Detection device, detection method and program

Abstract

To provide a detection device that has a wide range of application, while maintaining the accuracy of estimating a characteristic frequency.SOLUTION: There is provided a detection device comprising: a data acquisition unit that acquires measurement data from a target structure; an index calculation unit that analyzes vibration characteristics of the target structure based on the measurement data to calculate a diagnostic index related to the soundness of the target structure; a diagnosis unit that diagnoses the soundness of the target structure based on the diagnostic index; and an output unit that outputs a result of diagnosis of the soundness. There is provided a detection method for detecting the soundness of the target structure by using the detection device, the detection method including the steps of: acquiring measurement data from the target structure; analyzing the vibration characteristics of the target structure based on the measurement data to calculate the diagnostic index related to the soundness of the target structure; diagnosing the soundness of the target structure based on the diagnostic index; and outputting the result of diagnosis of the soundness.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Conventionally, techniques for detecting scouring of target structures such as bridge piers are known (see, for example, Patent Documents 1 and 2). In Patent Document 1, a search range for vibration frequencies that includes the natural frequency of the target pier is set, and the frequency at which the amplitude of the Fourier spectrum is maximum is considered to be the natural frequency. In Patent Document 2, sensors are installed at both ends of the top of the structure, ground vibrations are estimated based on the vibrations of the structure detected by the sensors, and the natural frequency is estimated based on the phase difference between the vibrations of the pier and the ground. Patent Document 1 Patent No. 4698466 Specification Patent Document 2: JP 2017-166922 A Summary of the Invention [Problem to be solved by the invention]

[0003] With the method of Patent Document 1, it is sometimes difficult to identify the natural frequency with high accuracy, and the range of application is limited. Furthermore, with the method of Patent Document 2, there is a risk that the number of sensors to be installed in the structure will increase. [Means for solving the problem]

[0004] In a first aspect of the present invention, a detection device is provided that includes a data acquisition unit that acquires measurement data from a target structure, an index calculation unit that analyzes the vibration characteristics of the target structure based on the measurement data and calculates a diagnostic index related to the soundness of the target structure, a diagnosis unit that diagnoses the soundness of the target structure based on the diagnostic index, and an output unit that outputs the diagnostic results of the soundness.

[0005] The output unit may output at least one of the measurement data, the diagnostic index, and the diagnostic result.

[0006] The detection device may include a storage unit that stores at least one of the measurement data, the diagnostic index, and the diagnostic result.

[0007] The diagnostic index may include the natural frequency and the statistical distribution of the natural frequency based on the measurement data of the target structure.

[0008] The diagnostic index may include a natural frequency based on measurement data of the target structure and an alarm issuance probability index value based on a probability density function identified from the statistical distribution of the natural frequency.

[0009] The index calculation unit may calculate the statistical distribution of the natural frequencies by fast Bayesian FFT.

[0010] The data acquisition unit may acquire free damped vibration data after a predetermined vibration occurs in the target structure.

[0011] The data acquisition unit may acquire constant microtremor vibration data when a predetermined vibration is not occurring in the target structure.

[0012] The output unit may display the diagnostic index and the statistical distribution of the diagnostic index.

[0013] The output unit may display the diagnostic index, the statistical distribution of the diagnostic index, and the probability density function identified from the statistical distribution.

[0014] The detection device may include an information acquisition unit that acquires information for analyzing the vibration characteristics of the target structure.

[0015] The data acquisition unit may acquire time-series measurement data from the target structure. The index calculation unit may analyze the vibration characteristics of the target structure based on the time-series measurement data and calculate diagnostic indices related to the soundness of the target structure in time series. The diagnosis unit may diagnose the soundness of the target structure based on the diagnostic indices calculated in time series. The output unit may output the soundness diagnosis results in time series.

[0016] The output unit may further display structural environment information indicating the environmental conditions of the target structure.

[0017] In a second aspect of the present invention, a detection method is provided for detecting the soundness of a target structure using a detection device, the detection method comprising the steps of acquiring measurement data from the target structure, analyzing the vibration characteristics of the target structure based on the measurement data and calculating a diagnostic index related to the soundness of the target structure, diagnosing the soundness of the target structure based on the diagnostic index, and outputting the diagnostic results of the soundness.

[0018] In a third aspect of the present invention, there is provided a program for causing a computer to execute the detection method according to the second aspect of the present invention.

[0019] The above summary of the invention does not list all of the features of the present invention. Subcombinations of these features may also be inventions. [Brief explanation of the drawings]

[0020] [Figure 1] 1 shows an example of the configuration of a detection device 100. [Figure 2] An example of the statistical distribution of the natural frequency estimation results is shown. [Figure 3] An example of the statistical distribution of the natural frequency estimation results is shown. [Figure 4A] 1 shows an example of an estimated distribution of natural frequencies. [Figure 4B] The time history of the natural frequency and the histogram corresponding to the natural frequency are shown. [Figure 5] The graphs show the estimated natural frequency, modal external forces, S / N ratio, and time history fluctuations of water level. [Figure 6A] 1 shows an example of an operational flowchart of a detection method using the detection device 100. [Figure 6B] An example of the estimation process in step S110 in FIG. 6A is shown. [Figure 6C] An example of the diagnostic process in step S120 in FIG. 6A is shown. [Figure 6D] An example of the statistical distribution of the natural frequency estimation results under normal conditions and the identified probability density function is shown below. [Figure 6E] An example of the statistical distribution of the natural frequency estimation results during flooding and the identified probability density function is shown below. [Figure 6F] 10 shows an example of a time history graph of the warning issuance probability index value and water level. [Figure 7] 22 illustrates an example computer 2200 in which aspects of the present invention may be embodied, in whole or in part. DETAILED DESCRIPTION OF THE INVENTION

[0021] The present invention will be described below through embodiments of the invention, but the following embodiments do not limit the scope of the invention as claimed. Furthermore, not all of the combinations of features described in the embodiments are necessarily essential to the solution of the invention.

[0022] 1 shows an example of the configuration of a detection device 100. The detection device 100 includes a data acquisition unit 10, an information acquisition unit 20, an index calculation unit 30, a diagnosis unit 40, an output unit 50, and a storage unit 60.

[0023] The data acquisition unit 10 acquires measurement data from the target structure 150. In this example, the data acquisition unit 10 acquires the measurement data of the target structure 150 from a sensor 110 installed in the target structure 150. By acquiring the measurement data of the target structure 150, the vibration characteristics of the target structure 150 can be acquired. For example, the measurement data is acceleration data acquired from an acceleration sensor. Furthermore, the measurement data is not limited to acceleration data, and may be any data such as velocity data or displacement data.

[0024] The sensor 110 is installed in the target structure 150. The detection device 100 may install one or more sensors 110 in the target structure 150. The sensor 110 transmits the measured measurement data to the detection device 100 via a network or the like. In this example, the sensor 110 is attached to the top 152 of the target structure 150. The top 152 is the highest part of the target structure 150. The sensor 110 may also be installed in other parts of the target structure 150, such as the side of the target structure 150.

[0025] The target structure 150 is a pier of a railway or road bridge. The detection device 100 analyzes measurement data from the target structure 150 to diagnose the soundness of the target structure 150. The soundness of the target structure 150 may be a scour possibility, which indicates whether scouring occurring in the target structure 150 has reached a dangerous level that could lead to collapse or other problems. Scour means that the soil around the target structure 150 is eroded away by rain, wind, or waves. Note that even when the scour possibility of the target structure 150 is described in this specification, it is also applicable to the evaluation of structural soundness other than scouring, such as damage to concrete structures or buckling of steel frames.

[0026] The information acquisition unit 20 acquires information for analyzing the vibration characteristics of the target structure 150. The information acquisition unit 20 may also acquire information necessary for diagnosing the soundness of the target structure 150. For example, the information acquisition unit 20 acquires information such as parameters used in the index calculation unit 30 or the diagnosis unit 40. The information acquisition unit 20 may acquire information input by input means such as a keyboard or a mouse. The information acquisition unit 20 may be able to communicate with the outside world by communication means such as a wired LAN or wireless LAN.

[0027] The index calculation unit 30 analyzes the vibration characteristics of the target structure 150 based on the measurement data and calculates a diagnostic index related to the health of the target structure 150. In one example, the index calculation unit 30 estimates the vibration characteristics of the target structure 150 based on the measurement data. The vibration characteristics may be at least one of the natural frequency, damping constant, or vibration mode of the target structure 150. For example, the index calculation unit 30 may calculate the statistical distribution of the natural frequency by the fast Bayes FFT, which will be described later. The fast Bayes FFT is an example of a Bayesian operating mode identification method based on Bayesian estimation.

[0028] In addition, the index calculation unit 30 may calculate a diagnostic index relating to the soundness of the target structure 150 based on the measurement data acquired by the data acquisition unit 10 and various information acquired by the information acquisition unit 20.

[0029] The diagnostic index is an index for diagnosing the soundness based on the vibration characteristics. For example, the diagnostic index may be a comparison between the natural frequency of the target structure 150 and a threshold value based on past statistical data. The diagnostic index may include a statistical distribution of the vibration characteristics. The diagnostic index may also include an alarm issuance probability index value based on a probability density function identified from the statistical distribution of the vibration characteristics of the target structure. The alarm issuance probability index value will be described later. The diagnostic index is not limited to this, as long as it can diagnose the soundness of the target structure 150 in the diagnosing unit 40.

[0030] The diagnosis unit 40 diagnoses the soundness of the target structure 150 based on the diagnostic index. For example, the diagnosis unit 40 diagnoses the soundness by comparing the diagnostic index with a predetermined diagnostic threshold. The diagnosis unit 40 transmits the soundness diagnosis result to the output unit 50. A specific method for diagnosing the soundness will be described later.

[0031] The output unit 50 outputs the diagnosis results of the soundness. The output unit 50 may include a display, a propagation device, a printer, etc. The output unit 50 outputs at least one of the measurement data acquired by the data acquisition unit 10, the various information acquired by the information acquisition unit 20, the vibration characteristics of the target structure 150, the diagnostic index calculated by the index calculation unit 30, or the diagnostic result obtained by the diagnosis unit 40. The output method by the output unit 50 may be a method such as display, transmission, or printing.

[0032] The output unit 50 displays the diagnostic indexes and the statistical distribution of the diagnostic indexes. For example, the output unit 50 simultaneously displays the diagnostic indexes and the statistical distribution of the diagnostic indexes to the user. By displaying the diagnostic indexes and the statistical distribution of the diagnostic indexes, the soundness can be diagnosed based on information about uncertainty.

[0033] The storage unit 60 stores the measurement data acquired by the data acquisition unit 10, various information acquired by the information acquisition unit 20, the diagnostic index calculated by the index calculation unit 30, or the diagnosis result by the diagnosis unit 40. The storage unit 60 in this example stores at least one of the measurement data, diagnostic index, or health diagnosis result of the target structure 150. For example, the storage unit 60 includes a hard disk, BD, DVD, CD-R, MO, floppy disk, or memory.

[0034] The detection device 100 of this example can prevent the collapse of the target structure 150 due to scouring by detecting scouring of the target structure 150 using the sensor 110. This can prevent disasters caused by the collapse of the target structure 150 due to scouring of the ground around the bridge pier caused by rising river waters, etc.

[0035] The detection device 100 of this example estimates the vibration characteristics of the target structure 150 based on the measurement data acquired by the data acquisition unit 10. To estimate the vibration characteristics of the target structure 150, a system identification method, the stochastic subspace method, or a method based on Bayes' theorem may be applied. In Bayes' estimation, the parameters that make up a statistical model are evaluated as a probability distribution, so information regarding the uncertainty of the estimated value can be estimated directly and quantitatively from the observed value. The stochastic subspace method and the method based on Bayes' theorem will be described later.

[0036] The detection device 100 of this example does not need to perform impact vibration tests, which involve risks such as actually applying an impact to a bridge pier and measuring the vibration frequency. Furthermore, since the detection device 100 does not need to perform irregular impact vibration tests, it can constantly monitor the target structure 150.

[0037] In one example, the data acquisition unit 10 acquires free damping vibration data after a predetermined vibration occurs in the target structure 150. For example, if the target structure 150 is a bridge, the predetermined vibration is a vibration that occurs when a vehicle passes over the bridge of the target structure 150. The detection device 100 can perform vibration analysis with reduced influence of noise by using the free damping vibration data after the vehicle passes over.

[0038] Furthermore, the data acquisition unit 10 may acquire constant microtremor vibration data when no predetermined vibration is occurring in the target structure 150. For example, the data acquisition unit 10 acquires constant microtremor vibration data when no vehicle is passing over the bridge of the target structure 150. The detection device 100 can grasp the vibration characteristics when the structure is healthy by using the constant microtremor data when no vehicle is passing over.

[0039] The detection device 100 of this example can also be used when the river is flooded. When the river is flooded, the risk of scouring increases, but if vehicle traffic is prohibited during floods, it becomes impossible to estimate vibration characteristics using vibrations caused by vehicle traffic. Therefore, in situations where vibration characteristics analysis using microtremor data is unavoidable, analysis using microtremor data is significantly affected by noise, etc., and the accuracy of the natural frequency estimation may not be high. Even in such cases, the detection device 100 of this example can improve reliability by applying a method that can directly evaluate the uncertainty of the estimated value and also showing the statistical distribution of the estimation results.

[0040] Therefore, the detection device 100 can diagnose the soundness of the target structure 150 even in an environment where ground vibrations are significantly mixed with surrounding noise. This makes it possible to widen the range of application of the detection device 100 while maintaining the accuracy of estimating the natural frequency. Furthermore, the scour diagnosis method using the detection device 100 can diagnose soundness even with a small number of sensors.

[0041] Figure 2 shows an example of the display of the statistical distribution of the natural frequency estimation results. The vertical axis shows the natural frequency [Hz], and the horizontal axis shows the number of hits [items]. In this example, the statistical distribution of the natural frequency of the target structure 150 is displayed.

[0042] The index calculation unit 30 calculates the natural frequency and the statistical distribution of the natural frequency based on the measurement data of the target structure 150 as a diagnostic index. The detection device 100 can inform the user of the likelihood of the natural frequency by displaying the statistical distribution of the natural frequency in addition to the natural frequency. The index calculation unit 30 may also calculate, as a diagnostic index, an alarm issuance probability index value based on the statistical distribution of the natural frequency estimation results. By calculating the alarm issuance probability index value, the user can be informed of the probability that the diagnostic index will fall below a threshold and an alarm will be issued.

[0043] The detection device 100 can also display the reliability of the estimated value by displaying the statistical distribution of the estimated results for a fixed period, such as every 10 minutes. In one example, the detection device 100 displays the average value and standard deviation of the natural frequency. The detection device 100 in this example displays the average value and variance of the natural frequency. The average value of the natural frequency in this example is 15.07, and the variance is 0.42.

[0044] Table 1 shows an example of a diagnostic index, category, and determination method for diagnosing soundness. The output unit 50 may display all the information necessary for diagnosing soundness as shown in Table 1 to the user.

Table 1

[0045] The detection device 100 can set an evaluation index for the natural frequency in the sound state and determine based on the estimated value distribution of the natural frequency over a certain period. The natural frequency in the sound state may be calculated using the constantly vibrating data when the vehicle is not passing. x is represented by the following equation. x = Estimated value of natural frequency / Natural frequency in sound state

[0046] The detection device 100 in this example diagnoses the soundness by dividing the category into four according to the evaluation index of x. For example, when x ≤ 0.70, it corresponds to category A1 and is determined to be abnormal. In the case of abnormality, repair or reinforcement of the target structure 150 is performed. When 0.70 < x ≤ 0.85, it corresponds to category A2 and is determined that confirmation is necessary. When 0.85 < x ≤ 1.00, it corresponds to category B and is determined that the possibility of abnormal occurrence is low. And when 1.00 < x, it corresponds to category S and is determined that the target structure 150 is sound.

[0047] Figure 3 shows an example of the display of the statistical distribution of the estimated results of the natural frequency. In this example, the position corresponding to the predetermined evaluation index is indicated by a dashed line.

[0048] The dashed line indicates the case where the evaluation index is x=0.85 when the natural frequency in a healthy state is 15.9 Hz. Referring to Table 1, natural frequencies greater than the dashed line can be determined to be category B or S, which means that the possibility of scour occurring is low. On the other hand, natural frequencies in the range below the dashed line can be determined to be category A1 or A2, which means that the possibility of scour occurring is high. The detection device 100 can determine the possibility of scour occurring by displaying the statistical distribution of the natural frequencies along with the estimated natural frequencies. As in this example, by indicating the range of the evaluation index, the soundness can be immediately diagnosed based on the estimated natural frequencies.

[0049] FIG. 4A shows an example of the estimated distribution of natural frequencies. The vertical axis indicates the natural frequency [Hz], and the horizontal axis indicates the date of acquisition of the data. The detection device 100 in this example displays the time series change in the natural frequency. The natural frequency in this example is estimated using a Bayesian actual operating mode identification method using a fast Bayesian FFT based on Bayesian estimation.

[0050] The Bayesian operational modal identification method using fast Bayesian FFT is an operational modal analysis method using frequency-domain microtremor data based on Bayesian theory. In Bayesian theory, the parameters that make up the statistical model are evaluated as probability distributions, so information about the uncertainty of the estimated values ​​can be directly and quantitatively estimated from the observed values. Using the estimated results, it is possible to calculate the signal-to-noise ratio (S / N ratio) to evaluate the influence of observation errors for each modal response.

[0051] When a set of suitable observed data D is obtained, we consider the problem of estimating the governing parameter θ of the statistical model in which that data arises. In Bayesian estimation, the parameter θ is estimated using the probability density as a measure. A probability distribution of the parameter θ, called the prior distribution, is assumed from information prior to the observation data D being obtained, and its probability density function is represented as p(θ). The probability distribution of the parameter θ estimated from the observation data D is called the posterior distribution, and is expressed as the conditional probability density function p(θ|D) based on the following Bayes' theorem.

number

[0052] In the Bayesian operational modal analysis method, the PSD (Power Spectral Density) curve obtained by performing a Fast Fourier Transform (FFT) on the measured values ​​of the acceleration, velocity, and displacement of a structure is used as the observed data D, and various parameters related to the vibration mode are used as the governing parameters θ of the statistical model. When a large number of observed values ​​are obtained, such as FFT data of acceleration time series in long-term measurements, it is known that the likelihood function p(D|θ) dominates the posterior distribution p(θ|D), and the prior distribution term can be ignored. If we assume that a non-informative prior distribution is applied as the prior distribution, the probability density function of the posterior distribution is proportional to the likelihood function. In other words, the following proportional relationship holds:

number

[0053] Therefore, for data D observed in vibration measurements, the posterior distribution of θ can be estimated by formulating the likelihood function p(D|θ) as an appropriate function of the parameter θ related to the vibration mode. The θ that maximizes the posterior distribution p(θ|D) is called the Most Probable Value (MPV), and in cases such as Equation 2, which assume an uninformative prior distribution, it coincides with the θ that maximizes the likelihood function p(D|θ). Here, θ in the posterior distribution p(θ|D) is composed of modal frequencies, modal damping, and S / N ratios, and the natural frequency is estimated by calculating the θ that maximizes the likelihood function p(D|θ). This point estimation method, which calculates an estimate of θ that maximizes the likelihood function, is known as maximum likelihood estimation, and many vibration mode identification methods, including stochastic subspace methods, are based on the least-squares method, which is one type of estimation problem. One of the advantages of introducing a Bayesian approach to vibration mode identification is that it is possible to estimate the degree of uncertainty in the most probable value (MPV) estimation results by evaluating the posterior distribution of vibration characteristics.

[0054] Next, we will explain how to identify vibration characteristics using the stochastic subspace method. The target structure 150 can be expressed as a discretized state equation as follows:

number

[0055] where, TIFF0007770646000005.tif727 shows the observed value at point m of the structure, and C is a matrix that extracts the observed value y(k) from the state variable x(k), TIFF0007770646000006.tif626. The coefficient matrix, external force vector, and observation matrix are expressed by the following equations, respectively. TIFF0007770646000007.tif2249

[0056] When performing system identification using vibration data from bridges and other structures, it is often difficult to observe the external forces that are the inputs to the system due to environmental effects such as river flow loads. Therefore, the Operational Modal Analysis (OMA) method is applied, which treats the inputs as unknown and uses only the observed values ​​of the outputs. In this case, the external forces are considered to be a time series of disturbance noise that is specific to the system. Due to stationarity and linearity of the process, this disturbance noise can be considered as stationary white noise. Furthermore, the observation noise when observing the output from the system can be expressed in a similar way. By formulating the above, the following equation is obtained:

number

number

[0057] δ pq is the Kronecker delta, and E[] is the mathematical mean. A similar process is performed for the observation noise v(k). The state variables in equation (4) can be given as the following equation using a non-stationary Kalman filter with one-period-ahead prediction based on the observation vector y(t):

number

[0058] However, here TIFF0007770646000011.tif612 is the estimated value of the state variable x(k) by the non-stationary Kalman filter. k-1 is a non-stationary Kalman gain that is updated each time a prediction is made based on information specific to the system. The mathematical mean of the error between this predicted value and the true value is minimized using a least squares approach, making it possible to algebraically estimate the vector space of the state variable vectors.

[0059] The oblique matrix O defined by the following equation i Think about it.

number

[0060] however, Let TIFF0007770646000013.tif711 represent the Moore-Penrose pseudoinverse matrix. Also, Y p , Y t are block Hankel matrices that contain past and future information of the observations, respectively, and are defined as follows:

number

number

[0061] Furthermore, the system matrix is ​​assumed to be observable, meaning that the governing parameters can be uniquely determined from the input and output data of the system. "Observability" means that the current state can be described from past responses. The observable matrix of the discretized state equation is expressed as follows:

number

[0062] rank(T p ), the system becomes observable. However, here, let m be the degrees of freedom of the output vector y(t) and n be the degrees of freedom of the dynamic system, and let m × p = 2n be true. In other words, p indicates the relationship between 2n state variables and m observation points, and represents the correspondence between 2n state variables and m observation points with a time delay of p points. Therefore, the state equation for 2n variables can be expressed as a p-th-delayed difference equation for m observation variables.

[0063] Here, Y p , Y fWhen j is sufficiently large, the observable matrix T defined by (Eq. 10) p Using this, it can be expressed as follows:

number

[0064] TIFF0007770646000018.tif76 is a vector of estimated state variables using the Kalman filter. This is the time series of TIFF0007770646000019.tif612. That is, the matrix O i The row space of the matrix This corresponds to the row space of TIFF0007770646000020.tif76. i By performing singular value decomposition as shown in the following equation, the following equation is obtained.

number

[0065] Here, W1 and W2 are appropriate weight matrices. Both of these weight matrices can be set as unit matrices of appropriate size. In singular value decomposition, matrices U and V are unitary matrices, and the singular values ​​of matrix S are arranged in the diagonal elements in descending order of absolute value. In other words, the basis vectors corresponding to the row and column spaces that make up the matrix are arranged in descending order of their contribution to each element of the matrix. By utilizing this property, the original matrix can be effectively approximated by a matrix of lower rank.

[0066] The time series of the estimated values ​​of the state variables based on the singular value decomposition of Eq. (12) TIFF0007770646000022.tif76 and its one-stage ahead time series TIFF0007770646000023.tif711 can be calculated, and the coefficient matrix of the state space model expressed by Eq. (4) can be calculated. TIFF0007770646000024.tif710 is identified by the following formula:

number

number

[0067] The detection device 100 may diagnose the health of the target structure 150 based on the time-series change in the natural frequency. The detection device 100 may also simultaneously display the range of the health diagnostic index. The detection device 100 may also display the average value or standard deviation of the natural frequency over a predetermined period.

[0068] FIG. 4B shows the time history of the natural frequency and a histogram corresponding to the natural frequency. The vertical axis indicates the estimated value of the natural frequency [Hz], and the horizontal axis indicates time. The time history of the estimated value of the natural frequency in this example shows the average value over a predetermined period. The detection device 100 in this example shows the time history variation over 12 months, but is not limited to this.

[0069] For example, the natural frequency when the structure is healthy is 9.0 Hz, and the diagnostic threshold is 7.65 Hz, which corresponds to Category B. The detection device 100 of this example can diagnose the health of the structure and evaluate the reliability of the estimated natural frequency by simultaneously displaying the natural frequency when the structure is healthy and the diagnostic threshold.

[0070] Figure 5 is a graph showing the estimated natural frequency, the PSD of the modal external force, the S / N ratio, and the time history fluctuation of the water level. The detection device 100 in this example shows the time history fluctuation over 24 hours, but is not limited to this. The scour detection device 100 estimates the PSD of the modal external force and the time series data observation error. Using these estimation results, the scour detection device 100 can calculate the signal-to-noise ratio (S / N ratio) to evaluate the influence of the observation error for each modal response.

[0071] Referring to Figure 5, as the water level rises, the PSD and S / N ratio of the modal external forces increase, and the variance in the estimated values ​​of the natural frequencies decreases. This indicates that signals corresponding to the modal responses of the target structure are clearly present in the measured acceleration time series. In other words, the reliability of the target frequency identification results can be evaluated as high. In this way, the detection device 100 can evaluate the reliability of the estimates using the variance in the estimated values ​​of the natural frequencies, the PSD of the modal external forces, and the magnitude of the S / N ratio.

[0072] The detection device 100 may also filter data using the calculated diagnostic index. For example, the detection device 100 filters the natural frequency using at least one of the PSD and S / N ratio of the modal external force. This allows the reliability of the estimated value to be evaluated based on more reliable data.

[0073] Here, the data acquisition unit 10 in this example acquires time-series measurement data from the target structure 150. The index calculation unit 30 analyzes the vibration characteristics of the target structure 150 based on the time-series measurement data and calculates diagnostic indexes related to the soundness of the target structure 150 in time series. The diagnosis unit 40 diagnoses the soundness of the target structure 150 based on the diagnostic indexes calculated in time series. Then, the output unit 50 outputs the soundness diagnosis results in time series.

[0074] In this way, the detection device 100 of this example can output a time-series diagnostic index based on time-series measurement data. In other words, the detection device 100 of this example can acquire vibration data without applying an external shock every time a diagnosis is performed.

[0075] As described above, the detection device 100 can diagnose the soundness of the target structure 150 regardless of whether flooding has occurred or whether vehicles are passing through. Therefore, the range of application of the detection device 100 can be expanded while maintaining the accuracy of estimating the natural frequency. Furthermore, the scour diagnosis method using the detection device 100 can achieve a small number of sensors.

[0076] 6A shows an example of an operational flowchart of a detection method using detection device 100. In step S100, it is determined whether a predetermined vibration has occurred. If the predetermined vibration has not occurred, data from sensor 110 is acquired as continuous micro-tremor vibration data (S102). On the other hand, if the predetermined vibration has occurred, it is determined whether the predetermined vibration has subsided (S104), and if it has subsided, data from sensor 110 is acquired as free damping vibration data (S106).

[0077] In step S110, an estimation process is performed on the vibration characteristics of the target structure 150 based on the data acquired by the sensor 110. A specific example of the estimation process will be described later. In step S120, a diagnosis process is performed on the soundness of the target structure 150. A specific example of the diagnosis process will be described later. In step S130, the diagnosis result is output.

[0078] For example, the detection device 100 may output at least one diagnostic index, such as the evaluation index x, the PSD of the modal external force, or the S / N ratio, in addition to the natural frequency. Furthermore, the detection device 100 may output structural environment information indicating the environmental conditions of the target structure 150 as a diagnostic index. For example, the structural environment information may be additional information indicating the conditions of a river, such as water level or rainfall. Displaying the diagnostic index and its statistical distribution makes it possible to directly evaluate the uncertainty of the estimated value, thereby improving reliability.

[0079] Fig. 6B shows an example of the estimation process in step S110 in Fig. 6A. In step S112, the natural frequency is calculated from each piece of data. In step S114, the natural frequency is estimated using the stochastic subspace method or fast Bayes FFT, and a statistical distribution is calculated.

[0080] 6C shows an example of the diagnosis process of step S120 in FIG. 6A. In step S122, an evaluation index is calculated from the estimated value of the natural frequency and the natural frequency in a healthy state. In step S124, the soundness of the target structure 150 is determined based on the calculated evaluation index.

[0081] (Explanation of warning probability index value) Here, a method for determining the possibility of scouring occurrence using an alarm issuance probability index value will be described. The detection device 100 sets an evaluation index for the natural frequency and calculates an alarm issuance probability index value for the estimation result of the natural frequency for a certain period. For example, the detection device 100 sets an evaluation index for the natural frequency when the structure is healthy and calculates an alarm issuance probability index value for the estimation result of the natural frequency for a certain period. Specifically, the detection device 100 derives a probability density function using the probability characteristics of the natural frequency obtained by fast Bayes FFT when the structure is healthy (normal), and calculates an alarm issuance probability index value. Note that in this example, a case will be described in which the target structure 150 is a bridge pier, but the target structure 150 is not limited to this.

[0082] First, a probability density function is derived for the statistical distribution of natural frequencies (distribution of MPV of frequencies) estimated during normal operation. For example, the parameter values ​​of the probability density curve of the stable distribution are identified using the maximum likelihood estimation method. The characteristic function E(e itZ ) is given by the following equation. The detection device 100 obtains E(e itZ ) is calculated. TIFF0007770646000027.tif17129The probability density function of a stable distribution is expressed as S(α,β,γ,d;Z).

[0083] Table 2 shows the parameters used in the probability density function of a stable distribution. The first shape parameter, α, controls the thickness of the tail of the probability density function and is called the characteristic index. As the shape parameter α becomes smaller, the tail of the probability density function becomes thicker. The second shape parameter, β, represents the skewness of the distribution. When β = 0, the distribution is symmetric. When β > 0, the probability density function has a long right tail. When β < 0, the probability density function has a long left tail. The scale parameter, γ, controls the spread of the probability density function and is called the magnitude index. The location parameter, d, represents the mean value of the probability density function. Z represents the random variable, and t represents the argument of the characteristic function. [Table 2]

[0084] Figure 6D shows the probability density function of the stable distribution identified from the statistical distribution of the natural frequencies estimated under healthy (normal) conditions (distribution of MPV of the frequencies).

[0085] The solid line shows the probability density function of the target frequency of the pier estimated under normal conditions. This probability density function is considered to be the distribution characteristic of the natural frequency of the pier estimated under normal conditions, and the cumulative probability density function Ψ(α N ,β N ,γ N ,d N , X) is calculated, and the "alert issuance probability index value" is set when the estimated natural frequency of the bridge pier drops to a certain value. This makes it possible to determine whether there is a risk of disaster due to scouring based on the calculated alarm issuance probability index value.

[0086] For example, if the natural frequency when healthy is 9.39 Hz, the warning probability index value will be 0.0027 when the estimated natural frequency drops to 7.98 Hz. The warning probability index value when the average value of the estimated natural frequency of a bridge pier during flooding is X can be defined as follows: Ψ C =Ψ(α N ,β N ,γ N,d N ,X)

[0087] Table 3 shows the relationship between the health index value and the warning issuance probability index value. [Table 3]

[0088] For example, if the threshold value for the warning probability index is set to 0.01, and if the warning probability index value falls during times such as high water levels and reaches this value, it is determined that there is a risk of a disaster due to scouring. This threshold value corresponds to a value greater than the limit value (7.98 Hz, κ = 0.85) for judgment rank B of the soundness judgment category shown in Table 3. This indicates that the threshold is set so that there is a high probability of a Type 1 error (judging that scouring has occurred despite the fact that the river is sound) and a low probability of a Type 2 error (judging that the river is sound despite scouring having occurred).

[0089] Figure 6E shows the probability density function curve identified from the statistical distribution of the natural frequency estimated during flooding (distribution of MPV of the frequency). As the distribution characteristics of the natural frequency of the bridge pier differ in this way between normal times and flooding, it is also possible to calculate the warning issuance probability index value taking into account the different distribution characteristics during flooding. The probability density function during flooding is S F (α F ,β F ,γ F ,d F ;Z), the warning issuance probability index value taking into account different distribution characteristics during floods is calculated as follows: TIFF0007770646000030.tif15141

[0090] FIG. 6F is an example of a display of the time history of the alarm issuance probability index value and water level [m]. In this example, a threshold value (e.g., 0.01) for the alarm issuance probability index value is shown. In this example, the alarm issuance probability index value obtained from the estimated natural frequency does not fall below the threshold value, so the result is that the target bridge pier is sound. The detection device 100 in this example can visualize the possibility of an alarm being issued by graphing the alarm issuance probability index value.

[0091] In the above-described embodiment, the alarm issuance probability index value is calculated using the probability characteristics of the natural frequency calculated by the fast Bayes FFT. However, the embodiment is not limited to this. The alarm issuance probability index value may be calculated using the probability characteristics of the natural frequency calculated by a method other than the fast Bayes FFT. For example, instead of the fast Bayes FFT, a probability density function may be derived using the probability characteristics of the natural frequency calculated by a stochastic subspace identification (SSI), a multidimensional AR model (VAR), a frequency domain decomposition (FDD), an eigensystem realization algorithm (ERA), or a normal FFT, and the alarm issuance probability index value may be calculated. For example, when calculating the alarm issuance probability index value using the stochastic subspace identification (SSI), a multidimensional AR model (VAR), a frequency domain decomposition (FDD), an eigensystem realization algorithm (ERA), or a normal FFT, the detection device 100 calculates mode-related parameters (e.g., mode shape and poles (frequency and damping constant may be calculated from the poles)) using A and C in Equation (13).

[0092] 7 shows an example of a computer 2200 in which aspects of the present invention may be embodied, in whole or in part. A program installed on the computer 2200 may cause the computer 2200 to function as or perform operations associated with an apparatus according to an embodiment of the present invention or one or more sections of the apparatus, and / or to perform a process or steps of a process according to an embodiment of the present invention. Such a program may be executed by the CPU 2212 to cause the computer 2200 to perform specific operations associated with some or all of the blocks in the flowcharts and block diagrams described herein.

[0093] A computer 2200 according to this embodiment includes a CPU 2212, a RAM 2214, a graphics controller 2216, and a display device 2218, which are interconnected by a host controller 2210. The computer 2200 also includes input / output units such as a communication interface 2222, a hard disk drive 2224, a DVD-ROM drive 2226, and an IC card drive, which are connected to the host controller 2210 via an input / output controller 2220. The computer also includes legacy input / output units such as a ROM 2230 and a keyboard 2242, which are connected to the input / output controller 2220 via an input / output chip 2240.

[0094] The CPU 2212 operates according to programs stored in the ROM 2230 and RAM 2214, thereby controlling each unit. The graphics controller 2216 acquires image data generated by the CPU 2212 into a frame buffer or the like provided in the RAM 2214 or into the graphics controller 2216 itself, and causes the image data to be displayed on the display device 2218.

[0095] The communication interface 2222 communicates with other electronic devices via a network. The hard disk drive 2224 stores programs and data used by the CPU 2212 in the computer 2200. The DVD-ROM drive 2226 reads programs or data from the DVD-ROM 2201 and provides the programs or data to the hard disk drive 2224 via the RAM 2214. The IC card drive reads programs and data from an IC card and / or writes programs and data to an IC card.

[0096] The ROM 2230 stores therein a boot program or the like that is executed by the computer 2200 upon activation, and / or programs that depend on the hardware of the computer 2200. The input / output chip 2240 may also connect various input / output units to the input / output controller 2220 via a parallel port, a serial port, a keyboard port, a mouse port, etc.

[0097] The programs are provided by a computer-readable medium such as a DVD-ROM 2201 or an IC card. The programs are read from the computer-readable medium, installed in the hard disk drive 2224, RAM 2214, or ROM 2230, which are also examples of computer-readable media, and executed by the CPU 2212. The information processing described in these programs is read by the computer 2200, and brings about cooperation between the programs and the various types of hardware resources described above. An apparatus or method may be configured by realizing information manipulation or processing in accordance with the use of the computer 2200.

[0098] For example, when communication is performed between the computer 2200 and an external device, the CPU 2212 may execute a communication program loaded into the RAM 2214 and instruct the communication interface 2222 to perform communication processing based on the processing described in the communication program. Under the control of the CPU 2212, the communication interface 2222 reads transmission data stored in a transmission buffer processing area provided in the RAM 2214, the hard disk drive 2224, the DVD-ROM 2201, or a recording medium such as an IC card, and transmits the read transmission data to the network, or writes reception data received from the network to a reception buffer processing area or the like provided on the recording medium.

[0099] The CPU 2212 may also read all or a necessary portion of a file or database stored on an external recording medium such as the hard disk drive 2224, the DVD-ROM drive 2226 (DVD-ROM 2201), an IC card, etc. into the RAM 2214, and perform various types of processing on the data on the RAM 2214. The CPU 2212 then writes back the processed data to the external recording medium.

[0100] Various types of information, such as various types of programs, data, tables, and databases, may be stored on the recording medium and subjected to information processing. The CPU 2212 may perform various types of processing on data read from the RAM 2214, including various types of operations, information processing, conditional judgment, conditional branching, unconditional branching, information search / replacement, etc., as described throughout this disclosure and specified by the instruction sequences of the program, and write the results back to the RAM 2214. The CPU 2212 may also search for information in a file, database, etc. on the recording medium. For example, if multiple entries each having an attribute value of a first attribute associated with an attribute value of a second attribute are stored on the recording medium, the CPU 2212 may search for an entry that matches a condition specified by the attribute value of the first attribute from among the multiple entries, read the attribute value of the second attribute stored in the entry, and thereby obtain the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition.

[0101] The above-described programs or software modules may be stored in a computer-readable medium on or near the computer 2200. Furthermore, a recording medium such as a hard disk or RAM provided in a server system connected to a dedicated communication network or the Internet can be used as a computer-readable medium, thereby providing the programs to the computer 2200 via the network.

[0102] Although the present invention has been described above using embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It will be apparent to those skilled in the art that various modifications and improvements can be made to the above embodiments. It is clear from the claims that such modifications and improvements can also be included within the technical scope of the present invention.

[0103] It should be noted that the execution order of each process, such as the operations, procedures, steps, and stages, in the devices, systems, programs, and methods shown in the claims, specifications, and drawings, is not specifically stated as "before" or "prior to," and that the processes can be performed in any order unless the output of a previous process is used in a later process. Even if the operational flow in the claims, specifications, and drawings is explained using "first," "next," etc. for convenience, this does not mean that it is essential to perform the process in that order. [Explanation of symbols]

[0104] 10···Data acquisition unit, 20···Information acquisition unit, 30···Index calculation unit, 40···Diagnosis unit, 50···Output unit, 60···Memory unit, 100···Detection device, 110···Sensor, 150···Target structure, 152···Top, 2200···Computer, 2201···DVD-ROM, 2210···Host controller, 2212···CPU, 2214···RAM, 2216···Graphics controller, 2218···Display device, 2220···Input / output controller, 2222···Communication interface, 2224··Hard disk drive, 2226··DVD-ROM drive, 2230···ROM, 2240···Input / output chip, 2242···Keyboard

Claims

1. a data acquisition unit that acquires measurement data from the target structure; an index calculation unit that analyzes vibration characteristics of the target structure based on the measurement data and calculates a diagnostic index related to the soundness of the target structure; a diagnostic unit that diagnoses the soundness of the target structure based on the diagnostic index; an output unit that outputs the diagnosis result of the health; Equipped with the index calculation unit calculates an evaluation index of the soundness based on the estimated value of the natural frequency estimated based on the measurement data and the natural frequency in a sound state; the output unit displays the range of the evaluation index for the category related to the soundness of the target structure superimposed on the statistical distribution of the estimation result of the natural frequency, the diagnostic index is an index for diagnosing the soundness of the target structure based on the vibration characteristics of the target structure, and includes the natural frequency and the statistical distribution; A detection device in which the evaluation index is an index for evaluating the soundness of the target structure.

2. The output unit outputs at least one of the measurement data, the diagnostic index, and the diagnostic result. The detection device of claim 1 .

3. a storage unit for storing at least one of the measurement data, the diagnostic index, and the diagnostic result; The detection device according to claim 1 or 2.

4. The evaluation index is x that satisfies the following formula: x = Estimated natural frequency / Natural frequency in healthy condition A detection device according to any one of claims 1 to 3.

5. The diagnostic index includes a natural frequency based on measurement data of the target structure and an alarm issuance probability index value based on a probability density function identified from the statistical distribution of the natural frequency. A detection device according to any one of claims 1 to 4.

6. The index calculation unit calculates the statistical distribution of the natural frequency by fast Bayes FFT. A detection device according to any one of claims 1 to 5.

7. The data acquisition unit acquires free damping vibration data after a predetermined vibration occurs in the target structure. A detection device according to any one of claims 1 to 6.

8. The data acquisition unit acquires constant microtremor vibration data when a predetermined vibration is not occurring in the target structure. A detection device according to any one of claims 1 to 7.

9. The output unit displays the diagnostic index and the statistical distribution of the diagnostic index. A detection device according to any one of claims 1 to 8.

10. The output unit displays the diagnostic index, the statistical distribution of the diagnostic index, and the probability density function identified from the statistical distribution. A detection device according to any one of claims 1 to 9.

11. an information acquisition unit that acquires information for analyzing the vibration characteristics of the target structure; A detection device according to any one of claims 1 to 10.

12. The data acquisition unit acquires time-series measurement data from the target structure, the index calculation unit analyzes vibration characteristics of the target structure based on the time-series measurement data and calculates diagnostic indices related to the soundness of the target structure in time series; the diagnosis unit diagnoses the soundness of the target structure based on the diagnostic index calculated in time series; The output unit outputs the health diagnosis results in time series. A detection device according to any one of claims 1 to 11.

13. The output unit further displays structural environment information indicating the environmental status of the target structure. Detecting device according to any one of claims 1 to 12.

14. A detection method for detecting the soundness of a target structure using a detection device, comprising: acquiring measurement data from a target structure; a step of analyzing vibration characteristics of the target structure based on the measurement data and calculating a diagnostic index related to the soundness of the target structure; diagnosing the soundness of the target structure based on the diagnostic index; outputting the health diagnosis result; Equipped with the step of calculating the diagnostic index includes a step of calculating an evaluation index of the health level based on the estimated value of the natural frequency estimated based on the measurement data and the natural frequency in a healthy state, The step of outputting the diagnosis result of the soundness includes a step of displaying the range of the evaluation index for the category related to the soundness of the target structure by superimposing it on the statistical distribution of the estimation result of the natural frequency, the diagnostic index is an index for diagnosing the soundness of the target structure based on the vibration characteristics of the target structure, and includes the natural frequency and the statistical distribution; A detection method in which the evaluation index is an index for evaluating the soundness of the target structure.

15. The evaluation index is x that satisfies the following formula: x = Estimated natural frequency / Natural frequency in healthy condition The detection method according to claim 14.

16. A program for causing a computer to execute the detection method according to claim 14 or 15.

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