Structural Deterioration Diagnostic System

Abstract

To provide a structure deterioration diagnosis system that performs deterioration diagnosis on a structure on the basis of a feature value that is not dependent on temperature.SOLUTION: A structure deterioration diagnosis system comprises: a sensor which is provided on a structure being diagnosed for deterioration and outputs acceleration information for the structure; and a sensor controller which determines whether deterioration of the structure has occurred on the basis of the acceleration information output from the sensor. The sensor controller calculates, as a feature value, an active load displacement which does not vary depending on temperature as an index value for determining deterioration of the structure on the basis of the acceleration information. The sensor controller also calculates an abnormality level to serve as a deterioration determination index of the structure on the basis of time series data of the feature value, and if the abnormality level exceeds a threshold, it determines that deterioration of the structure has occurred.SELECTED DRAWING: Figure 7

Description

[Technical Field]

[0001] The present disclosure relates to a structure deterioration diagnosis system that can identify whether deterioration has occurred in a structure such as a bridge or whether an abnormality has occurred in a sensor when diagnosing the deterioration of the structure using acceleration information detected by a sensor. [Background technology]

[0002] For example, structures such as bridges over which vehicles pass will gradually deteriorate over time as vehicles pass over them, so it is important to detect the deterioration of structures such as bridges before they break.

[0003] One prior art technique for diagnosing bridge deterioration involves diagnosing sensor abnormalities (failures) from changes in the inclination or natural frequency calculated based on multiple sensors (see, for example, Patent Document 1). Specifically, when the ratio of probability density distributions based on the detection results of multiple sensors deviates from an allowable range, it becomes possible to diagnose a failure in the sensor itself. As a result, it is possible to realize a structure deterioration diagnosis system that can ensure the reliability of deterioration diagnosis after confirming the soundness of the sensors. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 6081867 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the conventional techniques have the following problems. In Patent Document 1, the detection results of a plurality of sensors are compared with each other to diagnose abnormalities (failures) in the sensors. In other words, in order to identify whether deterioration of the structure or an abnormality in the sensor has occurred, it is necessary to compare the detection results of the plurality of sensors.

[0006] Furthermore, when comparing the probability density distribution of the slope between multiple sensors, it may not be possible to determine whether the problem is a structural abnormality or a sensor failure, depending on the influence of the sensor position or the temperature characteristics of the sensor.

[0007] The present disclosure has been made to solve the above-mentioned problems, and aims to provide a structural deterioration diagnosis system that can distinguish between a structural abnormality and a sensor failure from the detection results of a single sensor. [Means for solving the problem]

[0008] The structure deterioration diagnosis system according to the present disclosure includes a sensor that is installed in a structure that is a deterioration diagnosis target and outputs acceleration information of the structure, and a sensor controller that determines whether deterioration of the structure has occurred based on the acceleration information output from the sensor. Vibration component without DC component in Based on Calculate the live load displacement, and obtain the time series data of the most frequent value of the maximum displacement from the time transition of the live load displacement, which does not change depending on the temperature. Calculate it as a feature, Mode Based on the time series data, the degree of abnormality, which is an index for determining deterioration of the structure, is calculated, and if the degree of abnormality exceeds a threshold, it is determined that deterioration of the structure has occurred. [Effects of the Invention]

[0009] According to the present disclosure, it is possible to obtain a structure deterioration diagnosis system that can distinguish between a structure abnormality and a sensor failure from the detection result of one sensor. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a configuration diagram of a structure deterioration diagnosis system according to a first embodiment of the present disclosure. [Figure 2] 1 is an explanatory diagram showing a state in which sensors are installed in a structure that is a diagnosis target of a structure deterioration diagnosis system according to a first embodiment of the present disclosure. [Figure 3]10A to 10C are diagrams illustrating a time transition of a slope, a relationship between the slope and temperature, and a slope abnormality degree when the slope is used as a feature amount in the first embodiment of the present disclosure. [Figure 4] 10A to 10C are diagrams illustrating a time transition of the natural frequency, a relationship between the natural frequency and temperature, and a natural frequency anomaly degree when the natural frequency is used as a feature amount in the first embodiment of the present disclosure. [Figure 5] FIG. 10 is a diagram showing the time progression of live load displacement, the relationship between live load displacement and temperature, and the live load displacement abnormality degree when live load displacement is used as a feature quantity in the first embodiment of the present disclosure. [Figure 6] FIG. 10 is an explanatory diagram illustrating a case where a tilt, which is one of the feature amounts, is calculated using triaxial acceleration information output from a sensor according to the first embodiment of the present disclosure. [Figure 7] FIG. 10 is an explanatory diagram showing a case where only the tilt anomaly degree is in an abnormal state, but the natural frequency anomaly degree and the live load displacement anomaly degree are not in an abnormal state, in the first embodiment of the present disclosure. [Figure 8] FIG. 10 is a diagram comparing a histogram of each feature amount when the abnormality degrees for all feature amounts are determined to be in a normal state with a histogram of each feature amount when only the slope abnormality degree is determined to be in an abnormal state in the first embodiment of the present disclosure. [Figure 9] FIG. 10 is an explanatory diagram for comparing a histogram of the degree of inclination abnormality with a histogram of natural frequencies using a statistical method in the first embodiment of the present disclosure. [Figure 10] 1 is a flowchart showing a series of processes for identifying whether there is a structural abnormality or a sensor failure, which are executed by the structure deterioration diagnosis system according to the first embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, preferred embodiments of the structure deterioration diagnosis system of the present disclosure will be described with reference to the drawings. The present disclosure has a technical feature of having the ability to distinguish between a structural abnormality and a sensor failure by determining multiple feature values ​​from the detection results of one sensor, and further determining the degree of abnormality corresponding to each of the multiple feature values, and performing deterioration diagnosis using a combination of the multiple degrees of abnormality.

[0012] Embodiment 1 In the first embodiment, a specific configuration for distinguishing between a structural abnormality and a sensor failure when diagnosing deterioration of a structure based on the measurement results from one sensor will be described.

[0013] 1 is a configuration diagram of a structure deterioration diagnosis system according to a first embodiment of the present disclosure. The structure deterioration diagnosis system according to the first embodiment is configured to include one sensor 10 and a sensor controller 20.

[0014] 2 is an explanatory diagram showing a state in which a sensor 10 is installed in a structure that is a target for deterioration diagnosis by the structure deterioration diagnosis system according to the first embodiment of the present disclosure. In FIG. 2, a bridge 30 is shown as a specific example of the structure, with FIG. 2(A) being a side view of the bridge 30 and FIG. 2(B) being a rear view of the bridge 30.

[0015] The bridge 30 gradually deteriorates over time due to the passage of vehicles 1. Therefore, the structure deterioration diagnosis system according to the first embodiment diagnoses the ever-changing deterioration state of the bridge 30 by using a sensor controller 20 to analyze acceleration information output by a sensor 10 installed at a position suitable for deterioration diagnosis.

[0016] Furthermore, the sensor controller 20 according to the first embodiment has a function of determining whether or not a single sensor 10 is malfunctioning, based on the acceleration information output by the sensor 10.

[0017] The sensor 10 is installed on a main girder 31, which is a component of a bridge 30. Here, the main girder 31 corresponds to the structural element of the structure to be diagnosed. As shown in FIG. 2(B), the main girder 31 is configured as three main girders 31a, 31b, and 31c, for example. The example in FIG. 2(B) illustrates a case where the sensor 10 is installed in the center of the main girder 31b (i.e., the center of the span corresponding to the distance between the left and right supports 2). This installation position corresponds to an example of a position suitable for deterioration diagnosis.

[0018] The following description will discuss a specific method for diagnosing deterioration based on the detection results of one sensor 10 and distinguishing between a structural abnormality and a sensor failure. However, the sensor 10 itself may be installed in a position different from that in the first embodiment, or multiple sensors 10 may be installed in multiple locations on the bridge 30.

[0019] When multiple sensors 10 are installed, the sensor controller 20 can identify for each sensor 10 whether a structural abnormality has been detected or whether the sensor has failed, based on the individual detection results at the installation location of each sensor 10.

[0020] The sensor 10 detects acceleration information occurring on the bridge 30 and outputs the acceleration information to the sensor controller 20. One example of the sensor 10 is a three-axis acceleration sensor that uses a thin-film quartz crystal oscillator, has excellent responsiveness, and can measure accelerations in the measurement range of about DC to several tens of Hz.

[0021] In this way, by using a three-axis acceleration sensor as the sensor 10, leveling is not required and the sensor output can be obtained regardless of the direction of tilt or vibration. Therefore, if the direction of tilt can be identified by leveling or the like, a two-axis or one-axis acceleration sensor may be used.

[0022] Furthermore, the sensor 10 can convert analog signals relating to the three-axis acceleration of the structure at the installation location into digital signals at a predetermined sampling rate (for example, a sampling rate of 50 Hz) and transmit them to the sensor controller 20 as acceleration information.

[0023] The sensor controller 20, which receives the acceleration information output from the sensor 10, is configured to include a feature conversion unit 21, an abnormality degree calculation unit 22, and an abnormality / fault determination unit 23, as shown in FIG. 1 above.

[0024] The feature conversion unit 21 converts the acceleration information received from the sensor 10 into the tilt, natural frequency, and displacement of the structure at the installation position of the sensor 10. Here, the tilt, natural frequency, and displacement each correspond to a feature that serves as an index value for diagnosing the deterioration of the structure.

[0025] In the present embodiment, "displacement" refers to live load displacement. Here, live load refers to a load whose magnitude is not constant and whose position of action changes. Factors that cause displacement of such live load include the weight of the vehicle 1 passing over the bridge 30, the weight of the bridge 30 itself, and the inertial force acting on the bridge 30 due to an earthquake. In the following explanation, "displacement" and "live load displacement" are treated as synonyms.

[0026] The anomaly degree calculation unit 22 calculates the degree of anomaly for each of the multiple feature quantities, i.e., tilt, natural frequency, and live load displacement, generated by the conversion process by the feature quantity conversion unit 21. Below, a detailed explanation will be given of the method for calculating the degree of anomaly for each of tilt, natural frequency, and live load displacement.

[0027] 3A and 3B are diagrams showing the time progression of the slope, the relationship between the slope and temperature, and the slope abnormality degree when the slope is used as a feature quantity in the first embodiment of the present disclosure. FIG. 3A shows the time progression of the slope, with the vertical axis representing the slope and the horizontal axis representing the date. It can be seen that the feature quantity related to the slope changes depending on the temperature. Furthermore, FIG. 3A shows that it is difficult to set a threshold value for determining an abnormal state for the slope, which is a feature quantity.

[0028] Figure 3(B) shows the relationship between the slope and temperature, with the vertical axis representing the slope and the horizontal axis representing the temperature. Figure 3(B) shows the "prediction distribution," "normal data distribution," and "abnormal data distribution."

[0029] The "predictive distribution" is a distribution that can be created from the relationship between the slope and temperature when temperature is the input and the slope is the output, using time series data that shows the transition state of the slope calculated during the "learning period" shown in Figure 3(A).

[0030] Specifically, the anomaly degree calculation unit 22 uses Gaussian process regression to model the relationship between the input temperature and the output slope from the time series data obtained during the learning period in the form of a response surface or a regression line. Furthermore, the anomaly degree calculation unit 22 can create a "prediction distribution," for example, a 3σ range, from the modeled response surface or regression line.

[0031] In other words, the "prediction distribution" corresponds to a confidence interval determined by statistical processing using the covariance matrix of a Gaussian distribution estimated from time series data obtained during the learning period.

[0032] On the other hand, the "normal data distribution" and "abnormal data distribution" are distributions plotted from time series data collected during the "assessment period" after the "learning period" shown in Figure 3(A), and the distribution of data that falls within the range of the predicted distribution corresponds to the normal data distribution, while the distribution of data that falls outside the range of the predicted distribution corresponds to the abnormal data distribution.

[0033] One of the important features of using Gaussian process regression is its nonlinearity, which makes it effective when the input-output relationship cannot be fitted well by linear regression. Another important feature is that it uses Bayesian estimation, making it a nonparametric regression model.

[0034] Therefore, a specific method for calculating the degree of abnormality, which serves as an index for determining whether or not there is an "abnormal data distribution" state as shown in FIG. 3(B), will be further explained using mathematical formulas and FIG. 3(C).

[0035] The procedure for calculating the degree of anomaly using Gaussian process regression when the temperature is the input and the slope is the output is as follows: <Step 1> Consider N sets of training data, and then input the training data

number

number

number

[0036] <Step 2> Let the output of the response surface (or curve in the case of one dimension) at any coordinate x be f(x). If we assume that the model representing the noise during observation follows a normal distribution with a mean value of 0 and a variance of σ2, then we obtain the following equation (1).

number

[0037] <Step 3> The distribution of the response surface value f(x) for the input x is calculated from the data D.

number

number

[0038] <Step 4> For a model related to the smoothness of the response surface, let f(x) and f(x') be the values ​​of the response surface for arbitrary inputs x and x', respectively. At this time, f(x) and f(x') follow the probability distribution shown in the following equation (3).

number

[0039] Here, K is a kernel function, which intuitively expresses "how similar x and x' are." The RBF (radial basis function) kernel is often used as the kernel function.

[0040] Generally, N inputs are given by the following equation (4):

number

number

[0041] This is the basic assumption of the Gaussian process. However, if there are N inputs, K(x, x') becomes an N×N matrix, and if there are two inputs, it becomes a 2×2 matrix as shown in equation (3) above.

[0042] <Step 5> The degree of anomaly α(x') is the mean of the predictive distribution of GPR.

number

number

number

[0043] That is, the degree of anomaly corresponding to each feature amount can be calculated based on the predicted distribution (corresponding to the confidence interval) calculated during the learning period.

[0044] The degree of anomaly related to the slope calculated over the learning period and the evaluation period using the above procedure is the degree of slope anomaly shown in Figure 3(C). By setting an appropriate threshold and determining whether or not there is an anomaly degree that exceeds the threshold, it is possible to quantitatively determine whether the abnormal data distribution state shown in Figure 3(B) has occurred.

[0045] By performing such a series of processes, the abnormality degree calculation unit 22 can convert the slope, which is a feature amount, into an abnormality degree, which is an index value for determining with high accuracy whether or not an abnormal state exists. As a result, when using the slope as a feature amount, it has been difficult to set a threshold value for determining an abnormal state for the slope because the degree of temperature dependency varies from sensor to sensor. However, for the slope abnormality degree calculated from the slope, it is now possible to easily set a threshold value for determining an abnormal state.

[0046] That is, the sensor controller 20 has a function for making a determination using the degree of tilt abnormality, and is therefore able to identify an abnormal state based on the tilt with high accuracy, taking into account the influence of temperature fluctuations.

[0047] Next, a case where the natural frequency is used as the feature quantity will be described. Fig. 4 is a diagram showing the time transition of the natural frequency, the relationship between the natural frequency and temperature, and the natural frequency anomaly degree when the natural frequency is used as the feature quantity in the first embodiment of the present disclosure.

[0048] Figure 4(A) shows the time transition of the natural frequency, with the vertical axis representing the natural frequency and the horizontal axis representing the date. As with the slope, it can be seen that the feature quantity related to the natural frequency changes depending on the temperature. Therefore, as with the slope, it is clear that it is difficult to set a threshold value for determining an abnormal state in the case of the natural frequency as well.

[0049] Therefore, in the same way as in the case of the slope explained above with reference to FIG. 3, when the natural frequency is used as the feature, the relationship between the natural frequency and the temperature shown in FIG. 4(B) and the natural frequency anomaly degree shown in FIG. 4(C) can be obtained.

[0050] Although a detailed explanation will be omitted, by performing a series of processes similar to those in the case of the slope, the abnormality degree calculation unit 22 can convert the natural frequency, which is a feature quantity, into an abnormality degree, which is an index value for determining with high accuracy whether or not an abnormal state exists. As a result, when the natural frequency is used as a feature quantity, it has been difficult to set a threshold value for determining an abnormal state for the natural frequency, but for the natural frequency abnormality degree calculated from the natural frequency, it is now possible to easily set a threshold value for determining an abnormal state.

[0051] That is, the sensor controller 20 has a function for making a judgment using the natural frequency abnormality degree, and thus can accurately identify an abnormal state based on the natural frequency while taking into account the influence of temperature fluctuations.

[0052] Next, a case where live load displacement is used as a feature quantity will be described. Fig. 5 is a diagram showing the time progression of live load displacement, the relationship between live load displacement and temperature, and the live load displacement anomaly degree when live load displacement is used as a feature quantity in the first embodiment of the present disclosure.

[0053] Figure 5(A) shows the time transition of live load displacement, with the vertical axis representing live load displacement and the horizontal axis representing date. Unlike the inclination and natural frequency, the characteristic quantities related to live load displacement do not change depending on temperature. The reason why live load displacement does not change depending on temperature is that the most frequent value of maximum displacement is more influenced by factors such as the amount of travel, speed, and weight of the vehicle, while the influence of temperature is smaller.

[0054] As shown in Figure 5(B), even when using live load displacement, which is a feature that does not depend on temperature, the "prediction distribution" can be calculated as a distribution equivalent to the confidence interval using the covariance matrix of the Gaussian distribution estimated from the time series data obtained during the learning period.

[0055] As an example, when using live load displacement, which is a feature independent of temperature, the anomaly degree calculation unit 22 may use, for example, Hotelling's T2 method to calculate the anomaly degree α(x') using the following equation (7):

number

[0056] In the above formula (7), x' represents data, μ with a ^ attached represents the average value, and σ with a ^ attached represents the variance value.

[0057] When the live load displacement is used as the feature quantity, a threshold value for determining whether the live load displacement is in an abnormal state can be set as shown in FIG. 5(A).

[0058] Furthermore, as shown in Figure 5(C), in the same way as when the slope and natural frequency that change depending on the temperature are used as feature quantities, even when the live load displacement is used as the feature quantity, by calculating the live load displacement abnormality degree using the above equation (7) or the like, an abnormal state can be easily identified using a threshold value.

[0059] The thresholds for the tilt anomaly, natural frequency anomaly, and live load displacement anomaly can be set in advance, or they can be set statistically based on the results of time-series data acquired during the learning period. For example, if the standard deviation is σ, then 3σ or the value of the following equation (8) can be used as the threshold.

number

[0060] In this way, by comparing the degree of abnormality calculated based on the time-series data of the feature with the threshold value for the degree of abnormality, it is possible to easily quantitatively identify whether the feature indicates an abnormal state, regardless of whether the feature changes depending on the temperature.

[0061] Next, a method for distinguishing between a structural abnormality and a sensor failure, which is a technical feature of the structure deterioration diagnosis system according to the first embodiment, will be described. As described above, by using an index called the degree of abnormality, it is possible to easily distinguish whether a feature is in an abnormal state. However, such an abnormal state can occur both when a structure becomes abnormal and when a sensor fails.

[0062] Therefore, in the structure deterioration diagnosis system according to the first embodiment, the degree of tilt abnormality, the degree of natural frequency abnormality, and the degree of live load displacement abnormality calculated based on the three feature quantities of tilt, natural frequency, and live load displacement are used as deterioration diagnosis indices to identify whether the abnormality is in the structure or a sensor failure. This identification process will now be described in detail.

[0063] In the structure deterioration diagnosis system according to the first embodiment, a series of processes from [Process 1] to [Process 7] below is performed for three abnormality levels to distinguish between a structure abnormality and a sensor failure. [Process 1] Based on the acceleration information output from the sensor 10, the feature quantity conversion unit 21 calculates three feature quantities, namely, the tilt, natural frequency, and live load displacement of the structure.

[0064] [Process 2] The anomaly degree calculation unit 22 calculates the anomaly degree of tilt, the anomaly degree of natural frequency, and the anomaly degree of live load displacement for each of the three feature quantities, namely tilt, natural frequency, and live load displacement, as anomaly degrees that serve as indicators for determining deterioration of the structure.

[0065] [Process 3] The abnormality / failure determination unit 23 determines that an abnormal tilt has occurred if the degree of abnormal tilt exceeds the first threshold value. [Process 4] The abnormality / failure determination unit 23 determines that an abnormality in the natural frequency has occurred if the degree of abnormality in the natural frequency exceeds the second threshold value. [Process 5] The abnormality / fault determination unit 23 determines that a live load displacement abnormality has occurred if the live load displacement exceeds the third threshold value.

[0066] [Process 6] If the abnormality / failure determination unit 23 determines that an inclination abnormality has occurred and that neither a natural frequency abnormality nor a live load displacement abnormality has occurred, it determines that a sensor failure has occurred. [Process 7] The abnormality / failure judgment unit 23 determines that deterioration of the structure has occurred if it determines that at least one of a natural frequency abnormality and a live load displacement abnormality has occurred, regardless of whether an inclination abnormality has occurred or not.

[0067] The reason why process 6 and process 7 make it possible to distinguish between a structural abnormality and a sensor failure will be described with reference to Fig. 6. Fig. 6 is an explanatory diagram of a case where tilt, which is one of the feature quantities, is calculated using triaxial acceleration information output from sensor 10 according to the first embodiment of the present disclosure.

[0068] ax, ay, az are the acceleration values ​​for each axis. However, to calculate the static tilt excluding the effects of vibration, low-pass filtering is performed for each axis using a digital filter.

[0069] Therefore, the tilt is calculated from the direct current (DC) component of each axis, excluding the vibration component. This DC component may change due to the deterioration of electronic components such as the power supply inside the sensor 10. On the other hand, the natural frequency and live load displacement are calculated from the vibration rather than the DC component, and are therefore less susceptible to changes in the DC component due to sensor failure, etc.

[0070] Normally, if there is an abnormality in the tilt, it is possible that there is a decrease in rigidity, such as damage to the girders or supports, and that a decrease in rigidity will affect the live load displacement and natural frequency in the same way as the tilt. Therefore, if the results show that only the tilt is abnormal, it is likely that there is deterioration or damage to the electronic components inside the sensor, in other words, a failure of the sensor 10.

[0071] On the other hand, it is unlikely that a sensor failure will cause an abnormality only in the natural frequency. The reason for this is that when the natural frequency is used as an index for determining the failure state, it deals with vibration as mentioned above, and is therefore less susceptible to the influence of DC components.

[0072] Furthermore, the natural frequency is greatly affected by the moisture content of the entire bridge, the temperature of the entire bridge, the condition of the bearings, etc., and so varies greatly to begin with. Therefore, if an abnormality occurs in the natural frequency, it is assumed that the cause is a decrease in rigidity.

[0073] Similarly, it is unlikely that a sensor failure will cause an abnormality only in the live load displacement. The reason for this is that when live load displacement is used as an index for determining the failure state, the live load displacement is also calculated from the vibration component of the acceleration value (for example, around 1 Hz, which does not include the DC component), as mentioned above.

[0074] Therefore, the live load displacement is less susceptible to changes in the DC component due to deterioration of the internal electronic components, such as deterioration around the power supply. Therefore, if an abnormality occurs in the live load displacement, it is assumed that the cause is a decrease in the rigidity of the structure.

[0075] 7 is an explanatory diagram showing a case where only the tilt anomaly degree is in an abnormal state, but the natural frequency anomaly degree and the live load displacement anomaly degree are not in an abnormal state, in the first embodiment of the present disclosure. This case corresponds to the above-mentioned process 6, and the anomaly / failure determination unit 23 can determine that a failure has occurred in the sensor 10 because an tilt anomaly has occurred and neither the natural frequency anomaly nor the live load displacement anomaly has occurred.

[0076] In determining whether an inclination abnormality has occurred and neither natural frequency abnormality nor live load displacement abnormality has occurred, the abnormality / failure determination unit 23 can use a statistical determination method using a histogram or the like in addition to a determination method based on whether each abnormality degree has exceeded a threshold value. Therefore, the statistical determination method using a histogram will be described with reference to Figs. 8 and 9.

[0077] 8A and 8B are diagrams comparing histograms of feature quantities when the anomaly degrees for all feature quantities are determined to be normal and histograms of feature quantities when only the slope anomaly degree is determined to be abnormal, according to the first embodiment of the present disclosure. Comparing Fig. 8A with Fig. 8B, the frequency distribution of the slope anomaly degree is clearly different. Meanwhile, the distributions of the natural frequency anomaly degree and the live load displacement anomaly degree are not significantly different between Fig. 8A and Fig. 8B.

[0078] In this way, if only the anomaly of the slope increases, it is possible to determine whether only the anomaly calculated from the slope has increased by comparing the anomaly of the slope with the anomaly calculated from other physical quantities using a statistical method.Specific statistical methods that can be used include the T value from Welch's t-test or the density ratio.

[0079] 9 is an explanatory diagram of a case where a histogram of the degree of inclination abnormality and a histogram of the natural frequency are compared using a statistical method in the first embodiment of the present disclosure. (A) to (D) in FIG. 9 show the following diagrams. Figure 9(A): Histogram of each feature when the degree of abnormality for the two feature quantities, tilt and natural frequency, is judged to be normal. Figure 9(B): Histogram of each feature when, of the anomalies related to the two feature quantities, tilt and natural frequency, only the tilt anomaly is judged to be in an abnormal state, and the natural frequency anomaly is judged to be normal. Figure 9(C): Time series data of T values ​​from Welch's t-test calculated based on the histograms in Figures 9(A) and 9(B). Figure 9(D): Time series data of density ratio calculated based on the histograms in Figures 9(A) and 9(B).

[0080] The T-value of the Welch t-test can be calculated using the average value, variance, and number of samples for the slope anomaly and natural frequency anomaly, respectively, using the following formula (9):

number

[0081] The density ratio can be calculated using the probability density of the tilt anomaly and the probability density of the natural frequency anomaly according to the following equation (10).

number

[0082] 9(C) and 9(D), the T value and the density ratio are both larger in the transition state during the judgment period than in the transition state during the learning period. Therefore, the abnormality / fault judgment unit 23 can judge that only the degree of slope abnormality has become abnormal based on the result of comparing the transition state during the judgment period with a threshold value obtained from the transition state during the learning period or a preset threshold value.

[0083] In the example of FIG. 9 described above, a comparison is made using the inclination and natural frequency as feature quantities. However, a similar statistical method can also be applied to a comparison using the inclination and live load displacement as feature quantities, and detailed explanations thereof will be omitted.

[0084] Next, a series of processes for identifying whether a structural abnormality or a sensor failure is present from the detection result of one sensor by the structural deterioration diagnosis system according to the first embodiment will be described with reference to a flowchart. Fig. 10 is a flowchart showing a series of processes for identifying whether a structural abnormality or a sensor failure is present, which are performed by the structural deterioration diagnosis system according to the first embodiment of the present disclosure.

[0085] First, in step S1001, the feature transform unit 21 acquires acceleration information output from the sensor 10, and also sequentially acquires temperature information from the outside, which corresponds to the air temperature when the acceleration information was generated.

[0086] Next, in step S1002, the feature quantity conversion unit 21 converts the acceleration information into physical quantities (tilt, natural frequency, and live load displacement) corresponding to the three feature quantities.

[0087] Next, in step S1003, the anomaly degree calculation unit 22 calculates the tilt anomaly degree, the natural frequency anomaly degree, and the live load displacement anomaly degree based on the converted physical quantities. Note that when calculating the tilt anomaly degree and the natural frequency anomaly degree, the anomaly degree calculation unit 22 takes temperature information into consideration, thereby suppressing the influence of temperature-dependent changes in the determination process using the tilt anomaly degree and the natural frequency anomaly degree, and enabling stable determination process to be realized.

[0088] Next, in step S1004, the abnormality / failure determination unit 23 determines whether the tilt abnormality degree has become abnormal. If the abnormality / failure determination unit 23 determines that the tilt abnormality degree has become abnormal, the process proceeds to step S1007, and if the abnormality / failure determination unit 23 determines that the tilt abnormality degree has not become abnormal, the process proceeds to step S1005.

[0089] If the process proceeds to step S1005, the abnormality / failure determination unit 23 determines whether at least one of the live load displacement abnormality degree and the natural frequency abnormality degree has become abnormal. If the abnormality / failure determination unit 23 determines that at least one of the abnormality degrees has become abnormal, the process proceeds to step S1006, where it determines that a structural abnormality has occurred.

[0090] Furthermore, if the abnormality / failure judgment unit 23 determines in step S1005 that both the live load displacement abnormality degree and the natural frequency abnormality degree are in a normal state, all abnormality degrees are in a normal state, so the process returns to step S1001 and repeats the processes from step S1001 onwards.

[0091] If the process proceeds to step S1007, the abnormality / failure determination unit 23 determines whether or not both the live load displacement abnormality degree and the natural frequency abnormality degree are in a normal state. If the abnormality / failure determination unit 23 determines that at least one of the abnormality degrees has become abnormal, the process proceeds to step S1006, where it determines that a structural abnormality has occurred.

[0092] On the other hand, if the abnormality / failure judgment unit 23 judges in step S1007 that both the live load displacement abnormality degree and the natural frequency abnormality degree are in a normal state, it proceeds to the processing of step S1008, and determines that a sensor failure has occurred since only the tilt abnormality degree is in an abnormal state.

[0093] In addition, when the abnormality / failure judgment unit 23 is configured with multiple sensors 10 and judges that only the degree of tilt abnormality is in an abnormal state based on the acceleration information from each of two or more sensors 10, it is also possible to determine that a minor structural abnormality has occurred.

[0094] As described above, according to the first embodiment, the system is provided with a function of calculating multiple feature amounts from acceleration information detected by one sensor, determining the tilt anomaly degree, natural frequency anomaly degree, and live load displacement anomaly degree corresponding to each feature amount, and identifying whether there is a structural abnormality or a sensor failure from the combination of these anomaly degree states. In this way, by performing the identification process using multiple anomaly degrees calculated based on acceleration information from one sensor, it is possible to identify with high accuracy whether there is a structural abnormality or a sensor failure from the detection result of one sensor.

[0095] In the first embodiment described above, the case where three feature quantities, namely, tilt, natural frequency, and live load displacement, are used has been described, but the present invention is not limited to this. By using two feature quantities, with tilt as the first feature quantity and a feature quantity other than tilt as the second feature quantity, it is also possible to obtain a structure deterioration diagnosis system that can distinguish between a structural abnormality and a sensor failure from the detection result of a single sensor.

[0096] As the second feature, the natural frequency or live load displacement described in the first embodiment can be used, and any other feature can also be used as the second feature as long as it is an index value for diagnosing the deterioration of a structure.

[0097] In this case, if only an inclination abnormality occurs, it can be determined that a sensor failure has occurred, and if it is determined that a natural frequency abnormality or a live load displacement abnormality has occurred based on the second feature amount regardless of whether an inclination abnormality has occurred or not, it can be determined that deterioration of the structure has occurred. [Explanation of symbols]

[0098] 10 sensor, 20 sensor controller, 21 feature conversion unit, 22 anomaly degree calculation unit, 23 anomaly / fault determination unit.

Claims

1. a sensor that is installed in a structure that is a deterioration diagnosis target and outputs acceleration information of the structure; a sensor controller that determines whether deterioration of the structure has occurred based on the acceleration information output from the sensor; Equipped with The sensor controller Calculating a live load displacement based on vibration components not including a DC component in the acceleration information; calculating time series data of the most frequent value at the maximum displacement from the time transition of the live load displacement as a feature quantity that does not change depending on temperature; calculating an abnormality degree serving as an index for determining deterioration of the structure based on the time-series data of the mode; If the abnormality level exceeds a threshold, it is determined that deterioration of the structure has occurred. Structural deterioration diagnosis system.

2. The sensor controller determines that deterioration of the structure has occurred based on vibration components that do not include a DC component in the acceleration information, and can therefore infer that deterioration of the structure has occurred due to a decrease in the rigidity of the structure in a state that is less susceptible to the influence of changes in the DC component. The structure deterioration diagnosis system according to claim 1 .

3. The sensor controller creating a predictive distribution by performing statistical processing on the time series data of the mode during a predetermined learning period; The degree of anomaly, which serves as an index for determining deterioration of the structure, is calculated based on the predictive distribution as an index for determining that a distribution of data outside the range of the predictive distribution is an abnormal data distribution among the time-series data of the mode collected during a determination period after the learning period. The structure deterioration diagnosis system according to claim 1 or 2.

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