frequencies of both vibrations are close, for example, in equations (35) to (37), the moving average may be obtained for the vibration with the larger amplitude. Of course, low-pass filter processing may be performed in two steps to remove each vibration.

[0084] The number acquisition unit 304 acquires the time series data u(k) acquired by the function of the data acquisition unit 301 and the entry time t acquired by the function of the time acquisition unit 303. i and exit time t o The control unit 300 has a function of acquiring the number of rail cars included in the rail train 6 based on the above. i and o Based on this, the passing period t s Then, the control unit 300 derives the derived t s and the fundamental frequency F derived based on u(k) f Based on this, we use equation (33) to calculate the passing period t s The fundamental frequency F f The control unit 300 derives the number of rail cars N included in the rail train 6 based on the derived v, using equation (34). s =5.69[s], F f = 3.01 [Hz], the control unit 300 obtains the number N of railway cars as round((5.69) × 3.01 - 1) = round(16.13) = 16.

[0085] (2) Derivation process: Next, the process of deriving the number of rail cars of the rail train 6 executed by the server device 3 will be described with reference to Fig. 19. The server device 3 starts the process of Fig. 19 in response to transmission of displacement data at the observation point from the measurement device 1, but may start the process of Fig. 19 at any timing, such as a specified timing. In S100, the control unit 300 acquires time series data u(k) of the deflection occurring at the observation point from the measurement device 1 by the function of the data acquisition unit 301. S100 is an example of a data acquisition step.

[0086] In S105, the control unit 300 calculates the bridge length L B , the length L of each rail car of the rail train 6 C , and information on the distance La indicating the position of each railcar of the railcar train 6 are acquired as environmental information. Such environmental information may be stored in the storage unit 310 in advance, or may be input by a user or the like.

[0087] In S110, the control unit 300 removes the vibration component by the function of the removal unit 302. That is, the control unit 300 executes an FFT on u(k) acquired in S100. The control unit 300 detects peaks from the FFT result. Of the detected peaks, the control unit 300 identifies the peak corresponding to the lowest frequency excluding side lobe peaks caused by the influence of the window function used in the FFT. The control unit 300 calculates the frequency corresponding to the identified peak as the fundamental frequency F of the time series data u(k). f Furthermore, the control unit 300 performs low-pass filtering on the time series data u(k) using equations (35) to (37) to obtain the time series data u(k) from which the vibration component has been removed. lp (k) is obtained. S110 is an example of a removing step.

[0088] In S115, the control unit 300 acquires the entry time and the exit time by the function of the time acquisition unit 303. That is, the control unit 300 acquires the entry time and the exit time derived in S110. lp (k) and the predetermined threshold value C L and compare it with the threshold C LThe two data between them are lp The process of identifying the point in time from (k) and obtaining data k from one of the points is carried out on both the entry time side and the exit time side to obtain two values ​​of k.

[0089] Then, the control unit 300 sets the smaller value of k to the entry time t i The data k corresponding to i The control unit 300 also calculates the larger of the calculated k values ​​as the exit time t o The data k corresponding to o Then, the control unit 300 acquires t o =k o ΔT,t i =k i From the relationship of ΔT, the approach time t i , exit time t o S115 is an example of a time acquisition step.

[0090] In S120, the control unit 300, by using the function of the number acquisition unit 304, acquires the time series data u(k) acquired in S100 and the entry time t i and exit time t o That is, the control unit 300 obtains the number of rail cars included in the rail train 6 based on t i and o Based on this, the transit period t s Then, the control unit 300 derives the passing period t s The fundamental frequency F f The control unit 300 derives the number N of railcars included in the railcar train 6 based on the derived v, using equation (34).

[0091] (3-1) Second embodiment: As in the above embodiment, the deflection amount T stdIf we consider that the time series data u(k) of the observation data of (t) follows the deflection model, the number of railcars N can be obtained. i , exit time t o Therefore, these approach times t i , exit time t o The accuracy of has a large impact on the accuracy of the number of railway cars, N.

[0092] From the time series data u(k), the entry time t i , exit time t o The configuration for obtaining with high accuracy is, as in the above embodiment, the time series data u(k) and the threshold C L The present invention is not limited to a configuration in which the above-mentioned comparison is performed. Fig. 20 is a diagram showing a measurement example of time-series data u(k). The horizontal axis of Fig. 20 is time, the vertical axis is the amount of deflection, and the measurement example of the time-series data u(k) is indicated by a dotted line. This example is a measurement example in the case where drift noise is present in the sensor device 2. In other words, the detected value of acceleration or the like may shift over time in a specific direction (the negative direction of the amount of deflection in Fig. 20) due to a certain tendency of drift.

[0093] When such drift noise exists, the time series data u after low-pass filtering as shown in the above equation (37) lp (k) is the data shown by the solid line in FIG. 20. Therefore, the time series data u lp (k) is compared with a certain threshold to determine the entry time t i , exit time t o Therefore, when the drift noise exists, it is difficult to obtain the approach time t i , exit time t o It is conceivable to generate time series data from which the influence of drift noise has been removed so that the above-mentioned can be obtained.

[0094] A configuration for performing such processing can be realized by changing the processing of the time acquisition unit 303 in the configuration shown in FIG. 13 described above. That is, the control unit 300, by the function of the time acquisition unit 303, performs a high-pass filter process on the time series data on which the low-pass filter process has been performed, inverts the sign, and compares the sum of the time series data before the sign is inverted and the time series data after the sign is inverted with a predetermined threshold value, identifies two time series data sandwiching the threshold value, and performs the process of obtaining data k from one of them on each of the entry time side and the exit time side to obtain two values of k. Then, the control unit 300 obtains the entry time and the exit time from these values of k. Note that the low-pass filter process by the removal unit 302 is the same as that in the above-described embodiment.

[0095] Specifically, the control unit 300 performs a high-pass filter process on the time series data u lp (k) after the low-pass filter process obtained by Expression (37). Hereinafter, the time series data after the high-pass filter process is denoted as u hp (k). FIG. 21 shows the time series data u lp (k) obtained by performing a high-pass filter process on the time series data u hp (k) shown by the solid line in FIG. 20.

[0096] The time series data from which the vibration component has been removed has a substantially downwardly convex step function-like shape, like the time series data u lp (k) in FIG. 16. The characteristics of the time series data from which the vibration component has been removed should also appear in the time series data u hp (k) after the high-pass filter process shown in FIG. 21. Therefore, the control unit 300 divides the time space into three sections and corrects the shape of the time series data u hp (k) in each section to generate data like the time series data u lp (k) in FIG. 16.

[0097] In addition, the high-pass filter processing may be performed so long as the characteristics for generating a downward convex step function remain. Such high-pass filter processing may be realized by various methods. For example, the time series data u(k)-time series data u lp (k) may perform high-pass filtering. Note that the time series data u lp When (k) is used, the moving average may be calculated with a period that is a natural number multiple of the fundamental period. Also, high-pass filtering may be performed using an FIR filter or the like with a cutoff lower than the fundamental frequency.

[0098] In FIG. 21, the section divided into three sections is called the first section Z 1 , second section Z 2 , 3rd section Z 3 That is, the first section Z 1 is the time series data u hp Among the upwardly convex peaks in (k), the first peak P 1 appears at time t 1 (variable k 1 The third section Z 3 is the time series data u hp Among the upwardly convex peaks in (k), the second peak P 3 appears at time t 3 (variable k 3 The second section Z 2 is the time series data u hp (k) The first upward convex peak P 1 , the second peak P 3 That is, the interval between time t 1 (variable k 1 (corresponding to ) at time t 3 (variable k 3 In FIG. 21, the first peak P 1 The coordinates of are (k 1 ,u 1 ), the second peak P 3 The coordinates of are (k 3 ,u 3).

[0099] Here, the time series data u hp The correction curve for correcting (k) is Mcc(k), and the curve for expressing the correction curve for each section is Mcc 1 (k),Mcc 2 (k),Mcc 3 (k). In other words, the correction curve Mcc(k) is 1 (k)+Mcc 2 (k)+Mcc 3 (k) and is expressed by the following equation (38).

number

[0100] The control unit 300 controls the first section Z 1 and the third section Z 3 In the time series data u hp By inverting the sign of (k), the correction curve Mcc of the first section 1 (k) Correction curve Mcc for the third section 3 (k) is obtained. That is, the correction curve Mcc 1 (k),Mcc 3 (k) are expressed by equations (39) and (40), respectively.

number

number

[0101] Figure 22 shows the correction curve Mcc generated by inverting the sign 1 (k),Mcc 3 (k) in the first section Z 1 and the third section Z 3 The correction curve Mcc in the second section is shown by a solid line. 2 (k) is the correction curve Mcc 1 (k),Mcc 3 As shown in FIG. 22, the first section Z1 and the second section Z 2 near the boundary with the correction curve Mcc 1 (k) changes linearly. Also, in the third section Z 3 and the second section Z 2 near the boundary with the correction curve Mcc 3 (k) changes linearly.

[0102] Therefore, the control unit 300 is in the vicinity of the boundary between the first section Z 1 and the second section Z 2 and in the vicinity of the boundary between the third section Z 3 and the second section Z 2 and regards the correction curve Mcc 2 of the second section Z 2 (k) as also being a straight line. In the present embodiment, the control unit 300 determines that when the variable k is within a predetermined value k a to the value k 1 it obtains an approximation curve for the correction curve Mcc 1 (k), and extends the approximation curve up to the range of k > k 1 to regard it as the correction curve Mcc 1 in the vicinity of the value k 2 (k) in the second section.

[0103] For this reason, the control unit 300 multiplies the value of the correction curve Mcc 1 at the value k 1 (k), that is, the value obtained by inverting the sign of the amplitude of the first peak, -u 1 by a predetermined first coefficient c Th to obtain the value -u 1 ·c Th and determines the value of the variable k for which the correction curve Mcc 1 (k) is closest to the value -u 1 ·c Th to be k a . In FIG. 22, k 1 , k a , -u 1 , -u 1 ·c Th are shown. Note that the predetermined coefficient c Th is a value determined in advance within the range of 0 < c Th < 1.

[0104] The control unit 300 is a ~k 1 In the range of the correction curve Mcc 1 The first straight line, which is an approximation straight line of (k), is acquired. The straight line may be specified by various methods, and in this embodiment, the control unit 300 specifies the straight line by the least squares method. That is, the control unit 300 specifies L 1 (k)=s 1 k+i 1 Assuming that, the error e shown in Eq. (41) k By minimizing the coefficient s 1 and the constant term i 1 In FIG. 22, a straight line L 1 (k) is indicated by the dashed line.

number

[0105] In addition, the value k a ~k 1 The number of samples up to is n, and the coefficient s 1 is expressed as follows: 1 is expressed by the following equation (44).

number

number

number

[0106] Third Section Z 3 and the second section Z 2 The same process is performed near the boundary between the variable k and the value k. 3 From the default value k b Correction curve Mcc between 3 Obtain a fitted curve for (k), and k <k 3By extending the approximate curve up to the range, the value k in the second interval 3 Near the correction curve Mcc 2 (k) is regarded as

[0107] Therefore, the control unit 300 uses the value k 3 At the correction curve Mcc 3 (k), that is, the value -u obtained by inverting the sign of the amplitude of the third peak 3 The predetermined third coefficient c Th Is the value obtained by multiplying -u 3 ·c Th To obtain, and the correction curve Mcc 3 (k) is the value -u 3 ·c Th The value of the variable k closest to is set as k b In FIG. 22, k 3 , k b , -u 3 , -u 3 ·c Th Is shown. Here too, the predetermined coefficient c Th Is a value predetermined in the range of 0 < c Th < 1.

[0108] The control unit 300 is in the range of k 3 ~k b Obtains the third straight line which is the approximate straight line of the correction curve Mcc 3 (k). The straight line may be specified by various methods. In the present embodiment, the control unit 300 specifies the straight line by the least squares method. That is, the control unit 300 assumes the equation of the straight line as L 3 (k) = s 3 k + i 3 And obtains the coefficient s k And the constant term i 3 By minimizing the error e 3 Shown in Equation (45). In FIG. 22, the straight line L 3 (k) is indicated by a broken line.

Equation

[0109] In addition, by using a known calculation method, the value k a ~k 3 The number of samples up to is n, and the coefficient s 3 is expressed as follows: 3 is expressed by the following equation (48).

number

number

number

[0110] Next, the control unit 300 calculates the line L 1 (k), L 3 Line L between (k) 2 (k). The interpolation may be performed by various methods. In this embodiment, the control unit 300 performs the interpolation using the time series data u hp The first peak P of (k) 1 , the second peak P 3 Using the line Lm(k) that connects 2 FIG. 23 is a diagram showing a straight line Lm(k). The straight line Lm(k) is obtained by 1 , the second peak P 3 Since the line connecting the first peak P 1 , the second peak P 3 The coordinates of (k 1 ,u 1 ),(k 3 ,u 3 ) to obtain the straight line Lm(k) using the following equation (49).

number

[0111] The control unit 300 estimates that the shape of the straight line Lm(k) is similar to the shape of the base of a downwardly convex step function, and applies a predetermined second coefficient C 2By multiplying by, the straight line L which is the second straight line 2 (k) is generated. That is, the straight line L 2 (k) is obtained by the formula (50).

Number

[0112] As described above, when the straight lines L 1 (k), L 2 (k), L 3 (k) are generated, the control unit 300 obtains the intersection points (or the points closest to the intersection points) of these straight lines. That is, the control unit 300 is the first intersection point p which is the intersection point of the straight lines L 1 (k), L 2 (k), the first intersection point p 4 (k 4 , u 4 ) and the second intersection point p which is the intersection point of the straight lines L 2 (k), L 3 (k), the second intersection point p 5 (k 5 , u 5 ) are obtained. FIG. 24 is a diagram showing the relationship between these straight lines and the intersection points. These intersection points can be expressed as in the following formulas (51) and (52) based on the formulas (41) to (50).

Number

Number

[0113] When the intersection points are obtained, the control unit 300 is the correction curve Mcc2 (k) is defined as the following equation (53). 2 FIG. 13 is a diagram showing an example of (k) in solid lines.

number

[0114] The correction curve Mcc(k) is the correction curve Mcc(k) obtained by the above processing. 1 (k),Mcc 2 (k),Mcc 3 This is expressed by the following equation (54) using (k).

number

[0115] When the correction curve Mcc(k) is obtained, the control unit 300 converts the time series data after the high-pass filter process into u hp By adding the correction curve Mcc(k) to (k), time series data U(k) having a shape like a downward convex step function is obtained. The time series data U(k) is expressed as the following equation (55).

number

[0116] Therefore, the control unit 300 uses the function of the time acquisition unit 303 to obtain the time series data U(k) and a predetermined threshold value C L Compare with the threshold C LThe control unit 300 then uses the function of the time acquisition unit 303 to determine the smaller of the two k's obtained as the entry time t i The data k corresponding to i The control unit 300 also uses the larger of the k's obtained as the exit time t o The data k corresponding to o Obtain as.

[0117] In addition, the control unit 300 determines the passing period t s k corresponds to s k s =k o -k i Here too, t s =k s ΔT,t o =k o ΔT,t i =k i ΔT. Threshold C L It is sufficient that the threshold value C is set so that it is possible to identify whether the deflection of the unit girder is the deflection at the time of approach or the time of exit. For example, L can be defined by a value such as a predetermined percentage (e.g., 10%, 1%, etc.) of the maximum deflection amount at the observation point of the unit bridge girder when the railway train passes through the unit bridge girder. According to the above processing, even if drift noise occurs, the control unit 300 can determine the approach time t i and exit time t o can be obtained.

[0118] Approach time t i and exit time t o When is acquired, the control unit 300 i and o Based on this, the passing period t s Then, the control unit 300 derives the derived t s and the fundamental frequency F derived based on u(k) fBased on this, we use equation (33) to calculate the passing period t s The fundamental frequency F f The control unit 300 derives the number N of railcars included in the railcar train 6 based on the derived v, using equation (34). When the method of the fourth embodiment is applied to the same time-series data u(k) as in the first embodiment, t i = 7.09 [s], t o =12.865[s], t s =5.775[s], F f = 3.01 [Hz], the control unit 300 obtains the number N of railway cars as round((5.775) × 3.01 - 1) = round(16.38) = 16.

[0119] In this embodiment, in order to calculate the amount of deflection or evaluate the displacement by approximation, the time series data U(k) from which drift noise and vibration components have been removed is considered to be useful. 2 However, the time series data U(k) was obtained including the approach time t i and exit time t o If it is sufficient to obtain the first interval Z 1 and the third section Z 3 The time series data U(k) in the second interval Z 2 The time series data U(k) in may not be acquired.

[0120] That is, the control unit 300 calculates the correction curve Mcc for the first section by the processing of the formulas (37) to (40). 1 (k) and the correction curve Mcc for the third section 3 Furthermore, the control unit 300 obtains the correction curve Mcc 1 Based on (k), the approximation curve is drawn as the first straight line L by using equations (41) to (44). 1 The control unit 300 also acquires the correction curve Mcc for the third section. 3 Based on (k), the approximation curve is drawn as the third straight line L by using equations (45) to (48). 3 Obtained as (k).

[0121] Then, the control unit 300 calculates the time series data u hp (k) and the first line L 1 (k) and U 1 In addition, the control unit 300 obtains the time series data u hp (k) and the third straight line L 3 (k) and U 3 Obtain (k).

number

number

[0122] Then, the control unit 300 uses the function of the time acquisition unit 303 to acquire the time series data U 1 (k) and the predetermined threshold value C L Compare with the threshold C L The two data between them are U 1 The k acquired here is the time t i The data k corresponding to i In addition, the control unit 300 acquires the time series data U 3 (k) and the predetermined threshold value C L Compare with the threshold C L The two data between them are U 3 The k acquired here is the time t o The data k corresponding to o Of course, the method of specifying k can be various methods, as in the above embodiment. s k corresponds to s k s =k o -k i In addition, the control unit 300 acquires the value ts =k s ΔT,t o =k o ΔT,t i =k i ΔT is the transit time t s and exit time t o , approach time t i Get the.

[0123] In the first and second embodiments, the default threshold value may vary depending on the unit bridge girder and the environment (weather, etc.). Therefore, the default threshold value may be determined in advance for each unit bridge girder and environment. i and exit time t o In order to determine the entry time t i and exit time t o The threshold value can be determined by setting the value at which the value is most appropriate.

[0124] Approach time t i and exit time t o Whether or not is appropriate may be determined by various methods. For example, a configuration can be adopted in which multiple thresholds are provisionally set by the control unit 300, observation information is acquired based on the provisionally set thresholds, the time difference at a predetermined judgment level between the amount of deflection for each threshold acquired based on the observation information, environmental information, and an approximation equation for the deflection and the time series data is acquired, and a default threshold is determined based on the correlation between the acquired time difference and the multiple provisionally set thresholds.

[0125] The predetermined threshold value is u in equation (37) in the first embodiment. lp (k) in the second embodiment, or U(k) in equation (55) in the second embodiment. 1L ,C 2L ,C 3L In the following, we will mainly compare with U(k), but of course, u lp (k) may be compared with multiple thresholds C 1L ,C 2L,C 3L When the thresholds are provisionally set, the control unit 300 calculates a plurality of sets of entry times t i and exit time t o Also, the approach time t i and exit time t o Once obtained, the transit period t s The control unit 300 performs FFT processing on the time series data u(k) to obtain the fundamental frequency F t The control unit 300 can obtain the number N of railcars based on equations (33) and (34).

[0126] In this way, multiple thresholds C 1L ,C 2L ,C 3L Once the threshold is provisionally set, the entry time t i , exit time t o Then, a set of N railway cars (a set of observation information) is obtained. The control unit 300 associates each set of observation information with each threshold value. Furthermore, the control unit 300 calculates the bridge length L B and the vehicle length L as the length of the moving object C and L, which indicates the position of the railcar axles. a Then, the control unit 300 performs a calculation based on each set of observation information and the environmental information to obtain the formula (32) and obtains the time change T std The control unit 300 associates each of the time changes in the amount of deflection with each threshold value. std If (k) matches the time series data U(k), it can be said that the threshold is appropriate. Therefore, the control unit 300 calculates the time change T std The difference between (k) and the time series data U(k) is evaluated.

[0127] The time change in the amount of deflection T std(k) and the time series data U(k) generally have different scales, so it is preferable to expand or contract either one to match their scales. For example, for the time change T of the deflection amount std (k), multiplying by the coefficient c 1 and adding the constant term c 0 , if the linear function obtained is considered to be approximately equal to U(k), the scales of the two can be made to match. Note that the coefficient c 1 and the constant term c 0 can be calculated by the least squares method or the like. As a result of the above, U(k) and T std (k)·c 1 + c 0 are adjusted to match most closely. Here, the time change of the deflection amount after adjustment is denoted as T EOstd (k).

[0128] Note that this adjustment is determined based on the values within the interval from the entry time t i to the exit time t o . Therefore, the time change T of the deflection amount at times before the entry time t i and after the exit time t o can be regarded as other functions. For example, at times before the entry time t std (k) and after the exit time t i , the constant term c o can be considered to be 0. In this case, the constant term c 0 may change according to the interval or conditions so that the deflection amount is continuous at the entry time t i and the exit time t o . 0

[0129] In any case, according to the above processing, it is expected that the maximum and minimum values of both the adjusted time change T of the deflection amount EOstd (k) and the time series data U(k) are approximately the same, and the two are in a state of almost overlapping. The control unit 300 associates each of the adjusted time changes of the deflection amount with each threshold value. Then, based on each of the temporarily set threshold values, the control unit 300 determines the adjusted time change T of the deflection amount EOstdThe time difference between (k) and the time series data U(k) is compared, and the threshold value at which the time difference is minimized, i.e., the two match most closely, is obtained.

[0130] FIG. 28 shows the time change in the amount of deflection after adjustment T EOstd 28 is a partially enlarged diagram showing the time series data U(k) and U(k) by dotted lines and solid lines, respectively. EOstd From (k), the time change T of the deflection amount after adjustment corresponding to one threshold value is EOstd As shown in FIG. 28, the time change in the amount of deflection after adjustment T EOstd (k) Time change in the deflection amount after adjustment in the range from the maximum value (deflection amount = 0) to the minimum value (deflection amount = about -1.7) EOstd A time difference dT may occur between the time series data U(k) and the time series data U(k). The control unit 300 obtains the time difference dT at a predetermined determination level Lv. That is, the control unit 300 obtains the time change T EOstd The time when the time series data U(k) and the time series data U(k) reach a predetermined judgment level Lv is obtained, and the difference between the two times is obtained as the time difference dT. Note that the judgment level Lv is calculated based on the time change T EOstd The time difference T(k) is a value that is determined in advance as a level at which a time difference may occur between the time series data U(k) and the time series data U(k), and is a level determined within a range in which the amount of deflection is smaller than 0 and smaller than the minimum value. That is, the judgment level Lv is set to a value in advance so that the amount of deflection changes from a state in which the amount of deflection is equal to or greater than the judgment level to a state in which the amount of deflection is equal to or less than the judgment level, and also changes from a state in which the amount of deflection is equal to or less than the judgment level to a state in which the amount of deflection is equal to or greater than the judgment level. EOstd Since the deflection amount (k) and the time series data U(k) are discrete data on the time axis, the deflection amount and the time series data U(k) are not necessarily the same as the judgment level Lv. However, if there is no data identical to the judgment level Lv, the time when the value is closest to the judgment level Lv is identified, and the time difference dT is obtained. The time difference dT is calculated by multiplying the number of thresholds C 1L ,C 2L ,C 3L The time change T obtained from eachEOstd The time difference dT is obtained for (k). The obtained time difference dT is the time difference of the entry time, and is expressed as time difference dTi. Furthermore, the control unit 300 obtains the time difference dT at the judgment level Lv on the exit time side as well. The obtained time difference dT is the time difference of the exit time, and is expressed as time difference dTo. Note that the time difference may be evaluated using various indices, a plurality of judgment levels Lv may be set, and the time change T EOstd The time difference dT i may be evaluated by an amount obtained by integrating the difference between (k) and the time series data U(k), etc. Alternatively, the sum of the absolute value of the time difference dT i and the absolute value of the time difference dT o may be measured for each of a plurality of thresholds, and a threshold that minimizes the sum may be obtained based on the correlation between the sum and the thresholds.

[0131] The control unit 300 acquires a default threshold value based on the time difference dTi on the entry time side and the time difference dTo on the exit time side acquired based on multiple threshold values. FIG. 29 is an example showing a process of acquiring a default threshold value. In FIG. 29, the horizontal axis is the provisionally set threshold value, and the vertical axis is the time difference. In FIG. 29, the solid line indicates the time difference dTi on the entry time side, and the dashed line indicates the time difference dTo on the exit time side. The control unit 300 considers that a straight line connecting the time difference dTi on the entry time side acquired based on multiple provisionally set threshold values ​​indicates a correlation between the threshold value and the time difference dTi on the entry time side. In addition, the control unit 300 considers that a straight line connecting the time difference dTo on the exit time side acquired based on multiple provisionally set threshold values ​​indicates a correlation between the threshold value and the time difference dTo on the exit time side. Then, the control unit 300 acquires a threshold value (the intersection of the straight lines in FIG. 29) at which the time difference dTi (absolute value) on the entry time side and the time difference dTo (absolute value) on the exit time side are minimized, and calculates a default threshold value C L According to the above process, the threshold value can be optimized according to the bridge and the environment.

[0132] (3-2) Third embodiment: Furthermore, at the approach time t i and exit time t o is the time series data u after the vibration component has been removed. lp (k) and a given threshold C LFor example, the time acquisition unit 303 shown in FIG. 13 may be configured to determine the time series data u lp (k) is differentiated and the differentiated time series data u lp Based on the waveform of (k), the approach time t i and exit time t o and may be obtained.

[0133] That is, as shown in FIG. 16, at the approach time t i and exit time t o is the low-pass filtered time series data u lp (k) exists in the slope part. lp (k) Since it is almost constant or the rate of change is slow, if you calculate the derivative, the slope part will be downward or upward convex, and the part other than the slope will be almost flat. Figure 30 shows the time series data u when there is no drift noise. lp (k) and its time derivative v lp FIG. 31 shows the time series data u lp (k) and its time derivative v lp In both figures, the horizontal axis is time, the vertical axis is the amount of deflection, and the time series data u lp (k) is the dotted line, and the time derivative v lp (k) is shown by a solid line.

[0134] The time differential may be obtained by various methods. For example, the control unit 300 obtains the time differential by the difference method shown in Equation (58). i and exit time t o In order to obtain the average interval k, it is preferable that the time-differentiated time series data is smoothed. a The smoothing is performed as shown in equation (59) by using the moving average of v lp (k) is the time series data after smoothing. Note that smoothing may be omitted depending on the waveform after time differentiation.

number

number

[0135] As shown in Fig. 30 and Fig. 31, the approach time t i The time derivative of the slope where o The time derivative of the slope portion where there is a positive peak (an upward convex peak). As shown in Figures 30 and 31, each peak is separated into two peaks. The occurrence of these peak separations is thought to be due to the fact that there are two axles on the railway vehicle near the front end and two axles near the rear end. In other words, when the wheels enter the unit girder, it has a large effect on the displacement of the unit girder, so the beginning of the slope is formed when the two axles near the front end enter the unit girder, and the tendency of the displacement changes when the two axles near the rear enter the unit girder.

[0136] It is believed that the peaks are separated due to these influences. Therefore, in this embodiment, the control unit 300 calculates the differential time series data v lp When the peak of (k) is separated, the time to be acquired is the entry time t i If so, the time of the peak that corresponds to the earliest time among the separated negative peaks is taken as the entry time t i On the other hand, the control unit 300 acquires the differentiated time series data v lp When the peak of (k) is separated, the time to be acquired is the entry time t o If so, the time of the peak that corresponds to the later of the separated positive peaks is taken as the entry time t o Obtain as.

[0137] Specifically, the control unit 300 calculates the time differential v lp (k) based on the minimum value p min Coordinates of (k min ,v min ) and the maximum value p max Coordinates of (k max ,vmax Then, the control unit 300 determines the minimum value p min Data k that gives min is the time when train 6 enters the unit bridge girder t i The data k corresponding to i In addition, the control unit 300 acquires the maximum value p max Data k that gives max is the time when train 6 leaves the unit bridge girder, t o The data k corresponding to o In addition, the control unit 300 acquires the passing period t s k corresponds to s k s =k o -k i Obtain as.

[0138] After the above process is performed, the number acquisition unit 304 acquires the time series data u(k) acquired by the function of the data acquisition unit 301 and the entry time t acquired by the function of the time acquisition unit 303. i and exit time t o The control unit 300 has a function of acquiring the number of rail cars included in the rail train 6 based on the above. i and o Based on this, the passing period t s Then, the control unit 300 derives the derived t s and the fundamental frequency F derived based on u(k) f Based on this, we use equation (33) to calculate the passing period t s The fundamental frequency F f The control unit 300 derives the number N of railcars included in the railcar train 6 based on the derived v, using equation (34). When the method of the second embodiment is applied to the same time-series data u(k) as in the first embodiment, t i = 7.21 [s], t o = 12.76 [s], t s =5.55[s], F f= 3.01 [Hz], the control unit 300 obtains the number N of railcars as round((5.55) × 3.01 - 1) = round(15.71) = 16. With the above configuration, regardless of the presence or absence of drift noise, the approach time t i and exit time t o can be obtained.

[0139] (3-3) Fourth embodiment: Furthermore, time series data u lp Based on the shape characteristics of (k), the approach time t i and exit time t o The configuration for acquiring the time series data u that has been subjected to the low-pass filter process is not limited to that of the third embodiment. For example, the control unit 300 acquires the time series data u that has been subjected to the low-pass filter process by the time acquisition unit 303 shown in FIG. lp (k) is subjected to a high-pass filter process to attenuate the frequency range below the fundamental frequency, and the first peak is detected at the entry time t based on the waveform of the time series data after the high-pass filter process. i and the final peak is taken as the exit time t o Here, the fundamental frequency may be obtained by the time series data u lp Of the natural resonance peaks (natural vibration frequency of the unit bridge girder) shown by the Fourier transform result for (k), this corresponds to the peak with the lowest corresponding frequency.

[0140] The high-pass filter process may be realized by various methods. In this embodiment, the time series data u(k)-time series data u lp High-pass filtering is performed by (k), however, the high-pass filtering may be performed by various processes, such as an FIR high-pass filter with a cutoff lower than the fundamental frequency.

[0141] High-pass filtered time series data u hp 21. In the fourth embodiment, the control unit 300 uses the function of the time acquisition unit 303 to obtain the time series data u hpIn (k), a positive peak (a peak that is convex upward) P 1 The coordinates of (k 1 ,u 1 ), Peak P 3 The coordinates of (k 3 ,u 3 Then, the control unit 300 uses the function of the time acquisition unit 303 to identify the first peak P 1 Data k in the coordinates of 1 is the time when train 6 enters the unit bridge girder t i The data k corresponding to i The control unit 300 also acquires the final peak P 3 Data k in the coordinates of 3 is the time when train 6 leaves the unit bridge girder, t o The data k corresponding to o In addition, the control unit 300 acquires the passing period t s k corresponds to s k s =k o -k i The peak may be specified by various methods, for example, data in which the amount of deflection is equal to or greater than a predetermined value may be specified as the peak, or data in the top two locations in which the amount of deflection is large may be specified as the peak, and various configurations may be adopted.

[0142] After the above process is performed, the number acquisition unit 304 acquires the time series data u(k) acquired by the function of the data acquisition unit 301 and the entry time t acquired by the function of the time acquisition unit 303. i and exit time t o The control unit 300 has a function of acquiring the number of rail cars included in the rail train 6 based on the above. i and o Based on this, the passing period t s Then, the control unit 300 derives the derived t s and the fundamental frequency F derived based on u(k) f Based on this, we use equation (33) to calculate the passing period t s The fundamental frequency F fThe control unit 300 derives the number N of railcars included in the railcar train 6 based on the derived v, using equation (34). When the method of the third embodiment is applied to the same time series data u(k) as in the first embodiment, t i = 7.035 [s], t o = 12.92 [s], t s =5.885[s], F f = 3.01 [Hz], the control unit 300 obtains the number N of railway cars as round((5.885) × 3.01 - 1) = round(16.71) = 17.

[0143] The time series data shown in Figure 21 hp (k) is the time series data u with drift noise as shown in Figure 20. lp (k). However, the time series data u hp The waveform shown in (k) is not affected by drift noise. Therefore, according to this embodiment, even if the time series data u(k) includes drift noise, the approach time t i and exit time t o can be obtained.

[0144] In this embodiment, the high-pass filter process is performed by subtracting the time series data u(k) from the time series data u lp (k) is obtained. Time series data u lp Since (k) is given by equation (37) as described above, the fundamental frequency F of the time series data u(k) is f The fundamental period T obtained from f Therefore, the high-pass filter process is a time series data u lp This is the process of subtracting (k) from the time series data u(k) before the moving average.

[0145] Figure 32 shows the time series data u obtained by such a moving average. lp (k) and time series data u hp32 shows the gain frequency characteristic of (k). In FIG. 32, the horizontal axis is frequency and the vertical axis is gain. Note that in FIG. 32, the time series data u lp The gain frequency characteristic of (k) is indicated in gray with the label LPF, and the time series data u after high-pass filtering is hp The gain frequency characteristic of (k) is indicated in gray with the label HPF. In addition, the frequency characteristic of the time series data u(k) (intensity after FFT) is also indicated in black.

[0146] As shown in Figure 32, the time series data u after low-pass filtering lp The gain frequency characteristic of (k) decreases from 1 as the frequency increases in the range below 3.01 Hz of the fundamental frequency Ft. Also, above the fundamental frequency Ft, the gain reaches a minimum value of 0 at frequencies that are natural number multiples of the fundamental frequency, and repeats the characteristic of the gain increasing between frequencies that are natural number multiples of the fundamental frequency. Also, the gain tends to decrease as the frequency increases. As a result, the low-pass filter mainly attenuates data at frequencies above the fundamental frequency Ft.

[0147] The high-pass filter processing according to the present embodiment is carried out on the time series data u lp This is a process in which (k) is subtracted from the time series data u(k) before the low-pass filter process. Therefore, the time series data u after the high-pass filter process hp (k) also has a characteristic that reflects the gain frequency characteristic of the low-pass filter processing.

[0148] That is, as shown in Figure 32, the time series data u hp The gain frequency characteristic of (k) gradually increases with increasing frequency in the range below 3.01 Hz of the fundamental frequency Ft. Also, above the fundamental frequency Ft, the gain reaches a maximum value of 1 at frequencies that are natural number multiples of the fundamental frequency, and the characteristic of the gain decreasing is repeated between frequencies that are natural number multiples of the fundamental frequency. The amount of gain decrease gradually decreases, and the gain approaches 1 at higher frequencies. As a result, data at frequencies below the fundamental frequency Ft is mainly attenuated.

[0149] As described above, the high-pass filter processing according to this embodiment has a maximum value of 1 at frequencies equal to or greater than the fundamental frequency Ft and which are natural number multiples of the fundamental frequency. Therefore, according to this embodiment, the high-pass filter processing can be performed without damaging the vibration components and their harmonics that are included in the observed time series data u(k) and induced by the railway vehicle. As a result, the time series data u after high-pass filter processing hp In (k), the waveform including the vibration component caused by the passage of the railway vehicle can be analyzed, and the approach time t i and exit time t o This increases the likelihood of accurately obtaining the desired result.

[0150] (4) Other embodiments: The above embodiment is an example for carrying out the present invention, and various other embodiments can be adopted. The method for acquiring the entry time of a train into a structure and the exit time of a train from a structure based on the displacement at an observation point as in the above embodiment can also be realized as a program invention or a method invention.

[0151] Furthermore, a configuration may be adopted in which the functions of the server device 3 are realized by a plurality of devices. Also, each function of the server device 3 may be implemented in another device. For example, each function of the data acquisition unit 301, the removal unit 302, the time acquisition unit 303, and the number acquisition unit 304 may be implemented by the measurement device 1. Furthermore, the above-described embodiment is merely an example, and an embodiment in which some components are omitted or other components are added may be adopted.

[0152] In the above-described embodiment, the time acquisition system 10 acquires the time when the railway train 6, which is a train of one or more moving bodies, enters a unit bridge girder, which is a structural body, and exits the unit bridge girder. The train of moving bodies may be a train of one or more moving bodies, and the number of moving bodies may be one or more. Therefore, the time acquisition system 10 may acquire the entry time and the exit time of a train of moving bodies other than a railway train. For example, the time acquisition system 10 may acquire the entry time and the exit time of a train of moving bodies, which is a train of one or more trolleys, which are coupled together. The time acquisition system 10 may also acquire the entry time and the exit time of a vehicle included in a trailer to which one or more vehicles are coupled.

[0153] The observation point may be a position where the response of the structure can be observed, and is not limited to the center position of the structure. The physical quantity may be a response caused by the movement of the train moving body on the structure, and when the structure changes when it is affected by the movement of the train moving body or the weight of the train moving body compared to when it is not affected, the change is the response. Therefore, the structure is not limited to a unit bridge girder, and the response is not limited to the acceleration of the structure. For example, the time acquisition system 10 may acquire the entry time and exit time for a train moving body moving on a structure other than a bridge, such as a concrete foundation supporting a track. In the above embodiment, the number of sensor devices 2 included in the time acquisition system 10 is two, but it may be one, or three or more.

[0154] In the above embodiment, the control unit 300 acquires, as the time-series data u(k), data on displacement (deflection) measured from acceleration detected via the acceleration sensor 210. However, the control unit 300 may acquire, as u(k), data on the displacement of a unit bridge girder derived from a physical quantity detected via a sensor such as an impact sensor, a pressure sensor, a strain gauge, an image measuring device, a load cell, or a displacement meter. For example, the control unit 300 may detect the displacement of the observation point by periodically photographing a given object placed at the observation point of the bridge 5 via an image measuring device, and acquire data on the detected displacement. Furthermore, the physical quantity is a quantity obtained by actually measuring a change occurring at the observation point of the structure, and may be a quantity for identifying a value to be observed with respect to the train moving body. Therefore, the physical quantity may be various quantities, and the control unit 300 may acquire, as u(k), data on a physical quantity different from the displacement of a unit bridge girder. For example, the control unit 300 may acquire, as u(k), acceleration, speed, stress, or the like at the observation point of the bridge 5. The control unit 300 may also acquire, as u(k), the number of pixels corresponding to the amount of displacement of a given object placed at the observation point of the bridge 5 in an image captured via the image measuring device. The control unit 300 may also acquire data on multiple types of physical quantities (e.g., displacement, stress, etc.) that occur at the observation point as a response to the railway train 6 moving across the unit bridge girder.

[0155] The vibration components included in the time series data are responses other than the responses due to the entry of the train moving body onto the structure and the exit of the train moving body from the structure. Therefore, when the train moving body is composed of multiple moving bodies, the vibration components included in the time series data include vibration components generated in the structure when the moving body between the leading moving body and the trailing moving body moves onto the structure and exits from the structure. The method for removing the vibration components is not limited to the above-mentioned low-pass filter processing, and various methods may be adopted. For example, the processing may be such that the displacement corresponding to any k is regarded as a statistical value (minimum value, maximum value, median value) of a specific range including the k. In addition, a low-pass filter circuit may be included in the circuit that acquires the time series data u(k), and various other configurations may be adopted.

[0156] The entry time is the time when the train moves onto the structure. Therefore, typically, it is the time when the contact point (wheel) between the leading moving body of the train moves and the structure reaches onto the structure, but it may have another definition, for example, the time when the leading end of the train moves from outside the structure to on the structure. The same is true for the exit time, which may be the time when the contact point (wheel) between the trailing moving body of the train moves from on the structure to outside the structure.

[0157] In the above embodiment, the control unit 300 identifies a peak corresponding to the lowest frequency from the result of the FFT on the time series data u(k) acquired by the function of the data acquisition unit 301, excluding a side lobe caused by the influence of the window function used in the FFT, and calculates the identified peak as the fundamental frequency F f However, the control unit 300 takes into consideration the effect of noise that occurs in the result of the FFT for u(k) and calculates the fundamental frequency F f For example, the control unit 300 may determine, from the result of the FFT on u(k), a peak equal to or higher than a predetermined threshold value corresponding to the lowest frequency, excluding a side lobe caused by the effect of the window function used in the FFT, and calculate the determined peak as the fundamental frequency F f The time series data may be data acquired at a data rate that is at least twice the frequency of vibrations that are expected to occur in the structure due to the movement of the train moving body.

[0158] Furthermore, the present invention can be applied as a program or method executed by a computer. The above-mentioned program and method may be realized as a single device or may be realized by using parts of multiple devices, and include various aspects. In addition, the invention can be modified as appropriate, such as being partly software and partly hardware. Furthermore, the invention can also be realized as a recording medium for a program. Of course, the recording medium for the program may be a magnetic recording medium or a semiconductor memory, and any recording medium developed in the future can be considered in the same way. [Explanation of symbols]

[0159] 1...measuring device, 2...sensor device, 3...server device, 4...communication network, 5...bridge, 6...railroad train, 7...superstructure, 7a...bridge deck, 7b...bearing, 7c...rail, 7d...sleeper, 7e...ballast, 8...substructure, 8a...pier, 8b...abutment, 10...time acquisition system, 100...control unit, 110...storage unit, 120...communication unit, 200...control unit, 210...acceleration sensor, 220...storage unit, 230...communication unit, 300...control unit, 301...data acquisition unit, 302...removal unit, 303...time acquisition unit, 304...number acquisition unit, 310...storage unit, 320...communication unit

Claims

1. a data acquisition step of acquiring time series data showing a change in displacement of the structure over time based on a physical quantity occurring at a predetermined observation point in the structure as a response to a movement of a structure by a mobile unit formed of one or more mobile units; a removal step of removing a vibration component contained in the time series data; a time acquiring step of acquiring an entry time at which the train moving body enters the structure and an exit time at which the train moving body exits the structure based on the time series data from which the vibration component has been removed; Including, In the removing step, performing a low-pass filter process on the time series data to remove the vibration component; In the time acquisition step, performing a high-pass filter process on the time-series data that has been subjected to the low-pass filter process; obtaining a first peak and a second peak of the time series data subjected to the high-pass filter processing, and obtaining a first section before the first peak, a second section between the first peak and the second peak, and a third section after the second peak; Inverting the sign of the time series data that has been subjected to the high-pass filter processing in the first section to obtain a correction curve for the first section; In the second section, A first straight line is obtained as an approximation straight line of the correction curve in the first section in a section smaller than a product of a value obtained by inverting the sign of the amplitude of the first peak and a first coefficient; A second straight line is obtained by multiplying a straight line passing through the first peak and the second peak by a second coefficient; A third straight line is obtained as an approximation straight line of the correction curve of the third section in a section smaller than a product of a value obtained by inverting the sign of the amplitude of the second peak and a third coefficient; Obtaining a first intersection point between the first line and the second line and a second intersection point between the second line and the third line; a correction curve for the second section is obtained by defining a portion before the first intersection as the first straight line, a portion from the first intersection to the second intersection as the second straight line, and a portion after the second intersection as the third straight line in the second section; Inverting the sign of the time series data that has been subjected to the high-pass filter processing in the third section to obtain a correction curve for the third section; adding the correction curve for the first section, the correction curve for the second section, and the correction curve for the third section to obtain correction data; a process of acquiring a time within a period after one of two times corresponding to two consecutive data on either side of a predetermined threshold, which are included in the sum of the time series data subjected to the high-pass filter process and the correction data, and before the other of the two times, for each of the entry time and the exit time, thereby acquiring the entry time and the exit time; How to get time.

2. a data acquisition step of acquiring time series data showing a change in displacement of the structure over time based on a physical quantity occurring at a predetermined observation point in the structure as a response to a movement of a structure by a mobile unit formed of one or more mobile units; a removal step of removing a vibration component contained in the time series data; a time acquiring step of acquiring an entry time at which the train moving body enters the structure and an exit time at which the train moving body exits the structure based on the time series data from which the vibration component has been removed; Including, In the removing step, performing a low-pass filter process on the time series data to remove the vibration component; In the time acquisition step, performing a high-pass filter process on the time-series data that has been subjected to the low-pass filter process; obtaining a first peak and a second peak of the time series data subjected to the high-pass filter processing, and obtaining a first section before the first peak, a second section between the first peak and the second peak, and a third section after the second peak; Inverting the sign of the time series data that has been subjected to the high-pass filter processing in the first section to obtain a correction curve for the first section; A first straight line is obtained as an approximation straight line of the correction curve in the first section in a section smaller than a product of a value obtained by inverting the sign of the amplitude of the first peak and a first coefficient; Inverting the sign of the time series data that has been subjected to the high-pass filter processing in the third section to obtain a correction curve for the third section; A third straight line is obtained as an approximation straight line of the correction curve of the third section in a section smaller than a product of a value obtained by inverting the sign of the amplitude of the second peak and a third coefficient; acquiring, as the entry time, a time within a period after one of two times corresponding to two consecutive data on either side of a predetermined threshold, which are included in the sum of the time series data subjected to the high-pass filter process and the first straight line, and before the other time; acquiring, as the exit time, a time within a period after one of two times corresponding to two consecutive data on either side of a predetermined threshold, which are included in the sum of the time series data subjected to the high-pass filter process and the third line, and before the other of the two times; How to get time.

3. a data acquisition step of acquiring time series data showing a change in displacement of the structure over time based on a physical quantity occurring at a predetermined observation point in the structure as a response to a movement of a structure by a mobile unit formed of one or more mobile units; a removal step of removing a vibration component contained in the time series data; a time acquiring step of acquiring an entry time at which the train moving body enters the structure and an exit time at which the train moving body exits the structure based on the time series data from which the vibration component has been removed; Including, In the removing step, performing a low-pass filter process on the time series data to remove the vibration component; In the time acquisition step, Differentiating the time series data that has been subjected to the low-pass filter processing, obtaining a negative peak and a positive peak based on a waveform of the differentiated time series data, obtaining the negative peak as the entry time, and obtaining the positive peak as the exit time. How to get time.

4. a data acquisition step of acquiring time series data showing a change in displacement of the structure over time based on a physical quantity occurring at a predetermined observation point in the structure as a response to a movement of a structure by a mobile unit formed of one or more mobile units; a removal step of removing a vibration component contained in the time series data; a time acquiring step of acquiring an entry time at which the train moving body enters the structure and an exit time at which the train moving body exits the structure based on the time series data from which the vibration component has been removed; Including, In the removing step, performing a low-pass filter process on the time series data to remove the vibration component; In the time acquisition step, performing a high-pass filter process on the time-series data that has been subjected to the low-pass filter process to attenuate a frequency range below a fundamental frequency, and acquiring a first peak as the entry time and a last peak as the exit time based on a waveform of the time-series data that has been subjected to the high-pass filter process; How to get time.

5. From the time series data acquired as a response to the known train moving body moving through the structure and a plurality of provisionally set thresholds, observation information including the entry time, the exit time, and the number of the moving body is acquired in correspondence with each of the plurality of provisionally set thresholds, environmental information including a structure length which is the length of the structure, a moving body length which is the length of the moving body, and an installation position of a contact portion of the moving body with the structure is acquired, a time change in the amount of deflection at each of the provisionally set thresholds based on the observation information, the environmental information, and an approximation equation for deflection is acquired, a time difference at a predetermined judgment level between each of the acquired amounts of deflection and the time series data is acquired, and the threshold having the smallest time difference is acquired based on a correlation between the acquired time difference and the plurality of provisionally set thresholds, and this is set as the default threshold. The time acquisition method according to claim 1 or 2.

6. The amount of deflection at each of the thresholds is Derived from an equation that represents a model of deflection determined based on the structure of the structure; The time acquisition method according to claim 5 .

7. In the time acquisition step, When the negative peak and the positive peak of the differentiated time series data are separated, the time of the peak corresponding to the earlier time in the negative peak is acquired as the entry time, and the time of the peak corresponding to the later time in the positive peak is acquired as the exit time. The time acquisition method according to claim 3 .

8. The high-pass filter process is A process of subtracting the time series data obtained by performing a moving average with a period that is a natural number multiple of a fundamental period obtained from the fundamental frequency of the time series data from the time series data before the moving average, or a FIR high pass filter with a cutoff lower than the fundamental frequency; The time acquisition method according to claim 4.

9. The fundamental frequency is Among the natural resonance peaks shown by the result of Fourier transform of the time series data, the peak having the lowest corresponding frequency is determined as the fundamental frequency. The time acquisition method according to claim 8.

10. The structure is a bridge. The time acquisition method according to any one of claims 1 to 9.

11. The moving body is a railroad vehicle that moves the structure via wheels. The time acquisition method according to any one of claims 1 to 10.

12. The time-series data is based on data detected via at least one of an acceleration sensor, an impact sensor, a pressure sensor, a strain gauge, an image measuring device, a load cell, and a displacement meter. The time acquisition method according to any one of claims 1 to 11.

13. The structure is a simple beam supported at both ends. The time acquisition method according to any one of claims 1 to 12.

14. The structure is applicable to Bridge Weight in Motion (BWIM), The time acquisition method according to any one of claims 1 to 13.

15. an acquisition unit that acquires time-series data indicating a change in displacement of the structure based on a physical quantity generated at a predetermined observation point in the structure as a response to a movement of a structure by a composed mobile unit, the composed mobile unit being a group of one or more mobile units; A removal unit that removes vibration components included in the time series data; a time acquisition unit that acquires an entry time when the train moving body enters the structure and an exit time when the train moving body exits the structure based on the time series data after the vibration component has been removed; Equipped with The removal unit includes: performing a low-pass filter process on the time series data to remove the vibration component; The time acquisition unit, performing a high-pass filter process on the time-series data that has been subjected to the low-pass filter process; obtaining a first peak and a second peak of the time series data subjected to the high-pass filter processing, and obtaining a first section before the first peak, a second section between the first peak and the second peak, and a third section after the second peak; Inverting the sign of the time series data that has been subjected to the high-pass filter processing in the first section to obtain a correction curve for the first section; In the second section, A first straight line is obtained as an approximation straight line of the correction curve in the first section in a section smaller than a product of a value obtained by inverting the sign of the amplitude of the first peak and a first coefficient; A second straight line is obtained by multiplying a straight line passing through the first peak and the second peak by a second coefficient; A third straight line is obtained as an approximation straight line of the correction curve of the third section in a section smaller than a product of a value obtained by inverting the sign of the amplitude of the second peak and a third coefficient; Obtaining a first intersection point between the first line and the second line and a second intersection point between the second line and the third line; a correction curve for the second section is obtained by defining a portion before the first intersection as the first straight line, a portion from the first intersection to the second intersection as the second straight line, and a portion after the second intersection as the third straight line in the second section; Inverting the sign of the time series data that has been subjected to the high-pass filter processing in the third section to obtain a correction curve for the third section; adding the correction curve for the first section, the correction curve for the second section, and the correction curve for the third section to obtain correction data; a process of acquiring a time within a period after one of two times corresponding to two consecutive data on either side of a predetermined threshold, which are included in the sum of the time series data subjected to the high-pass filter process and the correction data, and before the other of the two times, for each of the entry time and the exit time, thereby acquiring the entry time and the exit time; Time acquisition device.

16. an acquisition unit that acquires time-series data indicating a change in displacement of the structure based on a physical quantity generated at a predetermined observation point in the structure as a response to a movement of a structure by a composed mobile unit, the composed mobile unit being formed of one or more mobile units; A removal unit that removes vibration components included in the time series data; a time acquisition unit that acquires an entry time when the train moving body enters the structure and an exit time when the train moving body exits the structure based on the time series data after the vibration component has been removed; Equipped with The removal unit includes: performing a low-pass filter process on the time series data to remove the vibration component; The time acquisition unit performing a high-pass filter process on the time-series data that has been subjected to the low-pass filter process; obtaining a first peak and a second peak of the time series data subjected to the high-pass filter processing, and obtaining a first section before the first peak, a second section between the first peak and the second peak, and a third section after the second peak; Inverting the sign of the time series data that has been subjected to the high-pass filter processing in the first section to obtain a correction curve for the first section; A first straight line is obtained as an approximation straight line of the correction curve in the first section in a section smaller than a product of a value obtained by inverting the sign of the amplitude of the first peak and a first coefficient; Inverting the sign of the time series data that has been subjected to the high-pass filter processing in the third section to obtain a correction curve for the third section; A third straight line is obtained as an approximation straight line of the correction curve of the third section in a section smaller than a product of a value obtained by inverting the sign of the amplitude of the second peak and a third coefficient; acquiring, as the entry time, a time within a period after one of two times corresponding to two consecutive data on either side of a predetermined threshold, which are included in the sum of the time series data subjected to the high-pass filter process and the first straight line, and before the other time; acquiring, as the exit time, a time within a period after one of two times corresponding to two consecutive data on either side of a predetermined threshold, which are included in the sum of the time series data subjected to the high-pass filter process and the third line, and before the other of the two times; Time acquisition device.

17. an acquisition unit that acquires time-series data indicating a change in displacement of the structure based on a physical quantity generated at a predetermined observation point in the structure as a response to a movement of a structure by a composed mobile unit, the composed mobile unit being formed of one or more mobile units; A removal unit that removes vibration components included in the time series data; a time acquisition unit that acquires an entry time when the train moving body enters the structure and an exit time when the train moving body exits the structure based on the time series data after the vibration component has been removed; Equipped with The removal unit includes: performing a low-pass filter process on the time series data to remove the vibration component; The time acquisition unit Differentiating the time series data that has been subjected to the low-pass filter processing, obtaining a negative peak and a positive peak based on a waveform of the differentiated time series data, obtaining the negative peak as the entry time and obtaining the positive peak as the exit time. Time acquisition device.

18. an acquisition unit that acquires time-series data indicating a change in displacement of the structure based on a physical quantity generated at a predetermined observation point in the structure as a response to a movement of a structure by a composed mobile unit, the composed mobile unit being formed of one or more mobile units; A removal unit that removes vibration components included in the time series data; a time acquisition unit that acquires an entry time when the train moving body enters the structure and an exit time when the train moving body exits the structure based on the time series data after the vibration component has been removed; Equipped with The removal unit includes: performing a low-pass filter process on the time series data to remove the vibration component; The time acquisition unit performing a high-pass filter process on the time-series data that has been subjected to the low-pass filter process to attenuate a frequency range below a fundamental frequency, and acquiring a first peak as the entry time and a last peak as the exit time based on a waveform of the time-series data that has been subjected to the high-pass filter process; Time acquisition device.

19. A time acquisition system including a time acquisition device and a sensor, The time acquisition device an acquisition unit that acquires time series data showing a change in displacement of the structure over time based on a physical quantity that occurs at a predetermined observation point in the structure as a response to a movement of a structure by a composed mobile unit, the physical quantity being measured via the sensor; and A removal unit that removes vibration components included in the time series data; a time acquisition unit that acquires an entry time when the train moving body enters the structure and an exit time when the train moving body exits the structure based on the time series data after the vibration component has been removed; Equipped with The removal unit includes: performing a low-pass filter process on the time series data to remove the vibration component; The time acquisition unit, performing a high-pass filter process on the time-series data that has been subjected to the low-pass filter process; obtaining a first peak and a second peak of the time series data subjected to the high-pass filter processing, and obtaining a first section before the first peak, a second section between the first peak and the second peak, and a third section after the second peak; Inverting the sign of the time series data that has been subjected to the high-pass filter processing in the first section to obtain a correction curve for the first section; In the second section, A first straight line is obtained as an approximation straight line of the correction curve in the first section in a section smaller than a product of a value obtained by inverting the sign of the amplitude of the first peak and a first coefficient; A second straight line is obtained by multiplying a straight line passing through the first peak and the second peak by a second coefficient; A third straight line is obtained as an approximation straight line of the correction curve of the third section in a section smaller than a product of a value obtained by inverting the sign of the amplitude of the second peak and a third coefficient; Obtaining a first intersection point between the first line and the second line and a second intersection point between the second line and the third line; a correction curve for the second section is obtained by defining a portion before the first intersection as the first straight line, a portion from the first intersection to the second intersection as the second straight line, and a portion after the second intersection as the third straight line in the second section; Inverting the sign of the time series data that has been subjected to the high-pass filter processing in the third section to obtain a correction curve for the third section; adding the correction curve for the first section, the correction curve for the second section, and the correction curve for the third section to obtain correction data; a process of acquiring a time within a period after one of two times corresponding to two consecutive data on either side of a predetermined threshold, which are included in the sum of the time series data subjected to the high-pass filter process and the correction data, and before the other of the two times, for each of the entry time and the exit time, thereby acquiring the entry time and the exit time; Time acquisition system.

20. A time acquisition system including a time acquisition device and a sensor, The time acquisition device an acquisition unit that acquires time series data showing a change in displacement of the structure over time based on a physical quantity that occurs at a predetermined observation point in the structure as a response to a movement of a structure by a composed mobile unit, the physical quantity being measured via the sensor; and A removal unit that removes vibration components included in the time series data; a time acquisition unit that acquires an entry time when the train moving body enters the structure and an exit time when the train moving body exits the structure based on the time series data after the vibration component has been removed; Equipped with The removal unit includes: performing a low-pass filter process on the time series data to remove the vibration component; The time acquisition unit performing a high-pass filter process on the time-series data that has been subjected to the low-pass filter process; obtaining a first peak and a second peak of the time series data subjected to the high-pass filter processing, and obtaining a first section before the first peak, a second section between the first peak and the second peak, and a third section after the second peak; Inverting the sign of the time series data that has been subjected to the high-pass filter processing in the first section to obtain a correction curve for the first section; A first straight line is obtained as an approximation straight line of the correction curve in the first section in a section smaller than a product of a value obtained by inverting the sign of the amplitude of the first peak and a first coefficient; Inverting the sign of the time series data that has been subjected to the high-pass filter processing in the third section to obtain a correction curve for the third section; A third straight line is obtained as an approximation straight line of the correction curve of the third section in a section smaller than a product of a value obtained by inverting the sign of the amplitude of the second peak and a third coefficient; acquiring, as the entry time, a time within a period after one of two times corresponding to two consecutive data on either side of a predetermined threshold, which are included in the sum of the time series data subjected to the high-pass filter process and the first straight line, and before the other time; acquiring, as the exit time, a time within a period after one of two times corresponding to two consecutive data on either side of a predetermined threshold, which are included in the sum of the time series data subjected to the high-pass filter process and the third line, and before the other of the two times; Time acquisition system.

21. A time acquisition system including a time acquisition device and a sensor, The time acquisition device an acquisition unit that acquires time series data showing a change in displacement of the structure over time based on a physical quantity that occurs at a predetermined observation point in the structure as a response to a movement of a structure by a composed mobile unit, the physical quantity being measured via the sensor; and A removal unit that removes vibration components included in the time series data; a time acquisition unit that acquires an entry time when the train moving body enters the structure and an exit time when the train moving body exits the structure based on the time series data after the vibration component has been removed; Equipped with The removal unit includes: performing a low-pass filter process on the time series data to remove the vibration component; The time acquisition unit Differentiating the time series data that has been subjected to the low-pass filter processing, obtaining a negative peak and a positive peak based on a waveform of the differentiated time series data, obtaining the negative peak as the entry time and obtaining the positive peak as the exit time. Time acquisition system.

22. A time acquisition system including a time acquisition device and a sensor, The time acquisition device an acquisition unit that acquires time series data showing a change in displacement of the structure over time based on a physical quantity that occurs at a predetermined observation point in the structure as a response to a movement of a structure by a composed mobile unit, the physical quantity being measured via the sensor; and A removal unit that removes vibration components included in the time series data; a time acquisition unit that acquires an entry time when the train moving body enters the structure and an exit time when the train moving body exits the structure based on the time series data after the vibration component has been removed; Equipped with The removal unit includes: performing a low-pass filter process on the time series data to remove the vibration component; The time acquisition unit performing a high-pass filter process on the time-series data that has been subjected to the low-pass filter process to attenuate a frequency range less than a fundamental frequency, and acquiring a first peak as the entry time and a last peak as the exit time based on a waveform of the time-series data that has been subjected to the high-pass filter process; Time acquisition system.

23. On the computer, a data acquisition step of acquiring time series data showing a change in displacement of the structure over time based on a physical quantity occurring at a predetermined observation point in the structure as a response to a movement of a structure by a unit of one or more mobile units; a removal step of removing a vibration component contained in the time series data; a time acquiring step of acquiring an entry time at which the train moving body enters the structure and an exit time at which the train moving body exits the structure based on the time series data from which the vibration component has been removed; Run the command, In the removing step, performing a low-pass filter process on the time series data to remove the vibration component; In the time acquisition step, performing a high-pass filter process on the time-series data that has been subjected to the low-pass filter process; obtaining a first peak and a second peak of the time series data subjected to the high-pass filter processing, and obtaining a first section before the first peak, a second section between the first peak and the second peak, and a third section after the second peak; Inverting the sign of the time series data that has been subjected to the high-pass filter processing in the first section to obtain a correction curve for the first section; In the second section, A first straight line is obtained as an approximation straight line of the correction curve in the first section in a section smaller than a product of a value obtained by inverting the sign of the amplitude of the first peak and a first coefficient; A second straight line is obtained by multiplying a straight line passing through the first peak and the second peak by a second coefficient; A third straight line is obtained as an approximation straight line of the correction curve of the third section in a section smaller than a product of a value obtained by inverting the sign of the amplitude of the second peak and a third coefficient; Obtaining a first intersection point between the first line and the second line and a second intersection point between the second line and the third line; a correction curve for the second section is obtained by defining a portion before the first intersection as the first straight line, a portion from the first intersection to the second intersection as the second straight line, and a portion after the second intersection as the third straight line in the second section; Inverting the sign of the time series data that has been subjected to the high-pass filter processing in the third section to obtain a correction curve for the third section; adding the correction curve for the first section, the correction curve for the second section, and the correction curve for the third section to obtain correction data; a process of acquiring a time within a period after one of two times corresponding to two consecutive data on either side of a predetermined threshold, which are included in the sum of the time series data subjected to the high-pass filter process and the correction data, and before the other of the two times, for each of the entry time and the exit time, thereby acquiring the entry time and the exit time; Time acquisition program.

24. On the computer, a data acquisition step of acquiring time series data showing a change in displacement of the structure over time based on a physical quantity occurring at a predetermined observation point in the structure as a response to a movement of a structure by a unit of one or more mobile units; a removal step of removing a vibration component contained in the time series data; a time acquiring step of acquiring an entry time at which the train moving body enters the structure and an exit time at which the train moving body exits the structure based on the time series data from which the vibration component has been removed; Run the command, In the removing step, performing a low-pass filter process on the time series data to remove the vibration component; In the time acquisition step, performing a high-pass filter process on the time-series data that has been subjected to the low-pass filter process; obtaining a first peak and a second peak of the time series data subjected to the high-pass filter processing, and obtaining a first section before the first peak, a second section between the first peak and the second peak, and a third section after the second peak; Inverting the sign of the time series data that has been subjected to the high-pass filter processing in the first section to obtain a correction curve for the first section; A first straight line is obtained as an approximation straight line of the correction curve in the first section in a section smaller than a product of a value obtained by inverting the sign of the amplitude of the first peak and a first coefficient; Inverting the sign of the time series data that has been subjected to the high-pass filter processing in the third section to obtain a correction curve for the third section; A third straight line is obtained as an approximation straight line of the correction curve of the third section in a section smaller than a product of a value obtained by inverting the sign of the amplitude of the second peak and a third coefficient; acquiring, as the entry time, a time within a period after one of two times corresponding to two consecutive data on either side of a predetermined threshold, which are included in the sum of the time series data subjected to the high-pass filter process and the first straight line, and before the other time; acquiring, as the exit time, a time within a period after one of two times corresponding to two consecutive data on either side of a predetermined threshold, which are included in the sum of the time series data subjected to the high-pass filter process and the third line, and before the other of the two times; Time acquisition program.

25. On the computer, a data acquisition step of acquiring time series data showing a change in displacement of the structure over time based on a physical quantity occurring at a predetermined observation point in the structure as a response to a movement of a structure by a unit of one or more mobile units; a removal step of removing a vibration component contained in the time series data; a time acquiring step of acquiring an entry time at which the train moving body enters the structure and an exit time at which the train moving body exits the structure based on the time series data from which the vibration component has been removed; Run the command, In the removing step, performing a low-pass filter process on the time series data to remove the vibration component; In the time acquisition step, Differentiating the time series data that has been subjected to the low-pass filter processing, obtaining a negative peak and a positive peak based on a waveform of the differentiated time series data, obtaining the negative peak as the entry time, and obtaining the positive peak as the exit time. Time acquisition program.

26. On the computer, a data acquisition step of acquiring time series data showing a change in displacement of the structure over time based on a physical quantity occurring at a predetermined observation point in the structure as a response to a movement of a structure by a unit of one or more mobile units; a removal step of removing a vibration component contained in the time series data; a time acquiring step of acquiring an entry time at which the train moving body enters the structure and an exit time at which the train moving body exits the structure based on the time series data from which the vibration component has been removed; Run the command, In the removing step, performing a low-pass filter process on the time series data to remove the vibration component; In the time acquisition step, performing a high-pass filter process on the time-series data that has been subjected to the low-pass filter process to attenuate a frequency range below a fundamental frequency, and acquiring a first peak as the entry time and a last peak as the exit time based on a waveform of the time-series data that has been subjected to the high-pass filter process; Time acquisition program.

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