JP7592487B2 - Structural Deterioration Diagnostic System - Google Patents

Description

This disclosure relates to a structure deterioration diagnosis system that performs deterioration diagnosis on structures such as bridges.

A conventional technique for detecting abnormalities in a structure is a structural anomaly detection system that uses natural frequency as a feature for detecting anomalies (see, for example, Patent Document 1). The system in Patent Document 1 detects abnormalities by evaluating the relationship between multiple positions for the vibration intensity of the natural frequency. In other words, it detects abnormalities in the structure from changes in the relationship between feature quantities at multiple positions based on the measurement results from multiple sensors with the same function.

International Publication No. 2017/064855 JP 2015-64347 A

However, the conventional techniques have the following problems.
In Patent Document 1, deterioration of a structure can be detected by a change in natural vibration calculated as a feature based on the measurement results of multiple sensors having the same function. However, in order to perform deterioration detection with high accuracy in response to seasonal variations or rapid temperature changes, it is necessary to collect data over a long period of time.

In addition, for example, if there is a sudden change in temperature, the natural frequency will change due to the effect of the temperature, and even if there is no actual deterioration, it may be mistakenly assumed that the natural frequency has changed due to the progression of deterioration, resulting in a false detection of an abnormality.

The present disclosure has been made to solve the problems described above, and aims to provide a structure deterioration diagnosis system that can detect deterioration of structures while suppressing false positives caused by temperature fluctuations.

The structure deterioration diagnosis system according to the present disclosure includes an acceleration sensor that is installed in a structure and detects the acceleration of the structure, and a diagnosis unit that performs deterioration diagnosis of the structure based on the acceleration detected by the acceleration sensor. The diagnosis unit sequentially acquires the acceleration detected by the acceleration sensor and temperature data while the acceleration is being detected by the acceleration sensor at a predetermined sampling period, and calculates the inclination of the structure or the natural vibration of the structure, which is an indicator of deterioration diagnosis of the structure, as a feature for each of the sequentially acquired accelerations , thereby generating time series data on the feature amounts , and sequentially stores in a memory unit over time associated data that associates the time series data on the temperature data sequentially acquired at the sampling period while the acceleration is being detected with the time series data on the feature amounts collected sequentially at the same time as the temperature data at the sampling period, and if it diagnoses that a variation has occurred in the relationship compared to when it was in a steady state based on the associated data sequentially stored in the memory unit, it determines that deterioration has occurred in the structure.

The present disclosure provides a structure deterioration diagnosis system that can detect deterioration of structures while suppressing false positives caused by temperature fluctuations.

1 is a configuration diagram of a structure deterioration diagnosis system according to a first embodiment of the present disclosure. FIG. 1 is an explanatory diagram showing a state in which an acceleration sensor and an air temperature sensor are installed on a structure to be diagnosed by the structure deterioration diagnosis system according to the first embodiment of the present disclosure, and diagnosis is performed in a steady state and in a state of progressive deterioration. 1 is an explanatory diagram showing a steady state in which no deterioration occurs in deterioration diagnosis using an acceleration sensor and an air temperature sensor according to the first embodiment of the present disclosure. FIG. 1 is an explanatory diagram showing a deterioration progression state in which deterioration has occurred in deterioration diagnosis using an acceleration sensor and an air temperature sensor according to the first embodiment of the present disclosure. FIG. 4 is an explanatory diagram different from FIG. 3 , illustrating a steady state in which no degradation occurs in degradation diagnosis using an acceleration sensor and an air temperature sensor in the first embodiment of the present disclosure. FIG. 1 is a diagram for explaining the advantage in terms of time required for deterioration detection of the structure deterioration diagnosis system in the first embodiment of the present disclosure. FIG. 1 is an explanatory diagram regarding a false detection suppression function of the structure deterioration diagnosis system in the first embodiment of the present disclosure. FIG.

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 diagnosing deterioration of a structure by suppressing false positives caused by temperature fluctuations based on the presence or absence of a significant difference between current feature values and past feature values associated with temperature data.

Embodiment 1.
In this embodiment 1, as an example of providing one sensor in the structure of a structure, a deterioration diagnosis of a structure is performed based on associated data that associates temperature data with the measurement results from one acceleration sensor installed in the main girder of a bridge, as described with reference to the drawings. Note that while the temperature data can be obtained from outside as meteorological station data related to the installation location of the structure, the following describes the case where the temperature data is obtained using a temperature sensor with reference to the drawings.

Figure 1 is a configuration diagram of a structure deterioration diagnosis system according to the first embodiment of the present disclosure. The structure deterioration diagnosis system according to the first embodiment is configured to include an acceleration sensor 10, a temperature sensor 11, and a diagnosis unit 20.

Figure 2 is an explanatory diagram showing a state in which an acceleration sensor 10 and an air temperature sensor 11 are installed on a structure that is to be diagnosed by the structure deterioration diagnosis system according to the first embodiment of the present disclosure, and diagnosis is performed in a steady state and in a state of progressive deterioration. In Figure 2, a bridge 30 is shown as a specific example of a structure, and a situation in which a vehicle 1 passes over the bridge 30 is shown.

Figure 2(a) shows the steady state before deterioration occurs. On the other hand, Figure 2(b) shows the deterioration progress state in which cracks have appeared in the bridge 30 due to deterioration.

In the present embodiment 1, the acceleration sensor 10 and the air temperature sensor 11 are installed on the main girder, which is a component of the bridge 30. Here, the main girder corresponds to the structural body of the structure to be diagnosed.

In the following explanation, a deterioration diagnosis is performed based on associated data that correlates the detection results from one acceleration sensor 10 with the detection results from the air temperature sensor 11. However, the acceleration sensor 10 itself may be installed as multiple acceleration sensors at two or more locations on the bridge 30. Also, even when multiple acceleration sensors are used, one air temperature sensor 11 can be shared.

When two or more acceleration sensors 10 are installed, the detection results at the installation positions of each acceleration sensor 10 can be associated with temperature data to obtain multiple deterioration diagnosis results for each acceleration sensor 10.

The diagnosis unit 20 generates the inclination or natural frequency of the bridge 30 as a feature quantity that serves as an index for diagnosing the deterioration state of the bridge 30, which is a structure, from the physical quantities acquired by the acceleration sensor 10 over time. Note that conventional technology can be applied as a method for determining the inclination or natural frequency of a structure from the physical quantities acquired by the acceleration sensor 10 (for example, see Patent Document 2).

The structural deterioration diagnosis system according to the first embodiment has a technical feature in that it manages the inclination or natural vibration, which is an index for structural deterioration diagnosis generated from the measurement results by the acceleration sensor 10, as a feature associated with temperature data, and determines that deterioration is progressing when there is a significant difference between past and current feature values related to the same temperature data. Therefore, the specific deterioration diagnosis method executed by the diagnosis unit 20 will be described in detail below.

Figure 3 is an explanatory diagram showing a steady state in which no degradation occurs in degradation diagnosis using the acceleration sensor 10 and the air temperature sensor 11 in embodiment 1 of the present disclosure.

Figure 3(a) shows temperature data collected over about a year and a half based on the measurement results of the temperature sensor 11. Also, Figure 3(b) shows tilt data collected over about a year and a half based on the measurement results of the acceleration sensor 10. Furthermore, Figure 3(c) is a diagram summarizing the relationship between temperature data and tilt data collected at the same time.

On the other hand, FIG. 4 is an explanatory diagram showing the deterioration progression state in which deterioration has occurred in deterioration diagnosis using the acceleration sensor 10 and the air temperature sensor 11 in the first embodiment of the present disclosure.

Figure 4(a) shows temperature data collected over a period of about nine months based on the measurement results of the temperature sensor 11. Figure 4(b) shows tilt data collected over a period of about nine months based on the measurement results of the acceleration sensor 10. Figure 4(c) is a diagram summarizing the relationship between temperature data and tilt data collected at the same time.

Next, a specific deterioration diagnosis method executed by the diagnosis unit 20 will be described with reference to Figs. 3 and 4. The diagnosis unit 20 sequentially acquires the measured values of the air temperature sensor 11 at a predetermined sampling period and generates time series data related to air temperature, thereby obtaining the data in Figs. 3(a) and 4(a).

In the same manner, the diagnostic unit 20 can obtain the data in Figures 3(b) and 4(b) by sequentially acquiring the measurement values of the acceleration sensor 10 at a predetermined sampling period and generating time series data regarding the tilt.

Furthermore, the diagnostic unit 20 can obtain the associated data shown in Figs. 3(c) and 4(c) by associating the time series data on temperature and the time series data on slope collected at the same sampling time. That is, the diagnostic unit 20 can obtain the change in the relationship over time by sequentially collecting temperature data and slope data at the same sampling time. Note that the time series data on temperature data and the time series data on slope may be averaged over a certain time span, and the associated data may be created using the average value.

Here, as shown in Figure 3(c), in a steady state where degradation is not progressing, the relationship between temperature and the slope is stable, and a high correlation value is obtained.

On the other hand, as shown in Figure 4(c), when the bridge 30 is in a state of advanced deterioration where cracks have begun to appear in parts of the bridge, the relationship between temperature and inclination varies, and the correlation value decreases compared to when the bridge is in a steady state.

Figure 4 shows a state in which deterioration occurred during the season indicated by the dotted ellipse that spans both Figure 4(a) and Figure 4(b), resulting in abnormal data with a loss of correlation, as shown in Figure 4(c).

The diagnostic unit 20 can therefore quantitatively diagnose deterioration of a structure by using the correlation value as an index value based on the temporal change in the relationship between the tilt data calculated based on the acceleration sensor 10 and the temperature data calculated based on the temperature sensor 11.

In other words, the diagnostic process described above can be performed by the diagnostic unit 20 quantitatively diagnosing the deterioration of a structure through the following procedure.
Step 1: While the acceleration sensor 10 is detecting acceleration, the air temperature sensor 11 acquires air temperature data.
Step 2: From the acceleration detected by the acceleration sensor 10, the inclination or natural vibration of the structure, which is an index for diagnosing deterioration of the structure, is calculated as a feature.

Step 3: The acquired temperature data is associated with the calculated feature quantities, and the associated data is stored in the storage unit over time. Note that the storage unit is not shown in FIG.
Step 4: From the related data stored sequentially in the memory unit, past features stored in association with the same temperature data as the temperature data associated with the current features are extracted, and if a predetermined significant difference exists between the past features and the current features, it is determined that deterioration has occurred in the structure.

Note that in Figs. 3 and 4, a case has been described in which temperature data is associated with the inclination calculated based on the acceleration sensor 10 to perform a deterioration diagnosis. However, it is also possible to perform a deterioration diagnosis by associating temperature data with the natural frequency calculated based on the acceleration sensor 10 instead of the inclination.

Figure 5 is an explanatory diagram different from Figure 3, showing a steady state in which no deterioration occurs in deterioration diagnosis using the acceleration sensor 10 and the air temperature sensor 11 in the first embodiment of the present disclosure. Figure 5(a) shows air temperature data collected over about seven months based on the measurement results of the air temperature sensor 11. Figure 5(b) shows natural frequency data collected over about seven months based on the measurement results of the acceleration sensor 10. Furthermore, Figure 5(c) is a diagram summarizing the relationship between air temperature data and natural frequency data collected at the same time.

As shown in FIG. 5(c), in a steady state where deterioration is not progressing, the relationship between temperature and natural frequency is stable, and a high correlation value is obtained. On the other hand, although not shown, in a state where deterioration is progressing and cracks have begun to appear in parts of the bridge 30, the relationship between temperature and natural frequency fluctuates, and the correlation value decreases compared to the steady state.

The diagnostic unit 20 can therefore quantitatively diagnose deterioration of a structure by using the correlation value as an index value based on the temporal change in the relationship between the natural frequency data calculated based on the acceleration sensor 10 and the temperature data calculated based on the temperature sensor 11.

Figure 6 is a diagram for explaining the advantage in terms of time required for deterioration detection of the structure deterioration diagnosis system in embodiment 1 of the present disclosure. Figure 6(a) in the upper part of Figure 6 shows a schematic diagram of the time required for deterioration detection using conventional deterioration detection technology. Meanwhile, Figure 6(b) in the lower part of Figure 6 shows a schematic diagram of the time required for deterioration detection using the deterioration detection technology in embodiment 1 of the present disclosure.

In the conventional technology shown in FIG. 6(a), in order to absorb seasonal changes in temperature and the like, it was necessary to obtain tilt data or natural frequency data over a long period of time (e.g., years) and determine the changes. In other words, because tilt data and natural frequency data have strong seasonal fluctuations, in order to diagnose deterioration by comparing before and after the time series, it was necessary to measure over a long period of time (years) in order to suppress the effects of seasonal fluctuations.

As a result, a long preparation period is required to perform deterioration detection with a high degree of accuracy, and there is a problem in that it takes a relatively long time to obtain a structural deterioration diagnosis system that can be put into practical use.

In contrast, the technology according to the first embodiment extracts past features associated with the same temperature data as the temperature data associated with the current features, and if there is a significant difference between the past features and the current features, it is possible to quickly determine that deterioration has occurred. Therefore, it is possible to obtain a structure deterioration diagnosis system that can be put to practical use sooner than with conventional technology.

The diagnosis unit 20 can determine that deterioration is progressing when, for example, there is a significant difference between the past characteristic amount and the current characteristic amount for one hour with the same average temperature. In the extreme case, when the temperature one hour before the present time is the same as the current temperature, the characteristic amount at the present time may be compared with the characteristic amount one hour before.

It is also possible to use the feature values for all the same temperatures from a year ago, or from a year ago up to one hour before the present time, to determine whether there is a statistically significant difference. Note that as long as the temperature and natural frequency can be calculated stably, the unit is not limited to one hour.

Next, the advantage of suppressing false positives by performing deterioration diagnosis using features based on the correlation with temperature will be further explained with reference to FIG. 7. FIG. 7 is an explanatory diagram of the false positive suppression function of the structure deterioration diagnosis system in the first embodiment of the present disclosure.

Figure 7 is an explanatory diagram illustrating a case where a steady state in which no degradation has occurred and freezing has occurred in deterioration diagnosis using the acceleration sensor 10 and the air temperature sensor 11 in the first embodiment of the present disclosure. Figure 7(a) shows temperature data collected over approximately two days based on the measurement results of the air temperature sensor 11. Figure 7(b) shows natural frequency data collected over approximately two days based on the measurement results of the acceleration sensor 10. Furthermore, Figure 5(c) is a diagram summarizing the relationship between the air temperature data and natural frequency data collected at the same time.

As shown in Figure 5(a), when freezing occurs during a certain period of time, the characteristic quantity, the natural frequency, changes sharply as shown in Figure 5(b), even though the system is in a steady state with no deterioration.

When such freezing occurs, conventional technology that monitors only the features may mistakenly determine that the feature changes due to freezing are due to deterioration, resulting in a false positive detection of deterioration.

In contrast, the structure deterioration diagnosis system according to the first embodiment determines whether deterioration is progressing by determining a significant difference between past feature values associated with temperature data and current feature values. Therefore, when feature values associated with the temperature at the time of freezing are associated as steady-state data and have a different correlation from feature values associated with the same temperature at the time of deterioration, the diagnosis unit 20 can suppress erroneous detection.

As described above, according to the first embodiment, deterioration diagnosis is performed based on the presence or absence of a significant difference between the current feature values associated with the temperature data and the past feature values. As a result, deterioration diagnosis of structures can be performed while suppressing false positives caused by temperature fluctuations.

Furthermore, by using index values associated with temperature data for deterioration diagnosis, a structure deterioration diagnosis system can be obtained that can detect deterioration of structures at an earlier stage compared to conventional technologies that do not associate with temperature data.

In this embodiment, a bridge has been used as an example, but the system can also be applied to deterioration diagnosis of buildings, tunnel accessories (such as jet fan installations and lighting devices), slopes, water pipes, etc.

10 Acceleration sensor, 11 Temperature sensor, 20 Diagnostic unit.

Claims (2)

An acceleration sensor that is installed in a structure and detects an acceleration of the structure;
a diagnosis unit that performs a deterioration diagnosis of the structure based on the acceleration detected by the acceleration sensor,
The diagnosis unit includes:
Sequentially acquiring the acceleration detected by the acceleration sensor and air temperature data during detection of the acceleration by the acceleration sensor at a predetermined sampling period;
calculating an inclination or a natural vibration of the structure, which is an index for diagnosing deterioration of the structure, as a feature for each of the sequentially acquired accelerations , thereby generating time series data on the feature ;
storing associated data in a storage unit over time, the associated data being associated with time series data on the temperature data sequentially acquired at the sampling period during the detection of the acceleration and time series data on the feature amount sequentially collected at the same time as the temperature data at the sampling period;
A structure deterioration diagnosis system which, based on the associated data sequentially stored in the memory unit, diagnoses that the relationship between the feature amount and the temperature data has varied over time compared to when the relationship was in a steady state, and determines that deterioration is occurring in the structure. The diagnosis unit calculates average values over a predetermined time span for each of the time series data related to the temperature data and the time series data related to the feature amount, creates the associated data using the average values, and sequentially stores the created associated data in the storage unit.
The structure deterioration diagnosis system according to claim 1 .