Structural monitoring device, structural monitoring program, and structural monitoring method

Abstract

This invention provides a structural monitoring device that can estimate the natural frequency of a structure even when using data obtained from low-precision sensors. [Solution] The present invention includes: a first control means that extracts acceleration in two directions from orthogonal acceleration data of two or more dimensions obtained by measuring a vibration waveform and determines a correlation function for each frequency component; a second control means that detects peaks from the correlation of the correlation function and determines the peak frequency and peak height for each peak; a third control means that detects peaks appearing in the frequency spectrum obtained based on the acceleration data and determines information about the peak, including the peak frequency and peak height, for each peak; and a fourth control means that finds the peak on the frequency spectrum closest to the peak frequency of the correlation function and estimates and outputs a candidate natural frequency as a natural frequency.

Description

Technical Field

[0001] The present invention relates to a structure monitoring device, a structure monitoring program, and a structure monitoring method, and can be applied, for example, to a device that estimates a natural frequency from a measured vibration frequency in a structure such as a bridge.

Background Art

[0002] Conventionally, various sensors have been installed in factory facilities and infrastructure (for example, columnar structures with bridges, signs, etc. attached) to measure the soundness of objects. Many objects have a unique vibration frequency (hereinafter referred to as "natural frequency"), and it is known that the natural frequency changes when an abnormality such as deterioration occurs in the structure.

[0003] For example, when an acceleration sensor is installed in a structure, the information that the sensor can acquire is the acceleration at each instant, and the sensor cannot directly measure the frequency. To measure the frequency, the monitoring device continuously collects the measurement signal measured by the sensor and performs a Fourier transform to obtain a frequency spectrum. Since the frequency spectrum only shows the vibration amount at each frequency, it is not known which frequency is the natural frequency of the structure. Therefore, the monitoring device analyzes the spectrum shape, takes the frequency at which a peak appears, and obtains the natural frequency by judging the peak characteristics of the frequency (hereinafter, the set of peak frequencies obtained by this method is referred to as the "peak frequency group").

[0004] By the way, the data of the peak frequency group includes peak frequencies generated from various vibration sources, such as peaks caused by the vibration of accessories and peaks caused by running vehicles, in addition to the natural frequency of the structure. In order to detect an abnormality in the structure, it is necessary to identify which peak among the obtained plurality of peak frequencies is the natural frequency of the structure.

[0005] Traditionally, impact vibration tests are often used to determine the natural frequency of a structure. However, impact vibration tests are dangerous and expensive, so the natural frequency of many structures remains unknown.

[0006] Another method involves estimating the natural frequency using the peak height, but this method lacks reliability because a high peak does not necessarily correspond to the natural frequency.

[0007] To address these problems, Patent Document 1 describes a method for identifying natural frequencies by installing sensors at two locations along the vibration direction of a bridge and utilizing the relationship between horizontal and vertical vibrations. [Prior art documents] [Patent Documents]

[0008] [Patent Document 1] Japanese Patent Publication No. 2017-166922 [Overview of the project] [Problems that the invention aims to solve]

[0009] Incidentally, the method described in Patent Document 1 is based on the premise of using high-precision sensors capable of accurately measuring horizontal and vertical vibrations. Compared to horizontal vibrations, vertical vibrations are significantly smaller, making accurate measurement difficult with inexpensive sensors. Furthermore, this method has the problem of high equipment costs, partly due to the need for two sensors.

[0010] Furthermore, bridge piers and other structures where equipment is installed are usually difficult to access. In the case of railway bridges, it may be necessary to enter the railway tracks or require qualifications for working at heights. Therefore, the equipment to be installed must not require maintenance for long periods of time. High-precision equipment generally consumes a lot of power, resulting in frequent maintenance such as battery replacement. For these reasons, measurements using low-precision sensors are required.

[0011] Therefore, there is a need for a structural monitoring device, a structural monitoring program, and a structural monitoring method that can estimate the natural frequency of a structure even using data obtained with low-precision sensors. [Means for solving the problem]

[0012] The first aspect of the present invention is characterized by comprising: a first control means for extracting acceleration in two directions from orthogonal two-dimensional or more-dimensional acceleration data obtained by measuring a vibration waveform and determining a correlation function for each frequency component; a second control means for detecting peaks from the correlation of the correlation function and determining the peak frequency and peak height for each peak; a third control means for detecting peaks appearing in the frequency spectrum obtained based on the acceleration data and determining information about the peak, including the peak frequency and peak height, for each peak; and a fourth control means for determining the peak on the frequency spectrum closest to the peak frequency of the correlation function and estimating and outputting a candidate for the natural frequency as the natural frequency.

[0013] The second structural monitoring program of the present invention is characterized in that a computer functions as a first control means that extracts acceleration in two directions from orthogonal two-dimensional or more-dimensional acceleration data obtained by measuring vibration waveforms and determines a correlation function for each frequency component; a second control means that detects peaks from the correlation of the correlation function and determines the peak frequency and peak height for each peak; a third control means that detects peaks appearing in the frequency spectrum obtained based on the acceleration data and determines information about the peak, including the peak frequency and peak height, for each peak; and a fourth control means that finds the peak on the frequency spectrum closest to the peak frequency of the correlation function and estimates and outputs a candidate natural frequency as a natural frequency.

[0014] The third aspect of the present invention is a structural monitoring method for use in a structural monitoring device, characterized in that a first control means extracts acceleration in two directions from orthogonal two-dimensional or more-dimensional acceleration data obtained by measuring a vibration waveform and obtains a correlation function for each frequency component; a second control means detects peaks from the correlation of the correlation function and obtains the peak frequency and peak height for each peak; a third control means detects peaks appearing in the frequency spectrum obtained based on the acceleration data and obtains information about the peak, including the peak frequency and peak height, for each peak; and a fourth control means finds the peak on the frequency spectrum closest to the peak frequency of the correlation function and estimates and outputs a candidate natural frequency as a natural frequency. [Effects of the Invention]

[0015] According to the present invention, the natural frequency of a structure can be estimated even using data obtained with low-precision sensors. [Brief explanation of the drawing]

[0016] [Figure 1] This is an internal configuration diagram showing the internal configuration of the monitoring device according to the embodiment. [Figure 2] This is an overall configuration diagram showing the overall configuration of the monitoring system according to the embodiment. [Figure 3] This is an internal configuration diagram showing the internal configuration of the sensor device according to the embodiment. [Figure 4] This is an explanatory diagram showing an example of peak data stored in the database according to the embodiment. [Figure 5] This is an explanatory diagram illustrating the method for deriving prominence according to the embodiment. [Figure 6] This flowchart shows the procedure for deriving prominences according to the embodiment. [Figure 7] This is an explanatory diagram showing the frequency spectrum and the correlation of the two-directional data by frequency component according to the embodiment. [Figure 8] This is a flowchart illustrating the characteristic operation (natural frequency identification operation) of the monitoring device according to the embodiment.

Mode for Carrying Out the Invention

[0017] (A) Main Embodiment Hereinafter, embodiments of the structure monitoring device, the structure monitoring program, and the structure monitoring method according to the present invention will be described in detail with reference to the drawings.

[0018] (A-1) Configuration of the Embodiment (A-1-1) Overall Configuration FIG. 2 is an overall configuration diagram showing the overall configuration of the monitoring system according to the embodiment.

[0019] In FIG. 2, the monitoring system 1 includes a master unit 10, a plurality of sensor devices 20 (20-1 to 20-n; n is a positive integer) as slave units, and a monitoring device 30.

[0020] Sensors of the sensor device 20 are installed on the structure to be monitored, and the sensors measure the vibration of the structure. The sensor device 20 transmits information including the measurement signal (measurement value) measured by the sensor to the master unit 10. The master unit 10 acquires the measurement signals measured by each sensor from each of the plurality of sensor devices 20, and provides the information acquired from each sensor device 20 to the monitoring device 30. The monitoring device 30 collects the measurement signals of the sensors from each sensor device 20, and monitors the state and situation of the monitoring target using the collected measurement signals.

[0021] The sensor device 20 mainly includes a sensor, control means, and communication means. The sensor of the sensor device 20 is an acceleration sensor 203 (see FIG. 3), and the acceleration sensor 203 is fixedly installed on the structure. Note that the acceleration sensor 203 may be installed on the structure when measuring the soundness of the structure. The sensor device 20 transmits the measurement signal (measurement value) intermittently measured by the acceleration sensor 203 to the master unit 10.

[0022] The master unit 10 is a wireless device that transfers information, including measurement signals, received from the sensor devices 20 to the monitoring device 30. The master unit 10 can communicate with multiple sensor devices 20 and transfers information received from each sensor device 20 to the monitoring device 30. In this embodiment, the reason for separating the functions (transfer function and sensor function) of the master unit 10 and the sensor devices 20 is the need to extend the battery life of the sensor devices 20.

[0023] As a variation, the master unit 10 may have the same configuration as the sensor device 20 in addition to the transmission function. In that case, the master unit 10 may directly transmit the measurement signals measured by its own sensors to the monitoring device 30. Another variation is that the master unit 10 may have all or part of the functions of the monitoring device 30 described later.

[0024] The monitoring device 30 collects and stores (stores) measurement signals measured by the acceleration sensor 203 from each sensor device 20 via the master unit 10. The monitoring device 30 performs a Fourier transform on the measurement signals from the acceleration sensor 203 to derive a frequency spectrum. Furthermore, the monitoring device 30 analyzes the shape of the frequency spectrum to extract specific frequency regions where peaks appear under normal conditions of the structure, and stores peak data for each specific frequency region.

[0025] Figure 2 illustrates an information communication system in which a sensor device 20, acting as a slave unit, transmits information to a monitoring device 30 via a master unit 10. However, this configuration of the information communication system is just one example. The configuration of the information communication system is not limited to the one shown in Figure 2, as long as the sensor device 20 can transmit information to the monitoring device 30.

[0026] The network, including the master unit 10, sensor devices 20, and monitoring device 30, may use either a wireless or wired connection. For example, the sensor devices 20 and the master unit 10 may form a sensor network. The communication method for the sensor network can be a low-speed wireless communication method, such as a low-power wireless communication method. For example, it may be a wireless network standard such as IEEE 802.11a / b / g / n, or a wireless communication method such as IEEE 802.15.4 or Bluetooth®.

[0027] The network between the master unit 10 and the monitoring device 30 may be a backbone network. The backbone network may be, for example, the Internet or Ethernet®. The backbone network may be a wireless line or a wired line.

[0028] (A-1-2) Internal configuration of sensor device 20 Figure 3 is an internal configuration diagram showing the internal configuration of the sensor device according to the embodiment.

[0029] In Figure 3, the sensor device 20 includes a communication unit 201, a control unit 202, an acceleration sensor 203, and a clock 204.

[0030] The sensor device 20 may be configured using hardware, or some of its components may be configured using software.

[0031] The communication unit 201 is a communication interface that communicates with the network connected to the master unit 10.

[0032] The acceleration sensor 203 measures vibrations of structures such as bridges. The acceleration sensor 203 measures instantaneous acceleration and provides a measurement signal to the control unit 202.

[0033] The control unit 202 is a device and processing unit that manages various functions in the sensor device 20. The control unit 202 includes, for example, a CPU, ROM, RAM, EEPROM, input / output interface, etc. Processing may be realized by the CPU executing a processing program (for example, a measurement program, etc.) stored in the ROM.

[0034] Clock 204 is a clock that provides the current time and instructs the control unit 202 when the time to be measured is reached. The measurement time may be determined, for example, based on an external instruction or a calculation performed by the control unit 202 from the measurement result.

[0035] In this embodiment, there are two operating modes: one that transmits the measured acceleration value as is, and another that performs a Fourier transform, extracts the peak, and transmits only the peak frequency and height. Switching between operating modes is determined by instructions from the monitoring device 30, the result of calculation processing inside the sensor device, or the clock 204 of the sensor device 20.

[0036] (A-1-3) Internal configuration of the monitoring device 30 Figure 1 is an internal configuration diagram showing the internal configuration of the monitoring device according to the embodiment.

[0037] In Figure 1, the monitoring device 30 includes a communication unit 301, a control unit 302, and a database 303.

[0038] The communication unit 301 is a communication interface for network communication connected to the master unit 10.

[0039] The control unit 302 is a device or processing unit that manages various functions of the monitoring device 30. The control unit 302 may be composed of, for example, a device having a CPU, ROM, RAM, EEPROM, input / output interface, etc. Processing may be realized by the CPU executing a processing program (for example, a monitoring program, etc.) stored in ROM.

[0040] Database 303 stores information for determining the structural integrity of a building. For example, database 303 stores measurement data from each acceleration sensor 203 (e.g., measurement signals measured by the acceleration sensor 203, frequency spectrum data obtained by Fourier transforming the measurement signals, etc.) and peak data in a specific frequency range obtained using the measurement signals (e.g., data including frequency, values ​​indicating peak height, date and time information, etc.).

[0041] In this embodiment, the monitoring device 30 estimates the natural frequency by determining the correlation and phase difference between vertical and horizontal vibrations, even with data obtained from a low-precision sensor (accelerometer 203).

[0042] This method utilizes the fact that, for example, assuming a vibration center exists directly beneath a bridge pier, vibrations of the same frequency occur in both the horizontal and vertical directions. Furthermore, in principle, the phase difference between vibrations in both directions is either 0 or 180 degrees.

[0043] Furthermore, the sensor device 20 performs measurements multiple times and prioritizes the use of data with high correlation, thereby increasing the reliability of the estimation of the natural frequency. In addition, by identifying the natural frequency using this method, it becomes possible to reduce the number of acceleration sensors 203 installed in each sensor device 20 to one.

[0044] Furthermore, after the monitoring device 30 has identified the natural frequency, it performs a Fourier transform on the sensor value to obtain the frequency spectrum. By determining the frequency at which the spectrum peaks (hereinafter referred to as the peak frequency), it becomes possible to investigate fluctuations in the natural frequency. Identifying the natural frequency requires a lot of communication, but determining the peak frequency can be done entirely through calculations within the sensor device 20, thus significantly reducing the amount of communication and the power consumption required for communication.

[0045] By enabling the identification of natural frequencies using low-precision sensors, and by having the ability to switch between two operations—natural frequency identification and peak frequency measurement—on the same device, it becomes possible to realize a device with reduced power consumption, resulting in a significant reduction in running costs, including battery replacement.

[0046] (A-2) Operation of the embodiment Next, the operation of the monitoring system 1 according to the embodiment having the above configuration will be described.

[0047] <Overall processing overview> First, we will explain the overall processing in monitoring system 1, including conventional processing (processing that is not directly related to the feature part described later).

[0048] When the measurement timing time is reached, the sensor device 20 activates the acceleration sensor 203 for a predetermined period of time. The operating acceleration sensor 203 measures data indicating the characteristics of the structure, such as acceleration due to vibration (hereinafter, this data is referred to as "acceleration data"). The acceleration data shall include at least time and acceleration information. If the acceleration sensor 203 is a 3-axis acceleration sensor, acceleration in three directions can be obtained, but the number of dimensions is not limited as long as data in the required directions can be obtained.

[0049] The communication unit 201 transmits information including the measurement signal (measured value) measured by the acceleration sensor 203 to the master unit 10, and the master unit 10 forwards the information received from the sensor device 20 to the monitoring device 30.

[0050] The monitoring device 30 obtains a frequency spectrum by performing a Fourier transform on the received acceleration data. Furthermore, the monitoring device 30 finds the frequency at which the frequency spectrum takes a maximum value and obtains a group of peak frequencies. This calculation may be performed by the sensor device 20 instead of the monitoring device 30. In this case, the sensor device 20 will transmit the calculation result (group of peak frequencies) to the monitoring device 30 via the master unit 10. Through these operations, the group of peak frequencies is acquired by the monitoring device 30.

[0051] The acceleration sensor 203 has an effective frequency range. Reliability of data obtained at frequencies outside this specific range may be compromised. Furthermore, individual peak frequencies have varying heights. Higher peaks are those obtained from the main vibration source and are considered highly valuable.

[0052] Here, we will use the concept of "prominence" as an example of deriving peak height. Figures 5 and 6 will be used to explain how to derive prominence.

[0053] Figure 5(A) shows an example of a frequency spectrum. For ease of explanation, the leftmost point of the frequency spectrum is denoted as "a" and its rightmost point as "g". The five maximum values ​​from "a" to "g" are designated as peak numbers "1" to "5", and the five minimum values ​​are designated as "b" to "f".

[0054] The control unit 302 of the monitoring device uses prominences to indicate the height of each peak, according to the flowchart in Figure 6.

[0055] First, the control unit 302 selects a peak (S11), and then moves to the left and right of the peak until the value it has traced reaches the next state, thereby guiding the endpoint of the horizontal line (S12). • The value intersects with a higher peak. • The value reaches the leftmost or rightmost limit.

[0056] Next, the control unit 302 finds the minimum point (minimum value) in the left and right sections of the peak derived in step S12 (S13), and sets the higher of the two minimum values ​​derived in step S13 as the reference level (highest local minimum) (S14). The control unit 302 sets the difference between the reference level and the peak as the peak height (prominence) (S15).

[0057] The control unit 302 determines whether all peaks have been selected (S16). If not all peaks have been selected (S16 / No), the control unit 302 proceeds to S11 and repeats the process. On the other hand, if all peaks have been selected (S16 / Yes), the control unit 302 terminates the process.

[0058] For example, we will specifically explain an example of how to derive the prominence of the peak labeled "Peak Number: 1" in Figure 5(A).

[0059] First, select the peak with "Peak Number: 1" (S11). Moving the value to the left from this peak, the value reaches "Left End a," so "Left End a" becomes the endpoint of the horizontal line for the left section of the peak. On the other hand, moving the value to the right from the peak, the value intersects with the peak with "Peak Number: 2," which is higher than the peak with "Peak Number: 1," so the point where the value intersects with Peak 2 becomes the endpoint of the horizontal line for the right section of the peak (S12).

[0060] Next, since the minimum point in the left section of the peak is "leftmost point a" and the minimum point in the right section of the peak is "b" (S13), the higher of "leftmost point a" and "b" is taken as the reference level (S14). Then, the height "P1" from the reference level "b" to "peak number: 1" is taken as the height (prominence) of the peak "peak number: 1" (S15).

[0061] The control unit 302 performs the above-described processing on all peaks, and the results are shown in Figure 5(B).

[0062] Figure 7(A) shows an example of a frequency spectrum. The horizontal axis represents frequency (Hz), and the vertical axis represents the amplitude spectrum. For example, high peaks are visible around 5 Hz and around 10 Hz.

[0063] In the monitoring device 30, the database 303 records, for example, peak frequency, height, measurement time, measured sensor, and axis (required when data from multiple sensors and axes is acquired) as one data item (one pick data item). Figure 4 is an explanatory diagram showing an example of peak data stored in the database according to this embodiment.

[0064] <Features of this embodiment> Next, the characteristic operation of this embodiment will be described. Here, first, the sensor device 20 transmits acceleration data to the monitoring device 30.

[0065] In this embodiment, it is assumed that data is acquired simultaneously in two directions: horizontal and vertical.

[0066] For example, let's consider the piers of a bridge. The direction in which vehicles travel on a bridge is called the "bridge axis direction." It is thought that the piers vibrate very little in this direction. In contrast, it is thought that they vibrate relatively significantly in the direction in which the river flows (hereinafter referred to as the "river direction"). Generally, vibration data in two directions, the "river direction" and the "gravity direction (vertical direction)," is used (hereinafter, the horizontal direction generally refers to the river direction, but is not limited to it).

[0067] The operation of the mode for identifying (estimating) the natural frequency is described below (hereinafter referred to as the "first mode"). Figure 8 is a flowchart of the characteristic operation (natural frequency identification operation) of the monitoring device according to the embodiment.

[0068] <S101、S102> The control unit 302 extracts horizontal and vertical data from the acceleration data acquired from the sensor device 20 (acceleration data stored in the database 303). The control unit 302 can determine the correlation at each frequency by calculating the frequency component-specific correlation (coherence function) of the two time series data.

[0069] <s103> Next, the control unit 302 extracts the frequencies at which the coherence function peaks (hereinafter, the extracted frequencies will be referred to as "coherence peaks"). The number of frequencies to be extracted can be set arbitrarily, and a configuration can also be adopted in which the phase difference is acquired simultaneously.

[0070] <s104> For each peak frequency PK1 of the coherence function, the peak frequency PK2 of the spectral function closest to PK1 is searched for. PK2 is taken as the natural frequency of the structure. If PK1 and PK2 differ significantly, a configuration that does not use that frequency can be adopted.

[0071] Furthermore, if the phase difference is large, such as 0 or 180 degrees, a configuration can be adopted in which the peak frequency in question is not used as the natural frequency.

[0072] Figure 7(B) shows an example of a coherence function (coherence takes a maximum value of 1 and a minimum value of 0).

[0073] As shown in Figure 7(A), the frequency spectrum has peaks around 5 Hz and 10 Hz. On the other hand, as shown in Figure 7(B), coherence peaks are present around 5 Hz and 12 Hz, so the peak around 5 Hz can be estimated to be the natural frequency (the peak in the frequency spectrum of the vibration around 5 Hz is close to 6 Hz, and the peak around 12 Hz is presumed to be the second harmonic of the peak around 6 Hz).

[0074] By using coherence (and even phase difference) in this way, it becomes possible to eliminate peaks in the frequency spectrum that are not at natural frequencies, as shown in Figure 7 above at 10 Hz.

[0075] Because vertical vibrations are considerably smaller than horizontal vibrations, inexpensive sensors are easily masked by noise, making accurate measurement difficult. Furthermore, when multiple peaks are detected, information for identifying the natural frequency is limited to the height and phase of the coherence peak. Additionally, if the ground vibrates, peaks may appear at unrelated frequencies. Therefore, identifying the natural frequency with a single measurement is difficult.

[0076] However, by repeatedly taking measurements and obtaining the frequency of coherence peaks, it is possible to identify the natural frequencies. It is unlikely that coherence peaks caused by disturbances would continuously occur at the same frequency. Therefore, it is possible to estimate the natural frequencies based on the continued occurrence of correlated vibrations at the same frequency.

[0077] Next, we will describe the operation after the natural frequency has been identified (hereinafter referred to as the "second mode").

[0078] Generally, natural frequencies appear in horizontal or vertical spectral peaks. Therefore, after identifying the natural frequencies, the monitoring device 30 can monitor them by acquiring simple spectral peaks.

[0079] Performing Fourier transform and peak extraction in the sensor device 20 significantly reduces the amount of communication data. Reducing the amount of communication data directly leads to a reduction in power consumption. Therefore, in the second mode, the sensor device 20 can significantly extend its battery life compared to continuing in the first mode by only transmitting peaks.

[0080] Furthermore, we will discuss switching between the first and second modes.

[0081] The triggers for the sensor device 20 to switch between the first and second modes are not particularly limited, but the following three examples can be given.

[0082] (1) Switch manually (including remote manual operation from monitoring device 30, etc.).

[0083] (2) Specify the acquisition time in the first mode in advance, and switch to the second mode after the time has elapsed.

[0084] (3) The first mode and the second mode are switched periodically by a timer.

[0085] In situations such as disasters, where an anomaly may have occurred, it may be necessary to perform the first mode again, even after the natural frequency has been determined.

[0086] (A-3) Effects of the Embodiment This embodiment provides the following effects.

[0087] In monitoring infrastructure structures, it becomes possible to estimate natural frequencies without performing tasks such as impact vibration tests. Furthermore, it becomes possible to continuously measure the presence or absence of abnormalities in the structure from the temporal changes in the estimated natural frequencies. In the monitoring system 1 of this embodiment, maintenance costs can be reduced by performing these measurements with low power consumption.

[0088] By streamlining monitoring, it becomes possible to monitor structures that were not previously included in the monitoring process.

[0089] (B) Other embodiments Although various modified embodiments have been mentioned in the embodiments described above, the present invention can also be applied to the following modified embodiments.

[0090] (B-1) There are many situations in which the estimation of natural frequencies is not guaranteed to be successful with each measurement. To estimate natural frequencies, the monitoring device 30 can be configured to perform the final estimation of natural frequencies by repeating measurements multiple times and using the reliability of the natural frequencies obtained from the height of the correlation function and the frequency of occurrence of the natural frequencies.

[0091] (B-2) As a variation, the monitoring device 30 may have a function to compare the estimated natural frequency with natural frequencies previously estimated and notify the user if it is determined to be an outlier (the notification means, such as outputting on the screen of the monitoring device 30, is not particularly limited).

[0092] (B-3) As a variation, when the monitoring device 30 determines the peak frequency on the frequency spectrum that is closest to the peak frequency of the correlation function, peak data on the frequency spectrum with a height below a threshold does not need to be considered in the estimation of the natural frequency.

[0093] (B-4) As a variation, when the monitoring device 30 finds the peak frequency on the frequency spectrum that is closest to the peak frequency of the correlation function, it does not need to estimate the frequency as an eigenfrequency if the difference in frequency is greater than or equal to a threshold.

[0094] (B-5) As a variation, if the natural frequency has been determined in advance through experiments or other means, the monitoring device 30 can be configured to not output natural frequency candidates that are significantly off the target by initially including a process that excludes frequencies other than those around the known natural frequency from the search.

[0095] (B-6) There are cases where a clear peak is present in the frequency spectrum, but no peak in the correlation function is observed at surrounding frequencies. Even in such cases, it is possible to use a configuration that outputs the peak in the frequency spectrum as a candidate for the natural frequency. This indicates that the vibration in the direction of gravity is extremely small and estimation by the correlation function is not working, but it takes advantage of the fact that if there is a clear peak in the frequency spectrum, it is a strong candidate for the natural frequency.

[0096] (B-7) It is also possible to use a configuration that combines (integrates) the measurement results from multiple sensors (for example, multiple acceleration sensors 203 in one sensor device 20 or multiple sensor devices 20). For example, the following two are possible.

[0097] (1) Multiple sensors are used to take measurements simultaneously, and only the frequency peaks that are commonly measured are used.

[0098] (2) Install multiple sensors at different locations and use only the frequency peaks that are measured in common. [Explanation of Symbols]

[0099] 1...Monitoring system, 2...Peak, 10...Master unit, 20...Sensor equipment, 30...Monitoring device, 201...Communication unit, 202...Control unit, 203...Accelerometer, 204...Clock, 301...Communication unit, 302...Control unit, 303...Database.

Claims

1. A first control means that extracts acceleration in two directions from orthogonal acceleration data of two or more dimensions obtained by measuring vibration waveforms, and determines correlation functions for each frequency component, A second control means detects peaks from the correlation of the aforementioned correlation function and determines the peak frequency and peak height for each peak, A third control means detects peaks appearing in the frequency spectrum obtained based on the acceleration data and obtains information about each peak, including the peak frequency and peak height. A fourth control means that finds the peak on the frequency spectrum closest to the peak frequency of the correlation function, estimates the candidate natural frequency as the natural frequency and outputs it. A structural monitoring device characterized by having the following features.

2. A storage means for storing the aforementioned natural frequency candidates along with the measurement time, A fifth control means estimates the natural frequency from the natural frequency candidate data measured multiple times and stored in the storage means. The structure monitoring device according to claim 1, further comprising the above.

3. The structural monitoring device according to claim 1, characterized in that it has a first mode for estimating the natural frequency by receiving acceleration data from a measuring instrument, and a second mode for monitoring the structure by receiving information on peaks appearing in the frequency spectrum obtained based on the acceleration data from the measuring instrument after estimating the natural frequency, instead of the acceleration data itself.

4. The structural monitoring device according to claim 1, further comprising a sixth control means for comparing the estimated natural frequency with the natural frequency previously estimated and notifying the user if it is determined to be an outlier.

5. The structure monitoring device according to claim 1, characterized in that the fourth control means does not consider peak data on the frequency spectrum with a height below a threshold when determining the peak frequency on the frequency spectrum closest to the peak frequency of the correlation function.

6. The structure monitoring device according to claim 1, characterized in that the fourth control means does not determine the candidate natural frequency as the natural frequency when determining the peak frequency on the frequency spectrum that is closest to the peak frequency of the correlation function if the difference in frequency is greater than or equal to a threshold.

7. The structure monitoring device according to claim 1, characterized in that the fourth control means deletes data other than the surrounding frequencies of a natural frequency specified from the outside in advance for a structure whose natural frequency is known in advance.

8. The structure monitoring device according to claim 1, characterized in that the fourth control means determines the reliability of the extracted natural frequency candidates as natural frequency candidates based on the height of the correlation function and outputs it.

9. The structural monitoring device according to claim 1, characterized in that the fourth control means measures acceleration using a plurality of measuring instruments and estimates the natural frequency by integrating the frequency spectrum and the correlation function results obtained based on the acceleration data.

10. The structure monitoring device according to claim 1, characterized in that the fourth control means uses the peak frequency on the frequency spectrum as the natural frequency candidate even if the peak frequency of the correlation function is not obtained around the peak frequency on the frequency spectrum.

11. It further includes a seventh control means for determining the phase difference of the frequency components of acceleration in two directions, The structure monitoring device according to any one of claims 1 to 10, characterized in that the fourth control means adopts the peak on the frequency spectrum closest to the peak frequency of the correlation function as the natural frequency candidate only when the phase difference is 0 or near 180 degrees.

12. Computers, A first control means that extracts acceleration in two directions from orthogonal acceleration data of two or more dimensions obtained by measuring vibration waveforms, and determines correlation functions for each frequency component, A second control means detects peaks from the correlation of the aforementioned correlation function and determines the peak frequency and peak height for each peak, A third control means detects peaks appearing in the frequency spectrum obtained based on the acceleration data and obtains information about each peak, including the peak frequency and peak height. A fourth control means that finds the peak on the frequency spectrum closest to the peak frequency of the correlation function, estimates the candidate natural frequency as the natural frequency and outputs it. A structural monitoring program characterized by its ability to function in this manner.

13. A method for monitoring structures used in a structural monitoring device, The first control means extracts acceleration in two directions from orthogonal acceleration data of two or more dimensions obtained by measuring the vibration waveform, and determines a correlation function for each frequency component. The second control means detects peaks from the correlation of the correlation function and determines the peak frequency and peak height for each peak. The third control means detects peaks appearing in the frequency spectrum obtained based on the acceleration data, and obtains information about each peak, including the peak frequency and peak height. The fourth control means finds the peak on the frequency spectrum closest to the peak frequency of the correlation function, estimates the candidate natural frequency as the natural frequency, and outputs it. A method for monitoring structures characterized by the following features.

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