JP7605716B2 - Deterioration diagnosis system - Google Patents

Description

This disclosure relates to a deterioration diagnosis system that diagnoses deterioration of structures such as bridges.

A conventional technique for detecting abnormalities in structures is a structural abnormality detection system that uses natural frequency as a feature for detecting abnormalities (see, for example, Patent Document 1). The system in Patent Document 1 detects abnormalities by evaluating the relationship between multiple positions regarding the vibration intensity of the natural frequency, etc.

International Publication No. 2017/064855

However, the conventional techniques have the following problems.
In Patent Document 1, deterioration of a structure can be detected based on a change in the natural frequency. However, when deterioration occurs near a node of the vibration form, the effect on the natural frequency is small, and the change in the natural frequency is difficult to detect.

Therefore, in such cases, there is a problem that deterioration cannot be detected with high accuracy based on changes in the natural vibration. Furthermore, in order to avoid such problems, a large number of sensors would be required, which would lead to an increase in manufacturing costs.

The present disclosure has been made to solve the problems described above, and aims to provide a deterioration diagnosis system that can suppress increases in manufacturing costs and detect deterioration with high accuracy in areas where changes in natural vibration are unlikely to occur.

The deterioration diagnosis system according to the present disclosure comprises a sensor head that is installed in a structure and detects physical quantities for performing deterioration diagnosis of the structure, and a diagnosis unit that performs deterioration diagnosis of the structure based on the physical quantities detected by the sensor head, wherein the structure is supported by supports provided outside a quartile in a structural component of the structure, and the sensor head is installed between the quartile and the support, and the diagnosis unit calculates tilt information of the structure as a feature amount from the physical quantities detected by the sensor head, the tilt information being a first indicator for deterioration diagnosis of the structure, and sequentially stores the calculated tilt information as time-series data in a memory unit over time, extracts past feature amounts from the time-series data sequentially stored in the memory unit, and when a predetermined significant difference exists between the past feature amounts and the current feature amount, it is possible to determine that deterioration has occurred in the structure from the change over time in the tilt information calculated from the detection results of one sensor head .

This disclosure makes it possible to obtain a deterioration diagnosis system that can suppress increases in manufacturing costs and detect deterioration with high accuracy in areas where changes in natural vibration are unlikely to occur.

1 is an explanatory diagram showing the installation position of a sensor head used in the degradation diagnosis system according to the first embodiment of the present disclosure. FIG. 11 is an explanatory diagram showing a change in inclination when damage due to a crack occurs near the center of a bridge in the first embodiment of the present disclosure. 1 is a configuration diagram of a degradation diagnosis system according to a first embodiment of the present disclosure. 11 is an explanatory diagram showing a change in tilt information over time when damage due to a crack occurs near the center of a bridge in the first embodiment of the present disclosure. FIG. FIG. 11 is an explanatory diagram showing a change in gradient information over time when damage due to a crack occurs near a quartile of a bridge in the first embodiment of the present disclosure. 11 is an explanatory diagram showing a change in tilt information over time when damage due to a crack occurs near a bridge support in the first embodiment of the present disclosure. FIG. FIG. 11 is an explanatory diagram showing a change in natural vibration information over time when damage due to a crack occurs near the center of a bridge in the second embodiment of the present disclosure. FIG. 11 is an explanatory diagram showing a change over time in natural vibration information when damage due to a crack occurs near a quartile of a bridge in embodiment 2 of the present disclosure. FIG. 11 is an explanatory diagram showing a change over time in natural vibration information when damage due to a crack occurs near a bridge support in embodiment 2 of the present disclosure.

Hereinafter, preferred embodiments of the degradation diagnosis system of the present disclosure will be described with reference to the drawings.
The present disclosure has a technical feature in which a structure supported by a support located outside a quartile in the structure of the structure is targeted for deterioration diagnosis, a sensor head is installed between the quartile and the support, tilt information of the structure which serves as an index for deterioration diagnosis of the structure is calculated as a feature value from physical quantities detected by the sensor head, and deterioration diagnosis of the structure is performed from changes in the tilt information over time.

Embodiment 1.
In this embodiment 1, as a specific example of installing a sensor head on the structural components of a structure, a deterioration diagnosis of the structure will be described based on a comparison of measurement results from two sensor heads installed on the main girders of a bridge, which corresponds to the structure.

The number of sensor heads may be other than two, as will be described in detail later. By providing one sensor head, it is possible to determine whether deterioration has occurred, and by providing two or more sensor heads, it is possible to further pinpoint the location where deterioration has occurred.

Figure 1 is an explanatory diagram showing the installation position of a sensor head used in a deterioration diagnosis system according to embodiment 1 of the present disclosure. A bridge 10, which corresponds to a structure, is supported by a support 12(1) provided outside a quartile 11(1) in the structure of the bridge 10, and a support 12(2) provided outside a quartile 11(2).

Sensor heads 20(1) and 20(2) are each installed on bridge 10 to detect physical quantities for diagnosing deterioration of bridge 10. Here, sensor head 20(1) is installed between quartile point 11(1) and support 12(1), and sensor head 20(2) is installed between quartile point 11(2) and support 12(2).

In other words, sensor head 20(1) and sensor head 20(2) are installed on the outer side of support 12(1) and support 12(2), which are beyond the quartile point from the center of the bridge 10 structure.

Because structures have elasticity, they deflect under their own weight. When deterioration or damage occurs between the supports, the deflection becomes greater. However, in a structure such as bridge 10, the change in the inclination of the structure that occurs when the deflection becomes greater due to damage is not constant between the supports, but varies depending on the position of the structure, due to its elasticity.

Figure 2 is an explanatory diagram showing the change in inclination when damage due to a crack 1 occurs near the center of the bridge 10 in embodiment 1 of the present disclosure. Figure 2(a) shows a state in which damage has occurred near the center such that the bridge 10 is completely separated. This state occurs due to the continued progression of deterioration or the occurrence of a major earthquake or disaster.

In this case, the cost of repairing the structure is high. On the other hand, Figure 2(b) shows the initial state of damage before the bridge 10 is completely severed. This state occurs when deterioration progresses gradually due to corrosion and other factors. If the structure is repaired at this stage, the repair cost is significantly less than the repair cost after the structure is severed, so there is a great advantage to detecting deterioration at an early stage.

When a structure is separated between supports as in Figure 2(a), the change in inclination tends to be greater the closer it is to the damaged area. On the other hand, in a damaged state as shown in Figure 2(b), before the structure is completely separated, the change in inclination at the center of bridge 10 is small, while the change in inclination near supports 12(1) and 12(2) is large. Therefore, even if a sensor head is installed in the center, it is difficult to detect gradual deterioration from the change in inclination.

On the other hand, by installing sensor head 20(1) and sensor head 20(2) at the above positions, damage to a structure such as a bridge 10 can be detected at an early stage of the damage. In other words, damage to the structure can be detected with higher sensitivity.

Even if deterioration does not occur in the center but in the quartiles where cracks between supports are more likely to occur, the change in slope will be larger at sensor head 20(1) and sensor head 20(2), or at positions closer to the supports than at the center.

Note that Figures 2(a) and 2(b) show a schematic diagram of a state in which a crack 1 has occurred near the center of the bridge 10, while Figure 1 shows a schematic diagram of a state in which damage caused by a crack 1 has occurred and deterioration is progressing between the quartile 11(1) and the support 12(1).

Next, a specific configuration of the degradation diagnosis system according to the first embodiment will be described with reference to FIG. 3, which corresponds to the explanatory diagram of FIG. 1. FIG. 3 is a configuration diagram of the degradation diagnosis system according to the first embodiment of the present disclosure. The degradation diagnosis system in the first embodiment is configured to include two sensor heads 20(1) and 20(2), a diagnosis unit 30, and a memory unit 40.

The diagnosis unit 30 corresponds to a controller that performs deterioration diagnosis of the bridge 10, which is a structure, based on the physical quantities detected by the sensor heads 20(1) and 20(2). The diagnosis unit 30 calculates, from the physical quantities detected by one or more sensor heads 20, tilt information of the structure, which is a first index for bridge deterioration diagnosis, as a feature quantity.

Next, the diagnosis unit 30 stores the calculated tilt information as time passes in the memory unit 40 as time-series data. Furthermore, the diagnosis unit 30 extracts past feature amounts from the time-series data stored in the memory unit 40, and determines whether or not a significant difference exists between the past feature amounts and the current feature amounts, which is set in advance. If the diagnosis unit 30 determines that a significant difference exists, it determines that deterioration has occurred in the bridge 10.

In particular, in this first embodiment, the tilt information of the structure is used as the feature amount. Therefore, the deterioration diagnosis method based on the tilt information will be described in detail using the specific example shown in Figures 4A to 4C.

Figure 4A is an explanatory diagram showing the change over time in tilt information when damage due to crack 1 occurs near the center of bridge 10 in embodiment 1 of the present disclosure. The line graph in Figure 4A shows the time course of the damage progression on the horizontal axis and the tilt on the vertical axis, showing the time change in tilt associated with the progression of damage as time-series data.

The diagnosis unit 30 calculates first tilt information, which is one of the characteristic quantities, from the physical quantity detected by the sensor head 20(1) and stores the first tilt information in the storage unit 40 as first time-series data in sequence over time. In the line graph of FIG. 4A, each piece of data indicated by a ▲ plot on the dotted line corresponds to the first time-series data.

Similarly, the diagnosis unit 30 calculates second tilt information, which is one of the feature quantities, from the physical quantity detected by the sensor head 20(2) and stores the second tilt information in the storage unit 40 as second time-series data sequentially over time. In the line graph of FIG. 4A, each data indicated by a ● plot on the solid line corresponds to the second time-series data.

When damage due to crack 1 occurs near the center of bridge 10, as shown in Figure 4A, the time change in the slope based on the first time series data and the time change in the slope based on the second time series data are opposite in positive and negative, and it can be seen that the absolute value of the slope changes at the same rate over time.

The diagnostic unit 30 can therefore determine that deterioration has occurred when a significant difference exists between the slope, which is a past characteristic quantity, and the slope, which is a current characteristic quantity, based on either the first time series data calculated based on the physical quantity detected by the sensor head 20(1) or the second time series data calculated based on the physical quantity detected by the sensor head 20(2).

For example, in FIG. 4A, if the past time is represented by time 0 on the horizontal axis and the current time is represented by time 6 on the horizontal axis, the diagnosis unit 30 can determine that a crack 1 has occurred if the difference in the slope at each time is equal to or greater than a predetermined significant difference.

Furthermore, the diagnostic unit 30 compares the time changes of the first time series data calculated based on the physical quantity detected by the sensor head 20 (1) and the second time series data calculated based on the physical quantity detected by the sensor head 20 (2), and if it determines that the positive and negative values are opposite and that the absolute values of the slopes change at the same rate over time, it can determine that damage due to crack 1 has occurred near the center of the bridge 10, and identify the location of the deterioration.

Figure 4B is an explanatory diagram showing the time change in slope information when damage due to crack 1 occurs near quartile 11(1) of bridge 10 in embodiment 1 of the present disclosure. The line graph in Figure 4B, like Figure 4A, shows the time course of the damage progression on the horizontal axis and the slope on the vertical axis, and shows the time change in slope associated with the progression of damage as time series data.

The diagnosis unit 30 calculates first tilt information, which is one of the characteristic quantities, from the physical quantity detected by the sensor head 20(1) and stores the first tilt information in the storage unit 40 as first time-series data in sequence over time. In the line graph of FIG. 4B, each piece of data indicated by a ▲ plot on the dotted line corresponds to the first time-series data.

Similarly, the diagnosis unit 30 calculates second tilt information, which is one of the feature quantities, from the physical quantity detected by the sensor head 20(2), and stores the first tilt information in sequence over time in the storage unit 40 as second time-series data. In the line graph of FIG. 4B, each data indicated by a ● plot on the solid line corresponds to the second time-series data.

When damage due to crack 1 occurs near quartile 11(1) of bridge 10, as shown in Figure 4B, the time change in the slope based on the first time series data and the time change in the slope based on the second time series data are opposite in positive and negative, and the absolute value of the slope changes over time to different degrees.

More specifically, in FIG. 4B, crack 1 occurs near quartile 11(1), and therefore the change over time in the slope as the first time series data from sensor head 20(1) is greater than the change over time in the slope as the second time series data from sensor head 20(2).

The diagnostic unit 30 can therefore determine that deterioration has occurred when a significant difference exists between the slope, which is a past characteristic quantity, and the slope, which is a current characteristic quantity, based on either the first time series data calculated based on the physical quantity detected by the sensor head 20(1) or the second time series data calculated based on the physical quantity detected by the sensor head 20(2).

For example, in FIG. 4B, if the past time is represented by time 0 on the horizontal axis and the current time is represented by time 6 on the horizontal axis, the diagnosis unit 30 can determine that crack 1 has occurred if the difference in the slope at each time is equal to or greater than a predetermined significant difference.

Furthermore, the diagnostic unit 30 compares the time changes of the first time series data calculated based on the physical quantity detected by the sensor head 20 (1) and the second time series data calculated based on the physical quantity detected by the sensor head 20 (2), and if it determines that the positive and negative values are opposite and that the absolute values of the slopes change to different degrees over time, it can determine that damage due to crack 1 has occurred near the quartile where the sensor head showing the greater time change is installed, and identify the location where deterioration has occurred.

Figure 4C is an explanatory diagram showing the time change in tilt information when damage due to crack 1 occurs near the support of bridge 10 in embodiment 1 of the present disclosure. The line graph in Figure 4C, like Figures 4A and 4B, shows the time course of the damage progression on the horizontal axis and the tilt on the vertical axis, and shows the time change in the tilt associated with the progression of damage as time series data.

The diagnosis unit 30 calculates first tilt information, which is one of the characteristic quantities, from the physical quantity detected by the sensor head 20(1) and stores the first tilt information in the storage unit 40 as first time-series data in sequence over time. In the line graph of FIG. 4C, each piece of data indicated by a ▲ plot on the dotted line corresponds to the first time-series data.

Similarly, the diagnosis unit 30 calculates second tilt information, which is one of the feature quantities, from the physical quantity detected by the sensor head 20(2) and stores the second tilt information in the storage unit 40 as second time-series data sequentially over time. In the line graph of FIG. 4C, each data indicated by a ● plot on the solid line corresponds to the second time-series data.

When damage due to crack 1 occurs near support 12(1) of bridge 10, as shown in Figure 4C, it can be seen that the time change in the slope based on the first time series data and the time change in the slope based on the second time series data change in the same direction, both positive and negative.

More specifically, in FIG. 4C, crack 1 occurs near support 12(1) outside quartile 11(1), so the time change in the slope as the first time series data from sensor head 20(1) and the time change in the slope as the second time series data from sensor head 20(2) both tend to increase in the positive direction. Furthermore, the time change in the first time series data from sensor head 20(1), which is closer to crack 1, tends to be smaller than the time change in the second time series data from sensor head 20(2).

The diagnostic unit 30 can therefore determine that deterioration has occurred when a significant difference exists between the slope, which is a past characteristic quantity, and the slope, which is a current characteristic quantity, based on either the first time series data calculated based on the physical quantity detected by the sensor head 20(1) or the second time series data calculated based on the physical quantity detected by the sensor head 20(2).

For example, in FIG. 4C, if the past time is represented by time 0 on the horizontal axis and the current time is represented by time 6 on the horizontal axis, the diagnosis unit 30 can determine that a crack 1 has occurred if the difference in the slope at each time is equal to or greater than a predetermined significant difference.

Furthermore, the diagnostic unit 30 compares the time changes of the first time series data calculated based on the physical quantity detected by the sensor head 20 (1) and the second time series data calculated based on the physical quantity detected by the sensor head 20 (2), and if it determines that the positive and negative directions are the same and that the absolute value of the slope changes to different degrees over time, it can determine that damage due to crack 1 has occurred near the support where the sensor head showing the smaller time change is installed, and identify the location where the deterioration has occurred.

It is desirable that sensor head 20(1) and sensor head 20(2) are placed at equal distances on both sides from the center of pier 10. However, if sensor head 20(1) is placed between quartile point 11(1) and support 12(1), and sensor head 20(2) is placed between quartile point 11(2) and support 12(2), the effect of being able to identify the location of the deterioration described above can be obtained even if the sensor heads are not placed at equal distances on both sides.

As described above, according to the first embodiment, a sensor head is installed between the quartile and the support, and the inclination information of the structure, which is an index for diagnosing deterioration of the structure, is calculated as a feature value from the physical quantity detected by the sensor head, and deterioration of the structure is diagnosed from the change in the inclination information over time. As a result, a deterioration diagnosis system that can detect deterioration occurring particularly near the support with high accuracy can be realized.

In particular, when using the amplitude, phase, natural vibration, etc. of a structure as index values for deterioration diagnosis for deterioration occurring near the support, it is difficult to detect deterioration with high accuracy because the index values do not change easily over time. In contrast, by placing the sensor head between the quartile and the support and using information on the inclination of the structure as an index value for deterioration diagnosis, it is possible to detect deterioration occurring near the support with high accuracy.

Embodiment 2.
In the above-described first embodiment, the case where the tilt information is used as the feature amount has been described. In contrast to this, in the second embodiment, a case where the natural vibration information of the structure, which is a second index for degradation diagnosis, is further calculated in addition to the tilt information of the structure, which is a first index for degradation diagnosis, and degradation diagnosis is performed using both the tilt information and the natural vibration information will be described.

First, a degradation diagnosis method based on characteristic vibration information will be described in detail using the specific example shown in Figures 5A to 5C. Note that each of the sensor heads 20(1) and 20(2) used in this embodiment 2 is capable of detecting acceleration as a physical quantity. In the following, a case where the detection result of sensor head 20(1) is used will be described.

Figure 5A is an explanatory diagram showing the time change in natural vibration information when damage due to crack 1 occurs near the center of bridge 10 in embodiment 2 of the present disclosure. The line graph in Figure 5A shows the time change in natural vibration due to the progression of damage as time series data, with the horizontal axis representing the progression of damage over time and the vertical axis representing the rate of decrease in natural vibration frequency compared to when there is no damage.

The diagnosis unit 30 calculates the first-order natural frequency, which is one of the characteristic quantities, as natural vibration information from the acceleration, which is a physical quantity detected by the sensor head 20(1), and stores the first-order natural frequency sequentially over time as third time-series data in the storage unit 40. In the line graph of FIG. 5A, each data indicated by a ● plot on the solid line corresponds to the third time-series data.

Similarly, the diagnosis unit 30 calculates the second-order natural frequency, which is one of the characteristic quantities, as natural vibration information from the acceleration, which is a physical quantity detected by the sensor head 20(1), and stores the second-order natural frequency sequentially over time in the storage unit 40 as fourth time-series data. In the line graph of FIG. 5A, each data indicated by a plot of x on the dotted line corresponds to the fourth time-series data.

When damage due to crack 1 occurs near the center of bridge 10, as shown in Figure 5A, it can be seen that the time change in the first-order natural frequency based on the third time series data is greater than the time change in the second-order natural frequency based on the fourth time series data.

Therefore, the diagnosis unit 30 can determine that deterioration has occurred if a predetermined significant difference exists between the past characteristic amount and the current characteristic amount based on the third time series data and the fourth time series data calculated based on the physical amount detected by the sensor head 20 (1).

For example, in FIG. 5A, if the past time is represented by time 0 on the horizontal axis and the current time is represented by time 6 on the horizontal axis, the diagnosis unit 30 can determine that a crack 1 has occurred when the rate of decrease in the first-order natural frequency is equal to or greater than a preset threshold value.

Furthermore, the diagnosis unit 30 compares the time changes of both the third time series data and the fourth time series data calculated based on the physical quantities detected by the sensor head 20 (1), and if the rate of decrease in the first-order natural frequency is greater than the rate of decrease in the second-order natural frequency, it determines that damage due to crack 1 has occurred near the center of the bridge 10, and can identify the location of the deterioration.

Figure 5B is an explanatory diagram showing the time change in natural vibration information when damage due to crack 1 occurs near quartile 11(1) of bridge 10 in embodiment 2 of the present disclosure. The line graph in Figure 5B, like Figure 5A, shows the time change in natural frequency due to the progression of damage as time series data, with the horizontal axis representing the progression of damage and the vertical axis representing the rate of decrease in natural frequency compared to when there is no damage.

The diagnosis unit 30 calculates the first-order natural frequency, which is one of the characteristic quantities, as natural vibration information from the acceleration, which is a physical quantity detected by the sensor head 20(1), and stores the first-order natural frequency in the storage unit 40 sequentially over time as third time-series data. In the line graph of FIG. 5B, each data indicated by a ● plot on the solid line corresponds to the third time-series data.

Similarly, the diagnosis unit 30 calculates the second-order natural frequency, which is one of the characteristic quantities, as natural vibration information from the acceleration, which is a physical quantity detected by the sensor head 20(1), and stores the second-order natural frequency sequentially over time in the storage unit 40 as fourth time-series data. In the line graph of FIG. 5B, each data indicated by a plot of x on the dotted line corresponds to the fourth time-series data.

When damage due to crack 1 occurs near quartile 11(1) of bridge 10, as shown in Figure 5B, it can be seen that the time change of the second-order natural frequency based on the fourth time series data is larger than the time change of the first-order natural frequency based on the third time series data.

Therefore, the diagnosis unit 30 can determine that deterioration has occurred if a significant difference exists between the past characteristic amount and the current characteristic amount, which is set in advance, based on the third time series data and the fourth time series data calculated based on the physical amount detected by the sensor head 20 (1).

For example, in FIG. 5B, if the past time is represented by time 0 on the horizontal axis and the current time is represented by time 6 on the horizontal axis, the diagnosis unit 30 can determine that a crack 1 has occurred when the rate of decrease in the second-order natural frequency is equal to or greater than a preset threshold value.

Furthermore, the diagnostic unit 30 compares the time changes of both the third time series data and the fourth time series data calculated based on the physical quantities detected by the sensor head 20 (1), and if the rate of decrease in the second-order natural frequency is greater than the rate of decrease in the first-order natural frequency, it determines that damage due to crack 1 has occurred near quartile 11 (1), and can identify the location where deterioration has occurred.

Figure 5C is an explanatory diagram showing the time change in natural vibration information when damage due to crack 1 occurs near support 12(1) of bridge 10 in embodiment 2 of the present disclosure. The line graph in Figure 5C, like Figures 5A and 5B, shows the time change in natural frequency due to the progression of damage as time series data, with the horizontal axis representing the progression of damage and the vertical axis representing the rate of decrease in natural frequency compared to when there is no damage.

The diagnosis unit 30 calculates the first-order natural frequency, which is one of the characteristic quantities, as natural vibration information from the acceleration, which is a physical quantity detected by the sensor head 20(1), and stores the first-order natural frequency sequentially over time in the storage unit 40 as third time-series data. In the line graph of FIG. 5C, each data indicated by a ● plot on the solid line corresponds to the third time-series data.

Similarly, the diagnosis unit 30 calculates the second-order natural frequency, which is one of the characteristic quantities, as natural vibration information from the acceleration, which is a physical quantity detected by the sensor head 20(1), and stores the second-order natural frequency sequentially over time in the storage unit 40 as fourth time-series data. In the line graph of FIG. 5C, each data indicated by a plot of x on the dotted line corresponds to the fourth time-series data.

When damage due to crack 1 occurs near support 12(1) of bridge 10, as shown in Figure 5C, it can be seen that there is no significant difference over time between the first-order natural frequency based on the third time series data and the second-order natural frequency based on the fourth time series data.

Therefore, the diagnosis unit 30 cannot determine that deterioration has occurred from the third time series data and the fourth time series data calculated based on the physical quantities detected by the sensor head 20(1).

In other words, even if the sensor head 20(1) is installed between the quartile 11(1) and the support 12(1), it is difficult to detect deterioration occurring near the support if deterioration diagnosis is performed using only the natural vibration information as a feature. However, as explained in the first embodiment above, it is possible to detect deterioration occurring near the support by using the tilt information as a feature.

In addition, if deterioration occurs in a location other than near the support, deterioration diagnosis using tilt information and deterioration diagnosis using natural vibration information can be used together to more accurately identify the occurrence of deterioration or the location where deterioration has occurred.

For example, even if a significant difference in tilt cannot be detected between past tilt information and current tilt information by only using tilt information as a feature, if a significant difference in the natural frequency can be detected between past natural vibration information and current natural vibration information by further using natural vibration information as a feature, it is possible to identify the occurrence of deterioration or the location where deterioration has occurred.

As described above, according to the second embodiment, a sensor head is installed between the quartile point and the support, and from the physical quantities detected by the sensor head, the inclination information of the structure, which is an index for diagnosing deterioration of the structure, and the natural vibration information are calculated as feature quantities, and the inclination information and the natural vibration information are used together to diagnose deterioration of the structure.

As a result, it is possible to obtain a deterioration diagnosis system that can detect deterioration occurring near the support with a high degree of accuracy based on tilt information, and can also detect deterioration occurring at a location other than near the support with a high degree of accuracy based on a combination of tilt information and natural vibration information.

In the second embodiment, the sensor head 20(1) has been described, but the sensor head 20(2) may also be used. In addition, both the sensor head 20(1) and the sensor head 20(2) may also be used.

Embodiment 3.
In this embodiment, a case will be described in which temperature data during detection of a physical quantity is taken into consideration to generate time-series data, thereby improving the accuracy of deterioration detection.

The physical quantities detected by the sensor head 20 may be affected by the temperature in the environment in which the structure is installed. For example, if tilt information calculated based on physical quantities detected in midwinter is directly compared with tilt information calculated based on physical quantities detected in midsummer, the accuracy of deterioration diagnosis may not be consistent.

The diagnosis unit 30 according to the third embodiment therefore acquires temperature data while the sensor head 20 is detecting the physical quantity. As an example, the diagnosis unit 30 can acquire temperature data from a thermometer installed near the structure, or can acquire temperature data as part of the weather data for the area in which the structure is installed.

On the other hand, in the third embodiment, the memory unit 40 stores a correction coefficient that is set in advance in association with the temperature data. Therefore, the diagnosis unit 30 can extract a correction coefficient corresponding to the acquired temperature data from the memory unit 40.

Then, the diagnosis unit 30 calculates the current feature amount by correcting the feature amount using the extracted correction coefficient, and generates temperature-corrected time series data by sequentially storing the calculated current feature amount in the storage unit 40 over time.

The temperature-corrected time series data generated in this manner takes into account the temperature data during detection of the physical quantity by the sensor head 20, and the influence of the temperature data is suppressed. Therefore, by using the temperature-corrected time series data, the diagnostic unit 30 can suppress the influence of the temperature data during detection of the physical quantity, thereby achieving uniform deterioration diagnosis accuracy.

As described above, according to the third embodiment, deterioration diagnosis is performed using temperature-corrected time series data generated by taking into account the air temperature data during detection of the physical quantity, thereby making it possible to standardize the accuracy of deterioration diagnosis that is not affected by air temperature.

10 Bridge (structure), 11 (1) quartile (first quartile), 11 (2) quartile (second quartile), 12 (1) bearing (first bearing), 12 (2) bearing (second bearing), 20 (1) sensor head (first sensor head), 20 (2) sensor head (second sensor head), 30 diagnosis unit, 40 memory unit.

Claims (5)

a sensor head that is installed in a structure and detects a physical quantity for diagnosing deterioration of the structure;
a diagnosis unit that performs a deterioration diagnosis of the structure based on the physical quantity detected by the sensor head,
The structure is supported by a support provided outside a quadrant of the structure of the structure,
The sensor head is disposed between the quartile and the support;
The diagnosis unit includes:
calculating, from the physical quantity detected by the sensor head, tilt information of the structure, which is a first index for deterioration diagnosis of the structure, as a feature quantity;
The calculated tilt information is stored in a storage unit as time-series data over time.
A past feature amount is extracted from the time-series data stored in the storage unit in sequence, and if a significant difference exists between the past feature amount and the current feature amount that is set in advance, it is possible to determine that deterioration has occurred in the structure based on a time change in the tilt information calculated from the detection result of one sensor head .
Deterioration diagnosis system. The sensor head includes:
a first sensor head installed between a first quartile located at one position from the center of the structure and a first support;
a second sensor head installed between a second quartile at the other position from the center of the structure and a second support;
The diagnosis unit includes:
calculating first tilt information, which is one of the feature amounts, from the physical amount detected by the first sensor head, and storing the first tilt information in the storage unit as first time-series data over time;
calculating second tilt information, which is one of the feature amounts, from the physical amount detected by the second sensor head, and storing the second tilt information in the storage unit as second time-series data over time;
When it is determined that deterioration has occurred in the structure based on the first time-series data or the second time-series data, the location where the deterioration has occurred can be identified by comparing the time changes of the tilt information of each of the two sensor heads calculated from the detection results of the first sensor head and the second sensor head .
The deterioration diagnosis system according to claim 1 . The sensor head detects acceleration as the physical quantity,
The diagnosis unit includes:
further calculating, as the feature quantity, natural vibration information of the structure, which is a second index for deterioration diagnosis of the structure, from the acceleration, which is a physical quantity detected by the sensor head;
The deterioration diagnosis system according to claim 1 or 2, wherein when it is determined that no deterioration has occurred in the structure based on the deterioration diagnosis of the structure based on the tilt information, it is determined whether or not deterioration has occurred in the structure based on the deterioration diagnosis of the structure based on the natural vibration information. The sensor head detects acceleration as the physical quantity,
The diagnosis unit includes:
further calculating, as the feature quantity, natural vibration information of the structure, which is a second index for deterioration diagnosis of the structure, from the acceleration, which is a physical quantity detected by the sensor head;
3. The deterioration diagnosis system according to claim 1 or 2, wherein when a deterioration diagnosis of a structure based on the natural vibration information determines that no deterioration has occurred in the structure, a deterioration diagnosis of the structure based on the tilt information determines whether or not deterioration has occurred in the structure. The diagnosis unit includes:
acquiring air temperature data during detection of the physical quantity by the sensor head;
5. The deterioration diagnosis system according to claim 1, further comprising: a correction coefficient that is preset according to the acquired temperature data to calculate the current feature amount; and a storage unit that stores the calculated current feature amount in sequence over time to generate the time series data.