Method and system for monitoring abnormalities in bridge girder support sections.

Abstract

To provide a bridge girder bearing part abnormality monitoring method and a bridge girder bearing part abnormality monitoring system for monitoring abnormality of a bridge girder bearing part by a quartz vibration type acceleration sensor.SOLUTION: The amount of warpage of a steel bridge 3 can be estimated on the basis of: a value (value of total amplitude of speed data / train speed, maximum value of speed data / train speed, minimum value of speed data / train speed, maximum absolute value of speed data / train speed) obtained by dividing, by a train speed, a value (value of total amplitude of speed data, maximum value of speed data, minimum value of speed data, maximum absolute value of speed data) which is related to a waveform of speed data that is a value to be associated with acceleration data obtained by measuring the acceleration of vibration of the steel bridge 3 at a train passage by using a quartz vibration type acceleration sensor 13 installed in the bearing part of the steel bridge 3; and a predefined correlation. If the estimated amount of warpage is greater than a threshold value, the presence or absence of an abnormality in the bridge girder bearing part can be monitored by determining that there is an abnormality in the steel bridge 3.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] This invention relates to a method for monitoring abnormalities in bridge girder support sections and a system for monitoring abnormalities in bridge girder support sections. [Background technology]

[0002] Conventionally, there is a known technique for detecting abnormalities in the support structure of a railway bridge (for example, a steel bridge) by measuring changes in the acceleration of the bridge girder vibration when a train passes over it using acceleration sensors attached to the bridge (see, for example, Patent Document 1). [Prior art documents] [Patent Documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2016-50404 [Overview of the Initiative] [Problems that the invention aims to solve]

[0004] However, the technology described in Patent Document 1 above did not detect the vertical movement of the girders, referred to as "tailgating," based on acceleration measured by an acceleration sensor. When "swaying" occurs at the support structure of a railway bridge, it can damage nearby structural members. Therefore, it is desirable to detect "swaying" before damage or other problems occur. Therefore, the inventors diligently conducted research and developed a technology to detect abnormalities related to "tilting" using acceleration sensors attached to bridge girders (steel bridges).

[0005] The object of the present invention is to provide a method for monitoring abnormalities in bridge girder bearings and a system for monitoring abnormalities in bridge girder bearings, which monitor abnormalities in bridge girder bearings using an acceleration sensor. [Means for solving the problem]

[0006] To achieve the above objective, one invention relating to this application is: A method for monitoring abnormalities in a bridge girder support, which uses a quartz crystal vibration acceleration sensor installed in the support of the bridge girder to monitor abnormalities in the support, The acceleration measurement step involves using the aforementioned quartz crystal vibration acceleration sensor to measure the acceleration of the vibration of the bridge girder when a train passes and to acquire acceleration data. A filtering process for extracting low-frequency acceleration data, including train passing frequencies, from the aforementioned acceleration data, The velocity waveform acquisition process involves integrating the acceleration data extracted through filtering to convert it into velocity data, and then obtaining the waveform of that velocity data. A determination step in which a determination is made whether or not an abnormality has occurred in the support part, based on a predetermined correlation between the value obtained by dividing the waveform value of the acquired speed data by the train speed at the time of the train's passage and the amount of tilt at the support part and the value obtained by dividing the waveform value of the speed data by the train speed, It has, The correlation is a value associated with the acceleration data of the bridge girder's vibration, which is measured multiple times in advance by a quartz crystal accelerometer when a train passes over it. This value is obtained by dividing the value related to the waveform of the speed data by the train speed, and the amount of vertical movement of the bridge girder, which is measured multiple times by a displacement sensor, are associated with this value.

[0007] With this configuration of bridge girder support abnormality monitoring method, a quartz crystal accelerometer is used to measure the acceleration of the bridge girder vibration when a train passes. Based on the value obtained by dividing the waveform value of the velocity data, which is a value associated with the acceleration data obtained from the measurement, by the train speed, and a predetermined correlation, the amount of sway of the bridge girder can be estimated. If the estimated amount of sway is greater than a threshold (reference value), it is determined that there is an abnormality in the bridge girder, thereby monitoring whether or not there is an abnormality in the bridge girder support. In the judgment process, for example, if the estimated amount of sway exceeds a predetermined threshold, it is determined that the vibration of the bridge girder has become greater than normal, and that there is an abnormality in the support structure.

[0008] Furthermore, preferably, The value relating to the waveform of the speed data shall be one of the following: the total amplitude of the speed data waveform, the maximum value of the speed data, the minimum value of the speed data, or the maximum absolute value of the speed data.

[0009] By obtaining velocity data waveforms from acceleration data measured using a quartz crystal accelerometer, and determining the total amplitude value of the velocity data waveform, the maximum value of the velocity data, the minimum value of the velocity data, or the maximum absolute value of the velocity data, it becomes possible to accurately estimate the amount of tilt of the bridge girder based on predetermined correlations, thereby enabling appropriate judgment regarding abnormalities in the bridge girder.

[0010] Furthermore, preferably, The quartz crystal vibration acceleration sensor is installed on the planar portion of the steel material constituting the bridge girder at the bridge girder support section, where the bridge girder is installed on the bridge abutment via a support.

[0011] This allows for the precise measurement of the acceleration of vibrations in the bridge girder when a train passes over it.

[0012] Furthermore, preferably, The correlation regarding the amount of tilt is classified into cases where the span length of the bridge girder is less than the distance between the front and rear wheels of a train, and cases where the span length of the bridge girder is greater than or equal to the distance between the front and rear wheels of a train, and the determination process is executed according to the span length of the bridge girder on which the crystal vibration type acceleration sensor is installed.

[0013] If the span length of a bridge girder (steel bridge) is less than the distance between the front and rear wheels of a train, when a train passes over that bridge girder, the wheels on both the front and rear of the train will not be on the bridge girder. On the other hand, if the span length of a bridge girder (steel bridge) is greater than or equal to the distance between the front and rear wheels of a train, then when a train passes over that bridge girder, at least one of the wheels on both the front and rear sides of the train will always be on the bridge girder. That is, when the span length of the bridge girder is less than the distance between the front and rear wheels in the train vehicle, there is a timing when the wheel load does not act on the bridge girder when the train passes over the bridge girder. When the span length of the bridge girder is equal to or greater than the distance between the front and rear wheels in the train vehicle, the wheel load always acts on the bridge girder when the train passes over the bridge girder. Thus, since there are differences in the wheel loads acting on the bridge girder according to the span length of the bridge girder, if the correlation of the aori amount is classified according to the span length of the bridge girder, a stronger correlation can be obtained, so that the determination regarding the abnormality of the bridge girder can be made more appropriately.

[0014] Further, another invention according to the present application is an abnormal monitoring system for a bridge girder support portion for implementing the above-described abnormal monitoring method for a bridge girder support portion, an acceleration measurement device including a crystal vibration type acceleration sensor that measures the acceleration of the vibration of the bridge girder when a train passes; and an information management device capable of communicating with the acceleration measurement device. The abnormal monitoring system for the bridge girder support portion is provided with control means for executing notification regarding an abnormality in the support portion based on the acceleration data measured and acquired by the crystal vibration type acceleration sensor.

[0015] With an abnormal monitoring system for a bridge girder support portion having such a configuration, by measuring the acceleration of the vibration of the bridge girder when a train passes using the crystal vibration type acceleration sensor provided in the acceleration measurement device, the value obtained by dividing the value regarding the waveform of the speed data, which is a value associated with the acceleration data obtained by the measurement, by the train speed and the predetermined correlation, the aori amount of the bridge girder can be estimated. When the estimated aori amount is greater than the reference value, it is determined that there is an abnormality in the bridge girder, and the presence or absence of an abnormality in the bridge girder support portion can be monitored. In particular, with this abnormal monitoring system, it is possible to notify the presence or absence of an abnormality in the bridge girder support portion to the administrator of the bridge girder or the like.

[0016] Also, preferably, the control means The acceleration measuring device includes a measuring device side control unit that performs a process of transmitting the acceleration data measured and acquired by the quartz crystal oscillating acceleration sensor to the information management device, The information management device includes a control unit that performs a filtering process to extract low-frequency acceleration data, including the train passing frequency, from the acceleration data transmitted from the acceleration measuring device; a process to integrate the acceleration data extracted by the filtering process to convert it into velocity data and obtain the waveform of the velocity data; a process to obtain values ​​related to the waveform of the velocity data from the obtained waveform; a process to determine whether there is an abnormality in the support unit based on a predetermined correlation between the value obtained by dividing the value related to the waveform of the velocity data by the train speed at the time of the train's passage and the amount of tilt at the support unit and the value related to the waveform of the velocity data by the train speed, and then performs a process to notify the management device of the abnormality. To have.

[0017] In this bridge girder support abnormality monitoring system, acceleration data measured by a quartz oscillator acceleration sensor in the acceleration measurement device is transmitted to the information management device. The information management device then calculates the amount of tilt of the bridge girder when a train passes over it from the acceleration data, and based on the calculated amount of tilt, it performs a process to determine whether or not there is an abnormality in the support, and can then provide notification regarding the abnormality.

[0018] Furthermore, preferably, The control means is The acceleration measuring device includes a measuring device side control unit that performs the following processes: filtering to extract low-frequency acceleration data including the train passing frequency from the acceleration data measured and acquired by the crystal oscillator type acceleration sensor; integrating the acceleration data extracted by the filtering process to convert it into velocity data and acquiring the waveform of the velocity data; acquiring values ​​related to the waveform of the velocity data from the acquired waveform of the velocity data; and transmitting data regarding the amount of tilt of the bridge girder when a train passes to the information management device, based on a predetermined correlation between the value obtained by dividing the value related to the waveform of the velocity data by the train speed when the train passes and the amount of tilt at the support section and the value obtained by dividing the value related to the waveform of the velocity data by the train speed. The information management device includes a control unit on the management device side which performs a process to determine whether or not there is an abnormality in the support section based on the data relating to the amount of tilt transmitted from the acceleration measuring device, and performs notification regarding the abnormality, To have.

[0019] This bridge girder support abnormality monitoring system uses acceleration data measured by a quartz crystal accelerometer in the acceleration measurement device to determine the amount of tilt of the bridge girder when a train passes. This data on the amount of tilt is then transmitted to the information management device, which processes the data on the amount of tilt to determine whether there is an abnormality in the support and can then issue a notification regarding the abnormality.

[0020] Furthermore, preferably, The control means is The acceleration measuring device includes a measuring device side control unit which performs the following: filtering to extract low-frequency acceleration data including the train passing frequency from the acceleration data measured and acquired by the crystal oscillator type acceleration sensor; integrating the acceleration data extracted by the filtering process to convert it into velocity data and acquiring the waveform of the velocity data; acquiring values ​​related to the waveform of the velocity data from the acquired waveform of the velocity data; and a predetermined correlation between the value obtained by dividing the acquired value related to the waveform of the velocity data by the train speed at the time of the train's passage and the amount of tilt at the support unit and the value related to the waveform of the velocity data by the train speed, and performs a process to determine whether or not there is an abnormality in the support unit, and transmits the determination result to the information management device. The information management device includes a control unit on the management device side which, based on the judgment result transmitted from the acceleration measuring device, performs notification regarding an abnormality in the support section, To have.

[0021] In this bridge girder support abnormality monitoring system, the acceleration measurement device uses a quartz oscillator type acceleration sensor to measure acceleration data, which is then used to estimate the amount of sway of the bridge girder when a train passes. Based on this estimated amount of sway, the system performs a process to determine whether or not there is an abnormality in the support, and the result of this determination is transmitted to the information management device. Furthermore, the information management device can provide notification regarding abnormalities in the support unit based on the judgment results.

[0022] Furthermore, preferably, The control means is The acceleration measuring device includes a measuring device side control unit which performs the following: filtering of acceleration data in the low-frequency band including the train passing frequency from the acceleration data measured and acquired by the crystal oscillator type acceleration sensor; integrating the acceleration data extracted by the filtering process to convert it into velocity data and acquiring the waveform of the velocity data; acquiring values ​​related to the waveform of the velocity data from the acquired waveform of the velocity data; and transmitting the acquired values ​​related to the waveform of the velocity data to the information management device. The information management device includes a control unit on the management device side that performs a process to determine whether there is an abnormality in the support section based on a predetermined correlation between the value obtained by dividing the waveform value of the speed data transmitted from the acceleration measuring device by the train speed at the time of the train's passage and the amount of tilt at the support section and the value obtained by dividing the waveform value of the speed data by the train speed, and performs a notification regarding the abnormality. To have.

[0023] In this bridge girder support abnormality monitoring system, the system executes a process to acquire the waveform of velocity data and values ​​related to the waveform of velocity data from the acceleration data measured by the quartz oscillator type acceleration sensor of the acceleration measurement device, and transmits the values ​​related to the waveform of velocity data to the information management device. Furthermore, the information management device can determine the amount of tilt of the bridge girder when a train passes over it from the waveform values ​​of the speed data, and then execute a process to determine whether or not there is an abnormality in the support section based on the determined amount of tilt, and can then provide notification regarding that abnormality. [Effects of the Invention]

[0024] According to the present invention, by using a quartz crystal oscillating acceleration sensor as the acceleration sensor and measuring the acceleration of the bridge girder vibration when a train passes over it, it becomes possible to appropriately estimate the amount of tilt of the bridge girder based on the acceleration data obtained, and thus it is possible to suitably monitor abnormalities in the bridge girder support section. [Brief explanation of the drawing]

[0025] [Figure 1] This is an explanatory diagram regarding the abnormality monitoring system for steel bridge bearings. [Figure 2] This diagram illustrates the installation location of the acceleration measurement device (quartz oscillator type acceleration sensor) in the steel bridge support section, and includes a side view (a) and a cross-sectional view (b) along line bb in Figure 2(a). [Figure 3] This is a schematic block diagram of the abnormality monitoring system for steel bridge bearings. [Figure 4] This table shows the results of a correlation analysis based on the coefficient of determination between the acceleration index of steel bridge vibration measured by various acceleration sensors and the amount of sway in the steel bridge. The results are for servo-type acceleration sensors (a), piezoelectric acceleration sensors (b), quartz crystal accelerometers (c), and capacitive acceleration sensors (d). [Figure 5] This is an explanatory diagram regarding the dominant frequency of displacement waveforms caused by tilting at steel bridge bearings. [Figure 6] This diagram illustrates the dominant frequency caused by excitation due to the spacing between train cars and wheels on a steel bridge. It shows a schematic diagram of a train (a) and the calculated dominant frequency (b). [Figure 7] This diagram illustrates the waveform of low-frequency acceleration data, showing the case without "scatter" (a) and the case with "scatter" (b). [Figure 8] This is an explanatory diagram regarding the conversion of acceleration data. [Figure 9] This diagram explains the maximum, minimum, and total amplitude values ​​read from the waveforms of acceleration, velocity, and displacement data. [Figure 10] This diagram illustrates the correlation between the amount of tilt and acceleration data, showing the correlation with the total acceleration amplitude (a), the correlation with the maximum acceleration value (b), and the correlation with the minimum acceleration value (c). [Figure 11] This diagram illustrates the correlation between the amount of tilt and velocity data, showing the correlation with the total velocity amplitude (a), the correlation with the maximum velocity (b), and the correlation with the minimum velocity (c). [Figure 12]This diagram illustrates the correlation between the amount of movement and displacement data, showing the correlation with the total displacement amplitude (a), the correlation with the maximum displacement (b), and the correlation with the minimum displacement (c). [Figure 13] This diagram (table (a) and graph (b)) illustrates the correlation of tilt amounts when classified by a span length of 11.7m for steel bridges. [Figure 14] These are explanatory diagrams (a) and (b) showing the correlation between tail speed and train speed. [Figure 15] This is an explanatory diagram of the process of converting the waveform of speed data into an absolute value waveform (a waveform of absolute speed). [Figure 16] This diagram illustrates the correlation between the amount of tilt and the waveform of the speed data, showing the correlation between the total speed amplitude and the train speed (a), the correlation between the maximum speed and the train speed (b), and the correlation between the minimum speed and the train speed (c). [Figure 17] This diagram illustrates the correlation between the amount of tilt and the waveform of speed data, specifically the correlation between the maximum absolute speed and the train speed. [Figure 18] Table (a) and graph (b) illustrate the correlation between the amount of tilt and the waveform of velocity data, specifically the coefficient of determination when classified for a steel bridge with a span length of 11.7m. [Figure 19] This is a flowchart showing the abnormality monitoring method for the steel bridge bearing section of this embodiment. [Figure 20] This is an explanatory diagram of a modified example of an abnormality monitoring system for steel bridge bearings. [Modes for carrying out the invention]

[0026] Hereinafter, embodiments of the bridge girder bearing abnormality monitoring method and bridge girder bearing abnormality monitoring system according to the present invention will be described in detail with reference to the drawings. However, the embodiments described below are subject to various technically preferred limitations for carrying out the present invention, but the scope of the present invention is not limited to the following embodiments and illustrated examples. In this embodiment, the system monitors the steel bridge support section of a railway bridge (steel bridge), which corresponds to the bridge girder support section of the steel bridge. The abnormality monitoring method and system for the steel bridge support section will be described. The abnormality monitoring method for steel bridge bearings in this embodiment is a technique for monitoring abnormalities in steel bridge bearings (bridge girder bearings) using, for example, a steel bridge bearing abnormality monitoring system, and in particular, it uses a quartz crystal vibration acceleration sensor installed on the bearing of the bridge girder of a steel bridge to monitor abnormalities in the bearing. In this embodiment, regarding the vertical rattling of the steel bridge support, which is referred to as "tailing" as an abnormality of the support, the amount of displacement of this rattling (tailing amount) is monitored.

[0027] [Anomaly monitoring system for steel bridge support sections] The abnormality monitoring system 100 for the steel bridge support section of this embodiment includes, for example, an acceleration measuring device 10 installed in the steel bridge support section where the steel bridge 3 is installed on the abutment 1 via the support 2, as shown in Figure 1, and an information management device 20 that can communicate with the acceleration measuring device 10.

[0028] The acceleration measuring device 10 is equipped with a quartz crystal vibration type acceleration sensor 13, which will be described later, and is installed on the upper surface of the planar portion of the steel material 3a that constitutes the bridge girder of the steel bridge 3 in the steel bridge support section, as shown in Figures 2(a) and 2(b). Here, the steel members 3a of the steel bridge 3 (bridge girder) are installed on the abutment 1 via the bearings 2 and sole plates 4, and an acceleration measuring device 10 (quartz vibration type acceleration sensor 13) is installed on the upper surface of the lower flange of the steel members 3a. Furthermore, the installation location of the acceleration measuring device 10 (crystal vibration type acceleration sensor 13) is not limited to the upper surface of the steel material 3a (lower flange); it may also be on the side or bottom, as long as stable installation is possible. This acceleration measuring device 10 (crystal vibration type acceleration sensor 13) measures the acceleration of vibrations when train T passes over the steel bridge 3. Furthermore, the quartz crystal oscillating accelerometer 13 is classified as a frequency-varying accelerometer among various types of accelerometers, and is known to have low noise and high stability among frequency-varying accelerometers. The reason for selecting a quartz crystal accelerometer from among various types of accelerometers will be explained later.

[0029] The acceleration measuring device 10 includes, for example, a control unit (CPU; control means) 11, a communication unit 12, a crystal oscillator type acceleration sensor 13, a memory unit 14, etc., as shown in Figure 3.

[0030] The control unit 11, which is the control unit on the measuring device side, is, for example, a CPU, and executes various processes according to the control program stored in the memory unit 14. The communication unit 12, for example, has an antenna and a communication circuit, and communicates with the information management device 20 under the control of the control unit 11, and transmits data on acceleration at the steel bridge bearing section measured by the crystal oscillator acceleration sensor 13 to the information management device 20. The quartz crystal vibration type acceleration sensor 13 is installed as part of the acceleration measuring device 10 on the upper surface of the planar portion of the steel material 3a that constitutes the steel bridge 3 in the steel bridge support section. This quartz crystal vibration accelerometer 13 measures the acceleration of vibrations in the steel bridge 3 when a train passes over it. The memory unit 14 is composed of, for example, RAM, ROM, non-volatile memory, and a hard disk drive, and stores various control programs executed by the CPU, as well as various fixed data.

[0031] The information management device 20 is composed of a computer having, for example, a control unit (CPU; control means) 21, a communication unit 22, a storage unit 23, a display unit 24, an operation unit 25, etc., as shown in Figure 3.

[0032] The communication unit 22, for example, has an antenna and a communication circuit, and communicates with the acceleration measuring device 10 under the control of the control unit 21, and receives data on acceleration at the steel bridge support from the acceleration measuring device 10. The memory unit 23 is composed of, for example, RAM, ROM, non-volatile memory, and a hard disk drive, and stores various control programs executed by the CPU, as well as various fixed data. The memory unit 23 also stores data on acceleration at the steel bridge bearing section measured by the acceleration measuring device 10 (quartz oscillator type acceleration sensor 13), as well as data obtained by analyzing that acceleration data. The display unit 24 is, for example, a liquid crystal display or an EL display, and displays data related to acceleration measured at the steel bridge support, data obtained by analyzing that acceleration data, and various processing results. The operation unit 25 is, for example, a keyboard, mouse, or touch panel, and can be used to input operations for executing various analysis processes.

[0033] The control unit 21, which is the control unit on the management device side, is, for example, a CPU, and executes various processes according to the control program stored in the memory unit 23. For example, the control unit 21 acquires acceleration data at the steel bridge support received by the communication unit 22, performs a filtering process to extract acceleration data in the low-frequency band, including the train passing frequency, from the acceleration data, and then calculates the amount of tilt of the steel bridge when a train passes, based on the values ​​associated with the acceleration data extracted by the filtering process and a predetermined correlation between the amount of tilt at the support and the values ​​associated with the acceleration data, and then performs a process to determine whether or not there is an abnormality at the support based on the calculated amount of tilt. This decision-making process will be explained later.

[0034] [Regarding the adoption of a quartz crystal accelerometer] Next, we will explain why a quartz crystal accelerometer 13 was selected as an acceleration sensor to measure the acceleration of vibrations of the steel bridge 3 when a train passes over it, in order to monitor for any abnormalities in the steel bridge support structure.

[0035] To verify what type of acceleration sensor is suitable for monitoring abnormalities in a steel bridge support, the inventors installed various acceleration sensors and displacement sensors on the upper surface of the lower flange of the steel material 3a constituting the steel bridge 3 in the steel bridge support. The acceleration of the vibration of the steel bridge 3 during the passage of a train was measured multiple times using the acceleration sensors, and the amount of vertical movement of the steel bridge 3 during the passage of a train was measured multiple times using the displacement sensors. Various acceleration sensors include: • Servo-type acceleration sensor (Model: Rion LS-10C) • Piezoelectric acceleration sensor (Model: Rion PV-87) • Crystal oscillator accelerometer (Model: Epson M-A352AD10) • Capacitive accelerometer (Model: Analog device ADXL355) I used it. Next, we investigated the degree of correlation between the values ​​associated with the acceleration of the steel bridge's vibration measured by each acceleration sensor and the amount of sway in that steel bridge. Here, we determine the values ​​of 12 indicators related to the acceleration of the steel bridge vibration: maximum acceleration, minimum acceleration, total acceleration amplitude, maximum velocity, minimum velocity, total velocity amplitude, maximum displacement, minimum displacement, total displacement amplitude, RMS, equivalent peak, and FFT area. We then examine the correlation between these values ​​and the amount of shock, using the coefficient of determination (R) of the correlation equation. 2 The comparison and verification were conducted based on the following criteria. The results of this comparison and verification are shown in Figure 4.

[0036] As shown in Figure 4, the coefficient of determination (R 2 If we consider an indicator with a value of 0.6 or higher to have a strong correlation, then the results showed a strong correlation for all 12 indicators with the quartz crystal accelerometer. Furthermore, the results showed a strong correlation between servo-type and piezoelectric-type accelerometers for five indicators: minimum acceleration, total acceleration amplitude, RMS, equivalent peak, and FFT area. On the other hand, the capacitive accelerometer showed weak correlations for all 12 indicators. In particular, the coefficient of determination (R) of the quartz crystal accelerometer was measured for all 12 indicators. 2The value was 0.8 or higher. Based on these results, the inventors considered that by measuring the acceleration of vibrations of a steel bridge using a quartz crystal accelerometer, the amount of sway of the steel bridge can be estimated based on the correlation between the value associated with that acceleration and the amount of sway of the steel bridge. Therefore, they decided to adopt a quartz crystal accelerometer.

[0037] [Regarding the necessity of filtering] Next, we will explain why filtering is performed to extract low-frequency acceleration data, including train passing frequencies, from the acceleration data measured by the acceleration sensor (quartz oscillator acceleration sensor 13) when monitoring for abnormalities in the steel bridge bearings. The train passing frequency referred to here is the frequency related to the displacement of the steel bridge 3 that occurs when a train passes over the steel bridge 3, and is the dominant frequency that arises in conjunction with the movement of that train.

[0038] (Dominant frequency of displacement waveform caused by tilting at steel bridge bearings) By measuring the displacement related to the tilt at the steel bridge support using a displacement sensor installed on the upper surface of the lower flange of the steel material 3a constituting the steel bridge 3, and performing a Fourier transform on the obtained displacement waveform, dominant frequencies such as 1.3 Hz, 2.5 Hz, and 3.8 Hz can be identified as a displacement power spectrum, for example, as shown in Figure 5. By measuring the displacement related to the tilt in a similar manner on various steel bridges 3 and performing a Fourier transform on the resulting displacement waveforms, similar displacement power spectra were obtained, and the dominant frequency of the approximate value could be confirmed. Based on these verifications, the inventors concluded that there is a dominant frequency in the low-frequency band of 0.01 Hz to 4.0 Hz, which includes the train passing frequency. This is the first consideration regarding the dominant frequency.

[0039] (Dominant frequency caused by excitation due to the distance between train cars and wheels on a steel bridge) As shown in Figure 6(a), when a train has a length of 20m, a wheel spacing of 13.8m, and a wheel spacing of 6.2m between connected trains, the dominant frequency caused by excitation when this train is traveling at 80km / h was calculated to be between 1.1Hz and 3.6Hz, as shown in Figure 6(b). Furthermore, assuming a maximum train speed of 130 km / h, the dominant frequency caused by excitation when the train is traveling at 130 km / h was calculated to be between 1.8 Hz and 5.8 Hz. Based on these verifications, the inventors concluded that there is a dominant frequency in the low-frequency band of 0.01 Hz to 6.0 Hz, which includes the train passing frequency. This is the second consideration regarding the dominant frequency.

[0040] Thus, based on the considerations (first consideration, second consideration) that the displacement and vibration of the steel bridge 3 have a dominant frequency in the low frequency band, the inventors have found that by filtering and extracting acceleration data in the low frequency band of 6.0 Hz or less, including the train passing frequency, from the measured acceleration data, it becomes possible to suitably obtain data on "swaying" at the steel bridge support section. The filtering process used here involves low-pass filters and band-pass filters.

[0041] (Presence or absence of "scatter" in the low-frequency waveform of acceleration data) As described above, the inventors have found that it is effective to extract low-frequency acceleration data, including the train passing frequency, from the acceleration data of the steel bridge 3 obtained by measurement using the quartz crystal vibration acceleration sensor 13 by filtering. I will now explain the rationale behind the effectiveness of this filtering process. As shown in Figure 7(a), in a steel bridge 3 where no "swaying" occurs, when acceleration data in the low-frequency band, including the train passing frequency, is extracted from the acceleration data of the vibration of the steel bridge 3 measured by the quartz crystal accelerometer 13 by filtering, it can be seen that the amplitude of the waveform of the extracted acceleration data is small. In contrast, as shown in Figure 7(b), when acceleration data in the low-frequency band, including the train passing frequency, is extracted from the acceleration data of the steel bridge 3 vibration measured by the quartz crystal oscillating acceleration sensor 13 in the steel bridge 3, it can be seen that the amplitude of the waveform of the extracted acceleration data is large. Thus, it is clear that information regarding "swaying" can be obtained from the acceleration data of the vibration of the steel bridge 3, and that information regarding "swaying" can be suitably obtained from the low-frequency band acceleration data, including the train passing frequency, extracted by filtering the acceleration data of the vibration of the steel bridge 3 measured by the quartz oscillator acceleration sensor 13.

[0042] [Acceleration Heat Conversion] Next, we will explain the transformation of acceleration data performed to obtain data related to the "sideways movement" in the steel bridge support section, which is linked to the acceleration of the vibration of the steel bridge 3. As shown in Figure 8, the acceleration data (original) of the vibration of the steel bridge 3 measured by the quartz crystal accelerometer 13 is subjected to the filtering process described above to extract acceleration data in the low-frequency band of 6.0 Hz or less, including the train passing frequency (filtering process). The waveform of the acceleration data extracted by this filtering process (waveform in the upper right of the figure) is obtained (acceleration waveform acquisition process). Furthermore, as shown in Figure 8, the acquired acceleration data is integrated to convert it into velocity data (data conversion step), and the waveform of that velocity data (the waveform in the lower left of the figure) is obtained (velocity waveform acquisition step). Furthermore, as shown in Figure 8, the acquired velocity data is integrated to convert it into displacement data, and the waveform of that displacement data (the waveform in the lower right of the figure) is obtained (displacement waveform acquisition process). From the waveforms obtained in this way, the maximum value, minimum value, and total amplitude can be determined, as shown in Figure 9. For example, from the waveform of acceleration data, it is possible to determine the total amplitude value of the acceleration data, the maximum value of the acceleration data, and the minimum value of the acceleration data. Furthermore, the total amplitude value, maximum value, and minimum value of the velocity data can be determined from the waveform of the velocity data. Furthermore, the total amplitude value of the displacement data, the maximum value of the displacement data, and the minimum value of the displacement data can be determined from the waveform of the displacement data.

[0043] [Correlation between the amount of tilt and values ​​associated with acceleration data] Next, we will explain the correlation between the amount of tilt in the steel bridge bearing and the values ​​associated with acceleration data. As explained in the section above titled "Regarding the Adoption of Quartz Crystal Vibration Acceleration Sensors," we determine the correlation between the amount of tilt at the steel bridge support and the value associated with the acceleration data. Specifically, a quartz crystal vibration acceleration sensor 13 and a displacement sensor are installed on the upper surface of the lower flange of the steel member 3a that constitutes the steel bridge 3 at the steel bridge support section. The quartz crystal vibration acceleration sensor 13 measures the acceleration of the vibration of the steel bridge 3 multiple times when a train passes, and the displacement sensor measures the amount of vertical movement of the steel bridge 3 when a train passes multiple times. Then, as explained in the "Conversion of Acceleration Data" section above, the acceleration data obtained by the quartz crystal accelerometer 13 is converted, "Total amplitude value of acceleration data", "Maximum value of acceleration data", "Minimum value of acceleration data", "Total amplitude value of velocity data", "Maximum value of velocity data", "Minimum value of velocity data", Nine values ​​were determined: "total amplitude of displacement data," "maximum value of displacement data," and "minimum value of displacement data." The "amount of tilt" measured by the displacement sensor was then associated with each of these nine values, and the correlation between each value and the "amount of tilt" was calculated. Here, the coefficient of determination (R) of the correlation equation was calculated. 2 The correlation between the above values ​​and the amount of sway was calculated as shown in the evaluation. Furthermore, in order to establish a correlation, it is naturally preferable to have a large number of measurements taken for the acceleration of vibration of steel bridge 3 and the amount of tilt of steel bridge 3 when a train passes over it. The correlation between the amount of tilt and acceleration data obtained from numerous measurements is shown in Figure 10, the correlation between the amount of tilt and velocity data is shown in Figure 11, and the correlation between the amount of tilt and displacement data is shown in Figure 12. By pre-determining such correlations (see, for example, Figures 10, 11, and 12), it becomes possible to estimate the amount of tilt of the steel bridge 3 based on the acceleration of the vibration of the steel bridge 3 measured by the quartz crystal accelerometer 13 when a train passes over it. In this embodiment, the predetermined correlations (correlation between tilt amount and acceleration data, correlation between tilt amount and velocity data, correlation between tilt amount and displacement data) are stored, for example, in the storage unit 23 of the information management device 20.

[0044] [Span length of steel bridges] Next, we will explain the correlation between the amount of tilt and the values ​​associated with acceleration data, classifying them based on the span length of steel bridge 3, which is 11.7m. In this embodiment, we assume the operation of a train in which the distance between the inner wheels on the front and rear bogies of the vehicle is 11.7m, as shown in Figure 6(a). Therefore, if the span length of the steel bridge 3 is less than 11.7m, when a train passes over the steel bridge 3, the wheels on both the front and rear of the train will not be on the steel bridge 3. On the other hand, if the span length of the steel bridge 3 is 11.7m or more, then when a train passes over the steel bridge 3, at least one of the wheels on both the front and rear sides of the train will always be on the steel bridge 3. In other words, if the span length of steel bridge 3 is less than 11.7m, there will be times when no wheel load acts on steel bridge 3 when a train passes over it, and if the span length of steel bridge 3 is 11.7m or more, wheel load will always act on steel bridge 3 when a train passes over it. As such, there are differences in the wheel load acting on the steel bridge 3 depending on the span length of the steel bridge 3. Therefore, the inventors have found that a stronger correlation can be obtained regarding the correlation of the amount of tilt by classifying it according to the span length of the steel bridge 3. Specifically, as shown in Figures 13(a) and 13(b), the coefficient of determination is generally higher and the correlation is stronger when the bridges are classified by their span length of 11.7m compared to when they are not classified (all bridges). Thus, it is preferable to classify and define the correlation regarding the amount of tilt based on whether the span length of the steel bridge 3 is less than the distance between the front and rear wheels of the train, or whether the span length of the steel bridge 3 is greater than or equal to the distance between the front and rear wheels of the train, and to determine whether or not there is an abnormality in the steel bridge support based on one of these correlations, depending on the span length of the steel bridge 3 on which the crystal vibration type acceleration sensor 13 (acceleration measuring device 10) is installed.

[0045] [Method for monitoring abnormalities in steel bridge support structures] Next, a method for monitoring abnormalities in the steel bridge bearing section using the abnormality monitoring system 100 of this embodiment will be described. As mentioned above, the steel bridge bearing abnormality monitoring system 100 includes an acceleration measuring device 10 equipped with a quartz crystal accelerometer 13 for measuring the acceleration of vibrations of the steel bridge 3 when a train passes over it, and an information management device 20 that can communicate with the acceleration measuring device 10. Furthermore, this abnormality monitoring system 100 includes control means (measuring device side control unit 11, management device side control unit 21) that perform notification regarding abnormalities in the steel bridge bearing section based on acceleration data measured and acquired by the quartz oscillator type acceleration sensor 13.

[0046] Here, we will explain the decision-making process performed by the control unit 21, which acts as the control unit on the management device side of the information management device 20 mentioned above. For example, under the control of the measurement device side control unit 11 of the acceleration measurement device 10, acceleration data measured by the quartz oscillator type acceleration sensor 13 at the steel bridge support is transmitted to the information management device 20. Then, the control unit 21 on the management device side performs a filtering process to extract low-frequency acceleration data, including the train passing frequency, from the acceleration data measured and acquired by the crystal oscillator type acceleration sensor 13 (filtering process). Furthermore, the control unit 21 on the management device side estimates and determines the amount of tilt of the steel bridge 3 when a train passes, based on the values ​​associated with the acceleration data extracted by filtering and the predetermined correlation between the amount of tilt at the support section and the values ​​associated with the acceleration data.

[0047] The values ​​associated with acceleration data here are values ​​related to the waveform of the acceleration data, which can be the total amplitude value of the acceleration data, the maximum value of the acceleration data, or the minimum value of the acceleration data; or values ​​related to the waveform of the velocity data, which can be the total amplitude value of the velocity data, the maximum value of the velocity data, or the minimum value of the velocity data; or values ​​related to the waveform of the displacement data, which can be the total amplitude value of the displacement data, the maximum value of the displacement data, or the minimum value of the displacement data. These nine values ​​associated with acceleration data can be obtained as explained in "Acceleration Data Conversion" above. (For example, values ​​associated with acceleration data can be obtained through processes such as acceleration waveform acquisition, velocity waveform acquisition, data conversion, and displacement waveform acquisition. See Figures 8 and 9.) Furthermore, predetermined correlations (correlation between tilt amount and acceleration data (Figure 10), correlation between tilt amount and velocity data (Figure 11), correlation between tilt amount and displacement data (Figure 12)) are stored in the storage unit 23 of the information management device 20.

[0048] Then, based on a predetermined correlation between one of the nine acceleration data points and the corresponding value, the amount of tilt of the steel bridge 3 when a train passes is estimated. The amount of tilt can be estimated based on the correlation shown in Figure 10(a) for the total amplitude of the acceleration data waveform, the amount of tilt can be estimated based on the correlation shown in Figure 10(b) for the maximum value of the acceleration data, and the amount of tilt can be estimated based on the correlation shown in Figure 10(c) for the minimum value of the acceleration data. Furthermore, the amount of tilt can be estimated based on the correlation shown in Figure 11(a) for the total amplitude value of the velocity data waveform, the amount of tilt can be estimated based on the correlation shown in Figure 11(b) for the maximum value of the velocity data, and the amount of tilt can be estimated based on the correlation shown in Figure 11(c) for the minimum value of the velocity data. Furthermore, the amount of tilt can be estimated based on the correlation shown in Figure 12(a) for the total amplitude of the displacement data waveform, the amount of tilt can be estimated based on the correlation shown in Figure 12(b) for the maximum value of the displacement data, and the amount of tilt can be estimated based on the correlation shown in Figure 12(c) for the minimum value of the displacement data. Furthermore, as explained above in the section on "Span Length of Steel Bridges," estimating the amount of tilt based on a correlation with the span length makes it possible to estimate the amount of tilt more accurately.

[0049] Then, the control unit 21 on the management device side performs a process to determine whether or not there is an abnormality in the support section based on the amount of tilt estimated based on a predetermined correlation (determination step). For example, the control unit 21 on the management device side determines that there is an abnormality in the support section if the estimated amount of tilt exceeds a predetermined threshold (if the amount of tilt is greater than the standard value), and determines that there is no abnormality in the support section if the estimated amount of tilt is less than or equal to the predetermined threshold (if the amount of tilt is slight). The result of this decision is notified to the manager of the steel bridge 3, for example, by displaying it on the display unit 24 of the information management device 20. Furthermore, as will be described later, the acceleration measuring device 10 (measuring device side control unit 11) of the abnormality monitoring system 100 can also perform a process to determine whether or not there is an abnormality in the support section based on the amount of tilt estimated based on a predetermined correlation.

[0050] [Improvement of abnormality monitoring technology for steel bridge support structures] Having found that the amount of tilt of the steel bridge 3 when a train passes can be appropriately estimated based on a predetermined correlation between any of the nine acceleration data values ​​mentioned above, the inventors then challenged themselves to develop a technology that can estimate the amount of tilt even more accurately.

[0051] Figure 14(a) shows the measured amount of tilt of the steel bridge 3 when a train passes over it, and Figure 14(b) shows a magnified explanatory diagram of that portion. As shown in Fig. 14(b), if the amount of sway (displacement) of the steel bridge 3 occurring during the minute time Δt when the train passes is denoted as δ, since the speed (V アオリ ) of the sway by which the steel bridge 3 is displaced is δ / Δt (displacement amount / time), Equation (1) holds. δ = V アオリ ×Δt ···(1) Also, since the minute time Δt when the train passes is inversely proportional to the train speed (V 列車 ), Equation (2) holds. Δt = K / V 列車 (K: coefficient) ···(2) And Equation (3) is obtained from these Equation (1) and Equation (2). δ = K×(V アオリ / V 列車 ) ···(3) Based on this Equation (3), it is considered that the amount of sway δ is proportional to (V アオリ / V 列車 ). That is, it is inferred that the amount of sway δ has a higher correlation with "sway speed / train speed".

[0052] And, as values related to the sway speed (V アオリ ), among the information related to "sway" obtained from the acceleration data of the vibration of the steel bridge 3, since three values, namely the value of the total amplitude of the speed data, the maximum value of the speed data, and the minimum value of the speed data, which are obtained from the waveform of the speed data acquired by integrating the acceleration data, were known, the inventors inferred that if the values related to the waveform of the speed data (the value of the total amplitude of the speed data, the maximum value of the speed data, and the minimum value of the speed data) are divided by the train speed when the train passes, they have a high correlation with the amount of sway. Based on the above, the inventors hypothesized that it would be possible to more accurately determine whether or not there is an abnormality in the support section based on a predetermined correlation between the value obtained by dividing the waveform values ​​of the speed data (total amplitude value of the speed data, maximum value of the speed data, minimum value of the speed data) by the train speed at the time of train passage, and the value obtained by dividing the amount of tilt at the support section by the waveform values ​​of the speed data by the train speed, and then conducted verification of this hypothesis. In other words, here, the values ​​associated with the waveform of the velocity data are used as the values ​​linked to the acceleration data.

[0053] Furthermore, the inventors hypothesized that, for example, as shown in Figure 15, if the waveform of velocity data obtained by integrating acceleration data is converted into an absolute value waveform, and the maximum value of the absolute velocity obtained from that absolute value waveform is divided by the train speed at the time of the train's passage, then the resulting value would have a high correlation with the amount of tilt. We then hypothesized that it would be possible to more accurately determine whether or not there was an abnormality in the support section based on a predetermined correlation between the value obtained by dividing the maximum absolute value of the speed (a value related to the waveform of the speed data) by the train speed at the time of the train's passage, and the value obtained by dividing the amount of tilt at the support section by the value related to the waveform of the speed data by the train speed. We then conducted a verification of this hypothesis. Here too, the value associated with the acceleration data is the value related to the waveform of the velocity data (the maximum value of the absolute velocity).

[0054] Specifically, a quartz crystal vibration acceleration sensor 13 and a displacement sensor are installed on the upper surface of the lower flange of the steel member 3a that constitutes the steel bridge 3 at the steel bridge support section. The quartz crystal vibration acceleration sensor 13 measures the acceleration of the vibration of the steel bridge 3 multiple times when a train passes, and the displacement sensor measures the amount of vertical movement of the steel bridge 3 when a train passes multiple times. Furthermore, the train speed at the time of tilt measurement (train speed when the train passes) is also obtained by measurement or other means. The train speed when the train passes here may be obtained by measurement using a Doppler sensor or the like, or the train speed (V) can be obtained from the time (T) it takes for the train to pass over a bridge 3 of a predetermined length (L). 列車It is also acceptable to obtain the result of (=L / T). Then, as explained in the "Conversion of Acceleration Data" section above, the waveform of the velocity data obtained by converting the acceleration data measured by the crystal oscillator accelerometer 13 is used to determine the "total amplitude value of the velocity data," the "maximum value of the velocity data," and the "minimum value of the velocity data." These values ​​are then divided by the train speed at the time of the train's passage to obtain three values: "total amplitude value of the velocity data / train speed" (total velocity amplitude / train speed), "maximum value of the velocity data / train speed" (maximum velocity / train speed), and "minimum value of the velocity data / train speed" (minimum velocity / train speed). Additionally, the "maximum absolute value of the velocity" is determined from the absolute value waveform obtained from the velocity data waveform, and this value is divided by the train speed at the time of the train's passage to obtain the value of "maximum absolute value of the velocity / train speed" (maximum absolute value of the velocity / train speed). The "sway amount" measured by the displacement sensor was associated with each of these four values, and the correlation between each value and the "sway amount" was calculated. Here, the coefficient of determination (R) of the correlation equation was calculated. 2 The correlation between the above values ​​and the amount of sway was calculated as shown in the evaluation. Furthermore, in order to establish a correlation, it is naturally preferable to have a large number of measurements taken for the acceleration of vibration of steel bridge 3 and the amount of tilt of steel bridge 3 when a train passes over it. The correlation between the amount of tilt obtained from these numerous measurements and the values ​​obtained by dividing the waveform values ​​of the speed data (total speed amplitude, maximum speed, minimum speed, maximum absolute speed) by the train speed at the time of passing (total speed amplitude / train speed, maximum speed / train speed, minimum speed / train speed, maximum absolute speed / train speed) is shown in Figures 16 and 17. The correlation between the amount of tilt at the support and "total velocity amplitude / train speed" is shown in Figure 16(a), the correlation between the amount of tilt at the support and "maximum velocity / train speed" is shown in Figure 16(b), and the correlation between the amount of tilt at the support and "minimum velocity / train speed" is shown in Figure 16(c). The correlation between the amount of tilt at the support and "maximum absolute velocity / train speed" is shown in Figure 17. By pre-determining such correlations (see, for example, Figures 16 and 17), it becomes possible to estimate the amount of tilt of the steel bridge 3 based on the acceleration of the vibration of the steel bridge 3 measured by the quartz crystal accelerometer 13 when a train passes over it. In this embodiment, the predetermined correlations (see Figures 16 and 17) are stored, for example, in the storage unit 23 of the information management device 20.

[0055] [Method for monitoring abnormalities in steel bridge bearings; Part 2] Next, we will explain an abnormality monitoring method for estimating the amount of tilt of the steel bridge 3 when a train passes, based on the predetermined correlation shown in Figures 16 and 17 (correlation between the amount of tilt at the support section and the value obtained by dividing the waveform value of the speed data by the train speed). This explanation will follow the same format as the previously mentioned "Abnormality Monitoring Method for Steel Bridge Support Sections". The steel bridge bearing abnormality monitoring system 100 includes an acceleration measuring device 10 equipped with a quartz crystal accelerometer 13 for measuring the acceleration of vibrations of the steel bridge 3 when a train passes over it, and an information management device 20 that can communicate with the acceleration measuring device 10. For example, under the control of the measurement device side control unit 11 of the acceleration measurement device 10, acceleration data measured by the quartz oscillator type acceleration sensor 13 at the steel bridge support is transmitted to the information management device 20. Then, the control unit 21 on the management device side of the information management device 20 performs a filtering process to extract low-frequency acceleration data, including the train passing frequency, from the acceleration data measured and acquired by the crystal oscillator type acceleration sensor 13 (filtering process). Furthermore, the control unit 21 on the management device side integrates the acceleration data extracted by the filtering process to convert it into velocity data, obtains the waveform of that velocity data (velocity waveform acquisition step), and obtains values ​​obtained by dividing the values ​​related to the acquired velocity data waveform by the train speed at the time the train passes (total velocity amplitude / train speed, maximum velocity / train speed, minimum velocity / train speed, maximum absolute velocity / train speed). Then, the amount of tilt of the steel bridge 3 when a train passes is estimated and determined based on the values ​​obtained by dividing the waveform values ​​of the speed data by the train speed at the time of train passage (total speed amplitude / train speed, maximum speed / train speed, minimum speed / train speed, maximum absolute speed / train speed) and the correlation shown in Figures 16 and 17 (a predetermined correlation between the amount of tilt at the support section and the values ​​obtained by dividing the waveform values ​​of the speed data by the train speed). The predetermined correlations (shown in Figures 16 and 17) are stored in the storage unit 23 of the information management device 20.

[0056] Then, the amount of tilt of the steel bridge 3 when a train passes is estimated based on a predetermined correlation between one of the following: the total amplitude of speed / train speed, the maximum speed / train speed, the minimum speed / train speed, or the maximum absolute speed / train speed. The value of total velocity amplitude / train speed can be estimated based on the correlation shown in Figure 16(a). Regarding the value of maximum speed / train speed, the amount of tilt can be estimated based on the correlation shown in Figure 16(b). Regarding the minimum speed / train speed values, the amount of tilt can be estimated based on the correlation shown in Figure 16(c). Regarding the value of the maximum absolute speed divided by the train speed, the amount of sway can be estimated based on the correlation shown in Figure 17.

[0057] Furthermore, as explained above in the section on "Span Length of Steel Bridges," estimating the amount of tilt based on a correlation with the span length makes it possible to estimate the amount of tilt more accurately. For example, as shown in Figures 18(a) and 18(b), the coefficient of determination is generally higher and the correlation is stronger when the bridges are classified by their span length of 11.7m compared to when they are not classified (all bridges).

[0058] Then, the control unit 21 on the management device side performs a process to determine whether or not an abnormality has occurred in the support part, based on the amount of tilt estimated based on a predetermined correlation (determination step). For example, the control unit 21 on the management device side determines that there is an abnormality in the support section if the estimated amount of tilt exceeds a predetermined threshold (if the amount of tilt is greater than the standard value), and determines that there is no abnormality in the support section if the estimated amount of tilt is less than or equal to the predetermined threshold (if the amount of tilt is slight). The result of this decision is notified to the manager of the steel bridge 3, for example, by displaying it on the display unit 24 of the information management device 20. Furthermore, as will be described later, the acceleration measuring device 10 (measuring device side control unit 11) of the abnormality monitoring system 100 can also perform a process to determine whether or not there is an abnormality in the support section based on the amount of tilt estimated based on a predetermined correlation.

[0059] [First method of anomaly monitoring] Next, the abnormality monitoring method using the steel bridge bearing abnormality monitoring system 100 will be explained based on the flowchart shown in Figure 19.

[0060] First, the quartz crystal oscillating acceleration sensor 13 of the acceleration measuring device 10 installed on the steel bridge 3 measures the acceleration of the vibration of the steel bridge 3 when a train passes over it (Step S1; acceleration measurement process). The acceleration data measured and acquired by the quartz crystal oscillating acceleration sensor 13 is transmitted to the information management device 20 by the measurement device side control unit 11 through a transmission process.

[0061] Next, the control unit 21 on the management device side of the information management device 20 performs a filtering process (filtering process) to extract low-frequency acceleration data including the train passing frequency from the acceleration data transmitted from the acceleration measuring device 10, a process (speed waveform acquisition process) to integrate the acceleration data extracted by the filtering process to convert it into speed data and acquire the waveform of that speed data, a process to acquire values ​​related to the waveform of the speed data (total speed amplitude, maximum speed, minimum speed, maximum absolute speed) from the acquired speed data waveform, and a process to calculate the values ​​related to the acquired speed data waveform at the train speed when the train passes. The process of obtaining the divided values ​​(total velocity amplitude / train speed, maximum velocity / train speed, minimum velocity / train speed, maximum absolute velocity / train speed) is executed, and the amount of tilt of the steel bridge 3 when the train passes is determined based on the values ​​obtained by dividing the values ​​related to the waveform of the velocity data by the train speed at the time of the train's passage (total velocity amplitude / train speed, maximum velocity / train speed, minimum velocity / train speed, maximum absolute velocity / train speed) and the correlation shown in Figures 16 and 17 (a predetermined correlation between the amount of tilt at the support section and the values ​​obtained by dividing the values ​​related to the waveform of the velocity data by the train speed) (Step S2). For example, the value of total velocity amplitude / train speed is obtained, and the amount of tilt is estimated based on the correlation shown in Figure 16(a).

[0062] Next, the control unit 21 on the management device side determines whether the determined amount of tilt is below a predetermined threshold (step S3; determination step). If the control unit 21 on the management device side determines that the amount of tilt is below a predetermined threshold (step S3; Yes), it determines that there is no abnormality in the steel bridge support section and returns to step S1. On the other hand, if it is determined that the amount of tilt is not below a predetermined threshold (i.e., it exceeds the threshold) (Step S3; No), it is determined that there is an abnormality in the steel bridge support, and the administrator of the steel bridge 3 is notified by displaying a message indicating the occurrence of an abnormality on the display unit 24 of the information management device 20 (Step S4), and the process returns to Step S1.

[0063] Thus, with the steel bridge bearing abnormality monitoring system 100 (steel bridge bearing abnormality monitoring method) of this embodiment, the amount of tilt in the steel bridge 3 can be estimated by measuring the acceleration of the vibration of the steel bridge 3 using a quartz vibration acceleration sensor 13, and based on the estimated amount of tilt, it is possible to determine whether or not there is an abnormality in the steel bridge bearing and report it. In other words, the steel bridge bearing abnormality monitoring system 100 (steel bridge bearing abnormality monitoring method) of this embodiment can monitor whether or not there is an abnormality in the bearing of the steel bridge 3 by measuring the acceleration of vibration of the steel bridge 3 using a quartz vibration acceleration sensor 13.

[0064] [Second method of anomaly monitoring] Furthermore, the abnormality monitoring method using the abnormality monitoring system 100 of the present invention (Figure 19) is not limited to the above embodiment. For example, this could be a method for monitoring abnormalities in a steel bridge support, in which the measurement device side control unit 11 performs some of the processing performed by the management device side control unit 21 described above. Specifically, first, the quartz crystal oscillating acceleration sensor 13 of the acceleration measuring device 10 installed on the steel bridge 3 measures the acceleration of the vibration of the steel bridge 3 when a train passes over it (Step S1; acceleration measurement process).

[0065] Next, the measurement device side control unit 11 of the acceleration measurement device 10 performs a filtering process (filtering process) to extract low-frequency acceleration data including the train passing frequency from the acceleration data measured and acquired by the crystal oscillator type acceleration sensor 13, integrates the acceleration data extracted by the filtering process to convert it into velocity data and acquires the waveform of that velocity data (velocity waveform acquisition process), acquires values ​​related to the velocity data waveform (total velocity amplitude, maximum velocity, minimum velocity, maximum absolute velocity) from the acquired velocity data waveform, and calculates the values ​​related to the acquired velocity data waveform at the time of train passage. The process of obtaining values ​​obtained by dividing by the train speed (total amplitude of speed / train speed, maximum speed / train speed, minimum speed / train speed, maximum absolute speed / train speed) is executed, and the amount of tilt of the steel bridge 3 when the train passes is determined based on the values ​​obtained by dividing the values ​​related to the waveform of the speed data by the train speed at the time of the train's passage (total amplitude of speed / train speed, maximum speed / train speed, minimum speed / train speed, maximum absolute speed / train speed) and the correlation shown in Figures 16 and 17 (a predetermined correlation between the amount of tilt at the support section and the values ​​obtained by dividing the values ​​related to the waveform of the speed data by the train speed) (Step S2). For example, the value of total velocity amplitude / train speed is obtained, and the amount of tilt is estimated based on the correlation shown in Figure 16(a). The data regarding the amount of tilt obtained here is transmitted to the information management device 20 by the transmission process performed by the measurement device control unit 11.

[0066] Next, the control unit 21 on the management device side of the information management device 20 determines whether the amount of tilt is below a predetermined threshold based on the data regarding the amount of tilt transmitted from the acceleration measuring device 10 (step S3; determination step). If the control unit 21 on the management device side determines that the amount of tilt is below a predetermined threshold (step S3; Yes), it determines that there is no abnormality in the steel bridge support section and returns to step S1. On the other hand, if it is determined that the amount of tilt is not below a predetermined threshold (i.e., it exceeds the threshold) (Step S3; No), it is determined that there is an abnormality in the steel bridge support, and the administrator of the steel bridge 3 is notified by displaying a message indicating the occurrence of an abnormality on the display unit 24 of the information management device 20 (Step S4), and the process returns to Step S1.

[0067] Even with such a steel bridge bearing abnormality monitoring system 100 (steel bridge bearing abnormality monitoring method), the acceleration of vibration of the steel bridge 3 can be measured by the crystal vibration acceleration sensor 13, and the presence or absence of abnormalities in the bearing of the steel bridge 3 can be monitored.

[0068] [Third method of anomaly monitoring] Furthermore, the abnormality monitoring method using the abnormality monitoring system 100 of the present invention (Figure 19) is not limited to the above embodiment. For example, this could be a method for monitoring abnormalities in a steel bridge support, in which the measurement device side control unit 11 performs some of the processing performed by the management device side control unit 21 described above. Specifically, first, the quartz crystal oscillating acceleration sensor 13 of the acceleration measuring device 10 installed on the steel bridge 3 measures the acceleration of the vibration of the steel bridge 3 when a train passes over it (Step S1; acceleration measurement process).

[0069] Next, the measurement device side control unit 11 of the acceleration measurement device 10 performs a filtering process (filtering process) to extract low-frequency acceleration data including the train passing frequency from the acceleration data measured and acquired by the crystal oscillator type acceleration sensor 13, integrates the acceleration data extracted by the filtering process to convert it into velocity data and acquires the waveform of that velocity data (velocity waveform acquisition process), acquires values ​​related to the velocity data waveform (total velocity amplitude, maximum velocity, minimum velocity, maximum absolute velocity) from the acquired velocity data waveform, and calculates the values ​​related to the acquired velocity data waveform at the time of train passage. The process of obtaining values ​​obtained by dividing by the train speed (total amplitude of speed / train speed, maximum speed / train speed, minimum speed / train speed, maximum absolute speed / train speed) is executed, and the amount of tilt of the steel bridge 3 when the train passes is determined based on the values ​​obtained by dividing the values ​​related to the waveform of the speed data by the train speed at the time of the train's passage (total amplitude of speed / train speed, maximum speed / train speed, minimum speed / train speed, maximum absolute speed / train speed) and the correlation shown in Figures 16 and 17 (a predetermined correlation between the amount of tilt at the support section and the values ​​obtained by dividing the values ​​related to the waveform of the speed data by the train speed) (Step S2). For example, the value of total velocity amplitude / train speed is obtained, and the amount of tilt is estimated based on the correlation shown in Figure 16(a). Furthermore, the control unit 11 on the measuring device side determines whether the determined amount of tilt is below a predetermined threshold (step S3; determination step). If the control unit 11 on the measuring device side determines that the amount of tilt is below a predetermined threshold (step S3; Yes), it determines that there is no abnormality in the steel bridge support section and returns to step S1. On the other hand, if the measuring device control unit 11 determines that the amount of tilt is not below a predetermined threshold (i.e., it is a value exceeding the threshold) (Step S3; No), it determines that there is an abnormality in the steel bridge support, and the result of this determination is transmitted to the information management device 20 by a transmission process performed by the measuring device control unit 11.

[0070] Next, the control unit 21 on the management device side of the information management device 20 notifies the administrator of the steel bridge 3, etc., by displaying a message indicating the occurrence of an abnormality on the display unit 24 of the information management device 20, based on the judgment result transmitted from the acceleration measuring device 10 (step S4), and then returns to step S1.

[0071] Even with such a steel bridge bearing abnormality monitoring system 100 (steel bridge bearing abnormality monitoring method), the acceleration of vibration of the steel bridge 3 can be measured by the crystal vibration acceleration sensor 13, and the presence or absence of abnormalities in the bearing of the steel bridge 3 can be monitored.

[0072] [Fourth method of anomaly monitoring] Furthermore, the abnormality monitoring method using the abnormality monitoring system 100 of the present invention (Figure 19) is not limited to the above embodiment. For example, this could be a method for monitoring abnormalities in a steel bridge support, in which the measurement device side control unit 11 performs some of the processing performed by the management device side control unit 21 described above. Specifically, first, the quartz crystal oscillating acceleration sensor 13 of the acceleration measuring device 10 installed on the steel bridge 3 measures the acceleration of the vibration of the steel bridge 3 when a train passes over it (Step S1; acceleration measurement process).

[0073] Next, the measurement device side control unit 11 of the acceleration measurement device 10 performs the following processes: a filtering process (filtering process) to extract acceleration data in the low-frequency band, including the train passing frequency, from the acceleration data measured and acquired by the crystal oscillator type acceleration sensor 13; a process (velocity waveform acquisition process) to integrate the acceleration data extracted by the filtering process and convert it into velocity data, and to acquire the waveform of that velocity data; and a process to acquire values ​​related to the waveform of the velocity data (total velocity amplitude, maximum velocity, minimum velocity, and maximum absolute velocity) from the acquired velocity data waveform. The waveform values ​​of the acquired velocity data (total velocity amplitude, maximum velocity, minimum velocity, and maximum absolute velocity) are transmitted to the information management device 20 by the transmission process performed by the measurement device control unit 11.

[0074] Next, the control unit 21 on the management device side of the information management device 20 performs a process to obtain values ​​obtained by dividing the waveform values ​​of the speed data transmitted from the acceleration measuring device 10 by the train speed at the time of train passage (total speed amplitude / train speed, maximum speed / train speed, minimum speed / train speed, maximum absolute speed / train speed). Based on the values ​​obtained by dividing the waveform values ​​of the speed data by the train speed at the time of train passage (total speed amplitude / train speed, maximum speed / train speed, minimum speed / train speed, maximum absolute speed / train speed) and the correlation shown in Figures 16 and 17 (a predetermined correlation between the amount of tilt at the support section and the value obtained by dividing the waveform values ​​of the speed data by the train speed), the amount of tilt of the steel bridge 3 at the time of train passage is determined (step S2). For example, the value of total velocity amplitude / train speed is obtained, and the amount of tilt is estimated based on the correlation shown in Figure 16(a). Next, the control unit 21 on the management device side of the information management device 20 determines whether the amount of sway obtained is below a predetermined threshold based on the data relating to the amount of sway (step S3; determination step). If the control unit 21 on the management device side determines that the amount of tilt is below a predetermined threshold (step S3; Yes), it determines that there is no abnormality in the steel bridge support section and returns to step S1. On the other hand, if it is determined that the amount of tilt is not below a predetermined threshold (i.e., it exceeds the threshold) (Step S3; No), it is determined that there is an abnormality in the steel bridge support, and the administrator of the steel bridge 3 is notified by displaying a message indicating the occurrence of an abnormality on the display unit 24 of the information management device 20 (Step S4), and the process returns to Step S1.

[0075] Even with such a steel bridge bearing abnormality monitoring system 100 (steel bridge bearing abnormality monitoring method), the acceleration of vibration of the steel bridge 3 can be measured by the crystal vibration acceleration sensor 13, and the presence or absence of abnormalities in the bearing of the steel bridge 3 can be monitored.

[0076] As described above, with the steel bridge bearing abnormality monitoring system 100 (steel bridge bearing abnormality monitoring method) of this embodiment, the amount of tilt of the steel bridge 3 can be estimated by measuring the acceleration of the vibration of the steel bridge 3 using the quartz vibration acceleration sensor 13, and the presence or absence of abnormalities in the steel bridge bearing can be monitored based on the estimated amount of tilt.

[0077] In the above embodiments, the steel bridge bearing abnormality monitoring system 100 is described as comprising an acceleration measuring device 10 and an information management device 20 capable of communicating with the acceleration measuring device 10. However, the present invention is not limited thereto, and for example, the processing in the information management device 20 may be performed on a server or in the cloud. Specifically, as shown in Figure 20, the steel bridge bearing abnormality monitoring system 100 may include an acceleration measuring device 10 installed at the steel bridge bearing where the steel bridge 3 is installed on the abutment 1 via the bearing 2, a cloud server 20a capable of communicating with the acceleration measuring device 10, and a terminal device 20b capable of communicating with the cloud server 20a. Of course, the acceleration measuring device 10 is equipped with a crystal oscillator type acceleration sensor 13 that measures the acceleration of vibrations of the steel bridge 3 when a train passes over it. Then, the processing by the management device side control unit 21 of the information management device 20 (steps S2 and S3), as described in the "first abnormality monitoring method" above, can be performed on the cloud server 20a, and the notification process of the determination that an abnormality has occurred in the steel bridge bearing (step S4) can be performed on a terminal device 20b such as a tablet. In other words, here the cloud server 20a and the terminal device 20b function as the information management device 20. This type of abnormality monitoring system 100 for steel bridge bearings can also monitor for abnormalities in the steel bridge bearings. Of course, the monitoring may be configured such that the cloud server 20a or terminal device 20b handles a part of the processing in the aforementioned "second anomaly monitoring method," "third anomaly monitoring method," and "fourth anomaly monitoring method."

[0078] Furthermore, in the above-described explanation of the abnormality monitoring method for steel bridge bearings, the example given was the case where the value of total velocity amplitude / train speed is obtained and the amount of tilt is estimated based on the correlation shown in Figure 16(a) (correlation between tilt amount and the value of total velocity amplitude / train speed). However, the present invention is not limited to this, and the value of maximum velocity / train speed may be obtained and the amount of tilt may be estimated based on the correlation shown in Figure 16(b) (correlation between tilt amount and the value of maximum velocity / train speed), or the value of minimum velocity / train speed may be obtained and the amount of tilt may be estimated based on the correlation shown in Figure 16(c) (correlation between tilt amount and the value of minimum velocity / train speed), or the value of maximum absolute velocity / train speed may be obtained and the amount of tilt may be estimated based on the correlation shown in Figure 17 (correlation between tilt amount and the value of maximum absolute velocity / train speed).

[0079] Furthermore, it goes without saying that other specific structural details can be modified as needed. [Explanation of Symbols]

[0080] 1. Abutment 2 Bearing 3. Steel bridge (bridge girder) 3a steel material 4. Sole Plate 10. Acceleration Measurement Device 11 Control Unit (Measurement device side control unit, control means) 12 Communications Department 13. Crystal oscillator accelerometer 14 Storage section 20 Information management device 21 Control Unit (Control device side control unit, control means) 22 Communications Department 23 Memory section 24 Display section 25 Control section 20a Cloud Server 20b Terminal device 100 Anomaly monitoring system for bridge girder support sections T train

Claims

1. A method for monitoring abnormalities in a bridge girder support, which uses a quartz crystal vibration acceleration sensor installed in the support of the bridge girder to monitor abnormalities in the support, The acceleration measurement step involves using the aforementioned quartz crystal vibration acceleration sensor to measure the acceleration of the vibration of the bridge girder when a train passes and to acquire acceleration data. A filtering process for extracting low-frequency acceleration data, including train passing frequencies, from the aforementioned acceleration data, The velocity waveform acquisition process involves integrating the acceleration data extracted through filtering to convert it into velocity data, and then obtaining the waveform of that velocity data. A determination step in which a determination is made whether or not an abnormality has occurred in the support part, based on a predetermined correlation between the value obtained by dividing the waveform value of the acquired speed data by the train speed at the time of the train's passage and the amount of tilt at the support part and the value obtained by dividing the waveform value of the speed data by the train speed, It has, The aforementioned correlation is a value associated with the acceleration data of the bridge girder vibration measured multiple times in advance by a quartz crystal accelerometer when a train passes over it, and is characterized by associating the value obtained by dividing the waveform value of the speed data by the train speed with the amount of vertical movement of the bridge girder measured multiple times by a displacement sensor.

2. The method for monitoring abnormalities in a bridge girder support section according to claim 1, characterized in that the value relating to the waveform of the speed data is one of the total amplitude value of the speed data waveform, the maximum value of the speed data, the minimum value of the speed data, or the maximum absolute value of the speed data.

3. The method for monitoring abnormalities in a bridge girder support section according to claim 1 or 2, characterized in that the quartz vibration acceleration sensor is installed on a bridge girder support section where the bridge girder is installed on a bridge abutment via a support, and is installed on a planar portion of the steel material constituting the bridge girder.

4. The correlation regarding the amount of tilt is classified into cases where the span length of the bridge girder is less than the distance between the front and rear wheels of a train and cases where the span length of the bridge girder is greater than or equal to the distance between the front and rear wheels of a train, and the determination step is performed according to the span length of the bridge girder on which the crystal vibration type acceleration sensor is installed, characterized in that the abnormality monitoring method for a bridge girder support according to claim 1 or 2.

5. A bridge girder support abnormality monitoring system for implementing the bridge girder support abnormality monitoring method described in claim 1, An acceleration measuring device equipped with a quartz crystal oscillating acceleration sensor for measuring the acceleration of vibration of the bridge girder when a train passes over it, The system comprises an information management device capable of communicating with the aforementioned acceleration measuring device, The bridge girder bearing abnormality monitoring system is characterized by comprising control means that performs notification regarding abnormalities in the bearing based on the acceleration data measured and acquired by the crystal oscillator acceleration sensor.

6. The control means is The acceleration measuring device includes a measuring device side control unit that performs a process of transmitting the acceleration data measured and acquired by the quartz crystal oscillating acceleration sensor to the information management device, The information management device includes a control unit that performs a filtering process to extract low-frequency acceleration data, including the train passing frequency, from the acceleration data transmitted from the acceleration measuring device; a process to integrate the acceleration data extracted by the filtering process to convert it into velocity data and obtain the waveform of the velocity data; a process to obtain values ​​related to the waveform of the velocity data from the obtained waveform; a process to determine whether there is an abnormality in the support unit based on a predetermined correlation between the value obtained by dividing the value related to the waveform of the velocity data by the train speed at the time of the train's passage and the amount of tilt at the support unit and the value related to the waveform of the velocity data by the train speed, and then performs a process to notify the management device of the abnormality. The abnormality monitoring system for bridge girder support sections according to claim 5, characterized by having the following features.

7. The control means is The acceleration measuring device includes a measuring device side control unit that performs the following processes: filtering to extract low-frequency acceleration data including the train passing frequency from the acceleration data measured and acquired by the crystal oscillator type acceleration sensor; integrating the acceleration data extracted by the filtering process to convert it into velocity data and acquiring the waveform of the velocity data; acquiring values ​​related to the waveform of the velocity data from the acquired waveform of the velocity data; and transmitting data regarding the amount of tilt of the bridge girder when a train passes to the information management device, based on a predetermined correlation between the value obtained by dividing the value related to the waveform of the velocity data by the train speed when the train passes and the amount of tilt at the support section and the value obtained by dividing the value related to the waveform of the velocity data by the train speed. The information management device includes a control unit on the management device side which performs a process to determine whether or not there is an abnormality in the support section based on the data relating to the amount of tilt transmitted from the acceleration measuring device, and performs notification regarding the abnormality, The abnormality monitoring system for bridge girder support sections according to claim 5, characterized by having the following features.

8. The control means is The acceleration measuring device includes a measuring device side control unit which performs the following: filtering to extract low-frequency acceleration data including the train passing frequency from the acceleration data measured and acquired by the crystal oscillator type acceleration sensor; integrating the acceleration data extracted by the filtering process to convert it into velocity data and acquiring the waveform of the velocity data; acquiring values ​​related to the waveform of the velocity data from the acquired waveform of the velocity data; and a predetermined correlation between the value obtained by dividing the acquired value related to the waveform of the velocity data by the train speed at the time of the train's passage and the amount of tilt at the support unit and the value related to the waveform of the velocity data by the train speed, and performs a process to determine whether or not there is an abnormality in the support unit, and transmits the determination result to the information management device. The information management device includes a control unit on the management device side which, based on the judgment result transmitted from the acceleration measuring device, performs notification regarding an abnormality in the support section, The abnormality monitoring system for bridge girder support sections according to claim 5, characterized by having the following features.

9. The control means is The acceleration measuring device includes a measuring device side control unit which performs the following: filtering of acceleration data in the low-frequency band including the train passing frequency from the acceleration data measured and acquired by the crystal oscillator type acceleration sensor; integrating the acceleration data extracted by the filtering process to convert it into velocity data and acquiring the waveform of the velocity data; acquiring values ​​related to the waveform of the velocity data from the acquired waveform of the velocity data; and transmitting the acquired values ​​related to the waveform of the velocity data to the information management device. The information management device includes a control unit on the management device side that performs a process to determine whether there is an abnormality in the support section based on a predetermined correlation between the value obtained by dividing the waveform value of the speed data transmitted from the acceleration measuring device by the train speed at the time of the train's passage and the amount of tilt at the support section and the value obtained by dividing the waveform value of the speed data by the train speed, and performs a notification regarding the abnormality. The abnormality monitoring system for bridge girder support sections according to claim 5, characterized by having the following features.

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