JP6927360B2 - Crack diagnostic method and equipment - Google Patents

Description

The present invention relates to a crack diagnosis method and an apparatus for diagnosing a crack to be diagnosed by a crack.

Structures such as bridges, steel towers, buildings, hulls, hulls, aircraft fuselage, cranes and other transport equipment skeletons are made primarily of metal structural members.

This type of structural member is often formed by joining a plurality of members, and most of the members are joined by welding.

In structural members, cracks may occur at joints and the like due to fatigue. If cracks generated in the structural member grow, it may lead to deterioration in the performance of the structural member. Further, the deterioration of the performance of the structural member may affect the maintenance of the structure and the maintenance of the function of the structure.

Therefore, it is desired to be able to diagnose the presence or absence of cracks in the structure.

Conventionally, a structure monitoring system has been proposed. This is an equivalence test between the response curved surface of the measured data by multiple sensors attached to the structure and the response curved surface of the measured data when the structure is healthy in order to detect damage to the structure. Then, when the equivalence of the response surface is statistically rejected, it is determined that an abnormality has occurred in the structure.

Furthermore, for this monitoring system, it is also proposed to use a strain gauge as a sensor, create a response curved surface between the two strain gauges, and determine the occurrence of cracks by a significant difference test of the equivalence of the response curved surfaces. (See, for example, Patent Document 1).

Japanese Patent No. 3777453

However, in what is shown in Patent Document 1, the idea of determining the occurrence of cracks by using a plurality of strain gauges is shown, but the arrangement of the plurality of strain gauges is not specifically shown. .. Further, Patent Document 1 does not show the relationship between the occurrence of cracks and the measurement result of the strain gauge.

Therefore, it is difficult to improve the accuracy of determining the occurrence of cracks in the one shown in Patent Document 1.

Therefore, the present invention is intended to provide a crack diagnosis method and an apparatus capable of diagnosing the presence or absence of crack occurrence in a crack diagnosis target and improving the accuracy of the diagnosis. be.

In order to solve the above problems, the present invention has a first strain sensor installed in a first region of a crack diagnosis target and a second strain installed in a second region of the crack diagnosis target. A sensor and a computing device for inputting data from the first and second strain sensors are provided, and the first region and the second region have a structure corresponding to the crack diagnosis target. The result of the distribution of the amount of strain change by stress analysis using the first analysis model without cracks and the second analysis model with the structure corresponding to the crack diagnosis target and with cracks were used. By comparing with the result of the distribution of the strain change amount by the stress analysis, two regions defined so as to satisfy the condition that the relative change ratio between the strain change amounts in each analysis model is equal to or more than the set value. In the arrangement corresponding to, the two regions are set as the crack diagnosis target, and the arithmetic unit is the first and the first when the crack diagnosis target is in a normal state in which no crack has occurred. The function of accumulating the data input from each of the strain sensors of 2 as a normal data group and the data input from the first and second strain sensors at the time of the crack diagnosis of the crack diagnosis target are diagnosed. A function to set in the data group and a function to compare the diagnostic data group with the normal data group by statistical processing and determine whether or not a crack has occurred in the crack diagnosis target based on the comparison result. , Strain diagnostic apparatus.

In the two regions defined so as to satisfy the condition that the relative change ratio between the strain change amounts in each analysis model is equal to or more than the set value, the strain change amount in one of the regions is strained. The amount of change in strain in the other region is the decrease in strain.

The arithmetic unit has a filter unit having a function of passing the frequency components corresponding to the set load modes for the data input from the first and second strain sensors, while cutting the other frequency components. It is a configuration having.

Distribution of strain changes by stress analysis using the first analysis model with a structure corresponding to the crack diagnosis target and no cracks in the first region and the second region as the crack diagnosis target. By comparing the result of the above with the result of the distribution of the strain change amount by the stress analysis using the second analysis model having the structure corresponding to the crack diagnosis target and in the state where there is a crack, the strain in each analysis model is compared. A first set in the first area, which is set in an arrangement corresponding to two areas determined so as to satisfy the condition that the relative change rate between the amounts of change becomes equal to or more than the set value. A strain sensor and a second strain sensor installed in the second region are prepared, and when the crack diagnosis target is in a normal state where no crack has occurred, each of the first and second strains The process of accumulating the data input from the strain sensor as a normal data group and the data input from the first and second strain sensors at the time of the crack diagnosis of the crack diagnosis target are combined into the diagnostic data group. The process of setting and the process of comparing the diagnostic data group with the normal data group by statistical processing and determining the presence or absence of a crack in the crack diagnosis target based on the comparison result are performed. It is used as a method for diagnosing fissures.

According to the crack diagnosis method and apparatus of the present invention, it is possible to diagnose the presence or absence of cracks in the crack diagnosis target, and it is possible to improve the accuracy of the diagnosis.

It is a schematic diagram which shows the 1st Embodiment of a crack diagnostic apparatus. It is a figure for demonstrating the arrangement of the strain sensor in 1st Embodiment. It is a schematic diagram which shows the modification of 1st Embodiment. It is a schematic diagram which shows the 2nd Embodiment of a crack diagnostic apparatus. It is a figure for demonstrating the arrangement of the strain sensor in 2nd Embodiment. It is a schematic diagram which shows the 3rd Embodiment of a crack diagnostic apparatus. It is a figure for demonstrating the arrangement of the strain sensor in 3rd Embodiment. It is a schematic diagram which shows the 4th Embodiment of a crack diagnostic apparatus.

Hereinafter, embodiments for carrying out the present invention will be described with reference to the drawings.

[First Embodiment]
FIG. 1 is a schematic view showing a first embodiment of a crack diagnostic apparatus. 2A and 2B are diagrams for explaining the arrangement of strain sensors (strain gauges) in the crack diagnostic apparatus of FIG. 1, and FIGS. 2A and 2B are schematic views and diagrams showing analysis models for stress analysis, respectively. 2 (c) is a schematic diagram showing a region set based on the strain change rate.

The crack diagnostic apparatus of the present embodiment is shown by reference numeral 1 in FIG. 1, and as an example, a case where it is applied to the crack diagnosis target 100 is shown.

Here, first, the crack diagnosis target 100 shown in FIG. 1 will be described.

In the crack diagnosis target 100 in the present embodiment, for example, in a structure, the end portion of another flat structural member 102 is joined to the surface of the flat structural member 101 by welding, and the welded joint 103 is formed. It is as a formed part. Further, the structural member 101 is assumed to repeatedly apply a uniaxial load of tension and compression in the direction indicated by the arrow X.

Due to the influence of this load, the crack diagnosis target 100 may have a crack (fatigue crack) 104 due to fatigue as shown by the alternate long and short dash line at a location near the welded joint 103 in the structural member 101. There is. The location where the crack 104 is generated due to fatigue in the crack diagnosis target 100 is shown in FIG. 1 empirically or by analysis from the structure of the crack diagnosis target 100, the joint type, the direction of the applied load, and the like. It is clear that the position is shown.

Therefore, the crack diagnosis device 1 of the present embodiment is applied to diagnose the presence or absence of the occurrence of the crack 104 with respect to the crack diagnosis target 100 as described above.

The crack diagnosis device 1 of the present embodiment includes a strain sensor (strain gauge) 2 installed in an arrangement described later with respect to the crack diagnosis target 100, and strain sensors (strain gauges) 3a and 3b, and further. The configuration is provided with a calculation device 4 that processes data input from the strain sensors 2, 3a, and 3b.

The arrangement of the strain sensor 2 and the strain sensors 3a and 3b installed in the crack diagnosis target 100 is defined by the following method.

That is, first, as shown in FIG. 2A, as an analysis model for stress analysis (stress / strain analysis), an analysis model 5A having a structure corresponding to the structure of the crack diagnosis target 100 (see FIG. 1). Prepare.

At this time, as the analysis model 5A, it is preferable to use an analysis model 5A having a structure in which the structure of the crack diagnosis target 100 is standardized, rather than imitating the structure of the crack diagnosis target 100 as it is.

In view of this point, the analysis model 5A is shown in FIG. 2A as a structure for standardizing the structure including the structural member 101, the structural member 102, and the welded joint 103 of the crack diagnosis target 100 shown in FIG. As shown in the above, the surface of the plate member 6 is provided with a structure of a so-called out-of-plane gusset welded joint in which the end portion of the out-of-plane gusset plate 7 is joined by fillet welding or complete penetration welding.

This out-of-plane gusset welded joint is widely referred to as a fatigue design standard for structures, such as "Structure Fatigue Design Guidelines and Explanations" by the Japanese Society of Steel Construction and "Nippon Road Association". It is known as one of the welded joints shown in "Steel Road Bridge Fatigue Design Guidelines".

Further, as shown in FIG. 2 (b), it has the same structure as the analysis model 5A shown in FIG. 2 (a), and further corresponds to the location where the crack 104 occurs in the crack diagnosis target 100 of FIG. An analysis model 5B of a structure having a crack 8 at a location is prepared.

At this time, the length L of the crack 8 provided in the analysis model 5B is the length of the crack 104 actually generated in the crack diagnosis target 100 (see FIG. 1) at the time when the crack 104 becomes. The dimensions may be set according to whether or not detection is desired.

The length L of the crack 8 is preferably set to 5 mm or more. When the length L of the crack 8 is set to less than 5 mm, the result of the stress analysis using the analysis model 5B and the analysis model 5A in the state where there is no crack in FIG. 2 (a) are used. This is because the difference from the result of the stress analysis becomes small, and therefore it becomes difficult to obtain information on the amount of strain change described later.

Further, the length L of the crack 8 is preferably set to 50 mm or less. This is, for example, according to the "2002 Survey and Research Report on Steel Pier Corners on the Hanshin Expressway", the length is 100 mm in the damage judgment of fatigue cracks at the corners of steel piers. Cracks less than or equal to are subject to repair on a multi-year basis rather than immediate repair. Therefore, if the crack 104 of the crack diagnosis target 100 shown in FIG. 1 can be detected at the stage of 5 mm to 50 mm in length by using the crack diagnosis device 1 of the present embodiment, on-site confirmation and repair can be performed. It will be possible to secure sufficient time for planning.

Further, the length L of the crack 8 is more preferably set to less than 30 mm, and further preferably set to 10 mm to 20 mm. This is in view of the fact that the length of cracks that can be found by visual inspection in an actual structure is generally 30 mm or more. Therefore, according to this configuration, the crack 104 generated in the crack diagnosis target 100 can be detected by the crack diagnosis device 1 of the present embodiment even at a stage where it cannot be detected by visual inspection. ..

It is preferable to use the finite element method analysis (FEM analysis) as the stress analysis method for the analysis model 5A and the analysis model 5B. However, the stress analysis is performed to obtain the strain distribution in the analysis model 5A and the analysis model 5B. If information can be obtained, any stress analysis method other than the finite element method analysis may be adopted. Therefore, the analysis model 5A and the analysis model 5B may be created as models according to the stress analysis method to be used.

After creating the analysis model 5A and the analysis model 5B as described above, for each of the analysis model 5A and the analysis model 5B, the stress when the stress acts in the direction indicated by the arrow x in FIGS. 2 (a) and 2 (b). The analysis is performed, and the distribution of strains generated in the analysis model 5A and the analysis model 5B is obtained.

Next, the analysis result of the strain distribution obtained by the analysis model 5B in the state with crack 8 (see FIG. 2 (b)) is obtained by the analysis model 5A in the state without crack 8 (see FIG. 2 (a)). Compare with the analysis result of the distributed strain. By this comparison, how the strain at each position of the analysis model 5B with the crack 8 changes with reference to the strain at the same position of the analysis model 5A without the crack is determined as the amount of strain change. Therefore, the amount of this strain change is such that the strain at each position of the analysis model 5A in the crack-free state changes to a state in which the crack 8 has a predetermined length L, that is, with the occurrence of the crack 8. It is an index showing how it changes. In the following description, the value of the strain change amount is indicated by r.

Next, based on the information of the strain change amount r, as shown in FIG. 2C, two regions 9 and 10 regions satisfying the following conditions are determined for the analysis model 5B. Although not shown, it goes without saying that the regions 9 and 10 may be defined in the analysis model 5A.

In the present embodiment, the conditions for determining the two areas 9 and the region 10 is an increase of the amount of change r ₁ strain in the first region 9 strain, the strain variation r ₂ strain in the second region 10 of a reduction, and the relative rate of change of the amount of change r ₂ strain and the strain variation r ₁ (hereinafter, the strain that the change rate), the strain that can be detected significant differences in various statistical techniques The condition is that there is a difference in the rate of change. In this case, for example, the condition that the equivalence is rejected at the significance level of 5% to 1% may be adopted, but it is needless to say that the condition is not necessarily limited to this condition. The difference in distortion change ratio as described above is set to be equal to or larger than a predetermined magnitude, the difference between the change amount r ₂ strain in the change amount r ₁ and region 10 strain in the region 9 is set to be clarified This is to improve the detection accuracy of the crack 104 (see FIG. 1), which will be described later.

After the regions 9 and 10 in the analysis model 5B are defined, as shown in FIG. 1, in the actual crack diagnosis target 100, the arrangements correspond to the arrangements of the regions 9 and 10 in the analysis model 5B, respectively. Two regions 11 and 12 are defined. In FIG. 1, for convenience of illustration, the two regions 11 and 12 are indicated by a two-dot chain line, but in the actual crack diagnosis target 100, the arrangement of the regions 11 and 12 may be known. , There is no need to display it.

After that, one of the two regions 11 and 12 set as described above is set as the crack detection data acquisition region, and the other is set as the comparison data acquisition region.

FIG. 1 shows a case where the area 11 is set as the crack detection data acquisition area and the area 12 is set as the comparison data acquisition area.

A strain sensor 2 for acquiring the crack detection data a is installed in the region 11 designated as the crack detection data acquisition region. Further, in the area 12 designated as the comparison data acquisition area, two strain sensors 3a and 3b for acquiring the comparison data b are arranged and installed at two places in the area.

The strain sensor 2 and the strain sensors 3a and 3b installed in the crack diagnosis target 100 are not shown in order to suppress and protect the influence of light, temperature, moisture, oil, oxidizing gas, etc. received from the environment. It may be covered with a resin or a cover.

The crack detection data a is input to the arithmetic unit 4, and the comparison data b is input from the strain sensors 3a and 3b.

Next, the function of the arithmetic unit 4 will be described.

The computing device 4 is used for comparison between the crack detection data a input from the strain sensor 2 and the comparison data input from the strain sensors 3a and 3b when the crack diagnosis target 100 is in a normal state where no crack has occurred. It has a function of accumulating a data group including the data b as a normal data group.

Further, after the normal data group is accumulated, the arithmetic device 4 inputs from the strain sensor 2 at the crack diagnosis time when it is necessary to diagnose the presence or absence of the crack 104 in the crack diagnosis target 100. It has a function of setting a data group including the crack detection data a and the comparison data b input from the strain sensors 3a and 3b as the diagnostic data group.

Further, the arithmetic unit 4 has a function of comparing the diagnostic data group with the normal data group by statistical processing and determining whether or not the crack 104 has occurred in the crack diagnosis target 100 based on the comparison result. ing.

As a method of relative comparison of the diagnostic data group with the normal data group by the arithmetic unit 4, for example, the following method may be used.

As shown in FIG. 1, in the present embodiment, the strain sensor 2 and the strain sensors 3a and 3b are provided in an arrangement relatively close to the crack diagnosis target 100. Therefore, the strain sensor 2 and the strain sensors 3a and 3b are affected by disturbances such as temperature change and load change in the crack diagnosis target 100 in almost the same manner.

Therefore, the relative relationship (correlation) between the crack detection data a by the strain sensor 2 and the comparison data b by the strain sensors 3a and 3b, for example, the proportional relationship is a disturbance such as a temperature change or a load change. It is not easily affected by the above and is kept almost constant.

On the other hand, the region 11 provided with the strain sensor 2 and the region 12 provided with the strain sensors 3a and 3b are stress-analyzed using the analysis models 5A and 5B (see FIGS. 2A, 2B and 2C). Therefore, the strain change rate associated with the occurrence of the crack 8 is set corresponding to two regions 9 and 10 having a difference in strain change rate so that a significant difference can be detected by various statistical methods. Therefore, when a crack 104 occurs in the crack diagnosis target 100, the proportional relationship, which is the relative relationship between the crack detection data a by the strain sensor 2 and the comparison data b by the strain sensors 3a and 3b, is changed. It is estimated that the change occurs at a rate related to the strain change rate.

Therefore, in the arithmetic unit 4, the proportional relationship between the crack detection data a and the comparison data b in the diagnostic data group is equivalent to the proportional relationship between the crack detection data a and the comparison data b in the normal data group. The sex is tested by the F test, which is one of the statistical tests (also referred to as statistical hypothetical tests). In this test process, the equivalence test between the response curved surface for the proportional relationship in the diagnostic data group and the response curved surface for the proportional relationship in the normal data group may be performed.

As a result, when the equivalence is rejected, the arithmetic unit 4 determines that the crack 104 has occurred in the crack diagnosis target 100.

On the other hand, if the equivalence is not rejected, the arithmetic unit 4 determines that the crack 104 has not occurred in the crack diagnosis target 100.

The determination result of the presence or absence of the crack 104 in the crack diagnosis target 100 by the arithmetic unit 4 is output to a recording device (not shown) and recorded, or is output to a display device or an audio output device (not shown) as needed. May inform the observer.

Data transmission from the strain sensor 2 and the strain sensors 3a and 3b to the arithmetic unit 4 may be performed via a communication line such as a wired, wireless, or telephone, or a network such as the Internet. Similarly, the output from the arithmetic unit 4 to the recording device, the display device, and the voice output device may be performed via a communication line such as a wired, wireless, or telephone, or a network such as the Internet.

Therefore, according to the crack diagnosis device 1 of the present embodiment, it is possible to diagnose the presence or absence of the occurrence of the crack 104 in the crack diagnosis target 100.

Further, at any plurality of locations in the region 11 of the crack diagnosis target 100, even if the distance from the crack 104 is different, when the crack 104 occurs, the strain shows a similar increasing tendency. Similarly, at any plurality of locations within the region 12 of the crack diagnosis target 100, even if the distance from the crack 104 is different, when the crack 104 occurs, the strain shows a similar decreasing tendency.

Therefore, even if a plurality of strain sensors are provided in the crack diagnosis target 100, all the installation positions thereof are included in the area 11, or are included in the area 12, or the crack 104 is generated. However, if the strain is arranged in a region where the change in strain is small, it is difficult to accurately detect the occurrence of the crack 104 based on the detection data of each strain sensor.

On the other hand, the crack diagnostic apparatus 1 of the present embodiment is a region in which the strain change rate associated with the occurrence of the crack 104 is estimated to be the predetermined value based on the analysis results using the analysis models 5A and 5B. The strain sensor 2 and the strain sensors 3a and 3b are separately installed in the 11 and the region 12. Therefore, according to the crack diagnosis device 1 of the present embodiment, it is possible to improve the accuracy of the diagnosis of the presence or absence of the occurrence of the crack 104 in the crack diagnosis target 100. When the region 11 and the region 12 are defined, preliminary strain measurement may be performed on the magnitude and distribution of the strain so that the strain change rate can be accurately grasped, and the strain may be taken into consideration together with the analysis result. In this way, the installation areas of the strain sensor 2 and the strain sensors 3a and 3b that realize more accurate diagnosis can be determined.

Further, in the present embodiment, two strain sensors 3a and 3b are provided in the region 12, and comparison data b can be acquired in each of the two strain sensors 3a and 3b.

For example, when there is only one comparison data b, when the comparison data b and the crack detection data a change in the same direction of increase or decrease, the relative difference in the strain change rate is statistically superior. In order to have a large difference, it is necessary to have a large difference, and therefore the detection sensitivity becomes low.

On the other hand, in the present embodiment, since two comparison data b are acquired, even if the two comparison data b both change in the same direction as the crack detection data a, the comparison is performed. The relative difference between the data b and the crack detection data a and the strain change rate can be detected at a smaller stage. Further, if one of the two comparison data b changes in the direction opposite to that of the crack detection data a, the detection accuracy can be further improved.

In the present embodiment, a configuration including two strain sensors 3a and 3b is illustrated as a strain sensor for acquiring comparison data b, but three or more strain sensors for acquiring comparison data b are provided. Of course, the configuration may be adopted.

[Modified example of the first embodiment]
FIG. 3 shows a modified example of the first embodiment. In FIG. 3, the same reference numerals as those shown in the first embodiment are designated by the same reference numerals, and the description thereof will be omitted.

In the first embodiment, as shown in FIG. 1, the area 11 is set as the crack detection data acquisition area and the area 12 is set as the comparison data acquisition area. However, as shown in FIG. The region 11 may be set as the comparison data acquisition region, and the region 12 may be set as the crack detection data acquisition region.

In this case, if the strain sensor 2 for acquiring the crack detection data a is installed in the region 12, and the strain sensors 3a and 3b for acquiring the comparison data b are installed in the region 11. good.

In this modified example having such a configuration, it can be used in the same manner as in the first embodiment, and the same effect as in the first embodiment can be obtained.

[Second Embodiment]
In the first embodiment, as shown in FIGS. 2A, 2B, and 2C, strain is generated with the occurrence of crack 8 based on the analysis results using the analysis model 5A and the analysis model 5B. The region 9 in which the strain increases and the region 10 in which the strain decreases are obtained under the condition that the strain change rates of the two regions have a difference in the strain change rate so that a significant difference can be detected by various statistical methods. .. In the crack diagnosis target 100 shown in FIG. 1, the area 11 and the area 12 for sensor installation are defined in the arrangement corresponding to the area 9 and the area 10 obtained for the analysis model 5B.

However, in the actual crack diagnosis target 100, a surface or space for installing the sensor may not be secured at a location corresponding to the region 10 in the analysis model 5B due to structural restrictions or the like.

In this case, the crack diagnostic apparatus may have the configuration shown in FIG.

FIG. 4 is a schematic view showing a second embodiment of the crack diagnostic apparatus. FIG. 5 is a diagram for explaining the arrangement of the strain sensor in the present embodiment. In FIGS. 4 and 5, the same reference numerals as those shown in the first embodiment are designated by the same reference numerals, and the description thereof will be omitted.

The crack diagnosis device of the present embodiment is shown by reference numeral 1A in FIG. 4, and similarly to the first embodiment, the strain sensors 2 and the strain sensors 3a and 3b installed in the crack diagnosis target 100 are used. Although it has a configuration including an arithmetic unit 4, the arrangement of the strain sensor 2 and the strain sensors 3a and 3b is determined by a method different from that of the first embodiment.

That is, in the present embodiment, similarly to the first embodiment, the analysis model 5A in the state without cracks shown in FIG. 2A and the analysis model 5B in the state with cracks 8 shown in FIG. 2B are used. Based on the analysis results used, how the strain at each position of the analysis model 5B changes with reference to the strain at the same position of the analysis model 5A is determined as the strain change amount r.

Next, based on the information of the strain change amount r, as shown in FIG. 5, two regions 13 and 14 regions satisfying the following conditions are defined for the analysis model 5B. Although not shown, it goes without saying that the regions 13 and 14 may be defined in the analysis model 5A.

The conditions for defining these two regions 13 and 14 are that the strain change amount r ₃ in the first region 13 is an increase in strain, and the strain change amount r ₄ in the second region 14 is from the set value. Is also a condition that the change (increase / decrease) of the strain is small. And, the strain rate of change of the strain variation r ₃ and the strain variation r _4, the conditions are provided that have a difference of various statistical techniques in significant differences detectable Such distortion change ratio. The reason why the difference in the strain change rate is set to be equal to or larger than the predetermined magnitude is the same as that in the first embodiment.

After the regions 13 and 14 in the analysis model 5B are defined, as shown in FIG. 4, in the actual crack diagnosis target 100, the locations corresponding to the arrangement of the regions 13 (see FIG. 5) of the analysis model 5B are located. , The area 15 for acquiring the crack detection data a is defined. Further, in the crack diagnosis target 100, a region 16 for acquiring comparison data b is defined at a location corresponding to the arrangement of the region 14 (see FIG. 5) of the analysis model 5B.

In FIG. 4, for convenience of illustration, the two regions 15 and 16 are indicated by a two-dot chain line, but in the actual crack diagnosis target 100, the arrangement of the regions 15 and 16 may be known. , There is no need to display it.

The same strain sensor 2 as in the first embodiment is installed in the region 15, and two strain sensors 3a and 3b are arranged and installed in the region 16 at two locations in the region. It should be noted that this embodiment is the same as the first embodiment in that a configuration including three or more strain sensors for acquiring comparative data b may be adopted.

The crack diagnostic apparatus 1A of the present embodiment having the above configuration can also be used in the same manner as in the first embodiment, and the same effect as in the first embodiment can be obtained.

Further, the installation areas of the strain sensor 2 and the strain sensors 3a and 3b can be set while avoiding the structural limitation of the crack diagnosis target 100.

[Third Embodiment]
In the first embodiment, as shown in FIG. 2C, the sensor is installed in the crack diagnosis target 100 as shown in FIG. 1 in an arrangement corresponding to the regions 9 and 10 obtained for the analysis model 5B. Region 11 and region 12 were defined.

However, in the actual crack diagnosis target 100, a surface or space for installing the sensor may not be secured at a location corresponding to the region 9 in the analysis model 5B due to structural restrictions or the like.

In this case, the crack diagnostic apparatus may have the configuration shown in FIG.

FIG. 6 is a schematic view showing a third embodiment of the crack diagnostic apparatus. FIG. 7 is a diagram for explaining the arrangement of the strain sensor in the present embodiment. In FIGS. 6 and 7, the same reference numerals as those shown in the first embodiment are designated by the same reference numerals, and the description thereof will be omitted.

The crack diagnosis device of the present embodiment is shown by reference numeral 1B in FIG. 6, and similarly to the first embodiment, the strain sensors 2 and the strain sensors 3a and 3b installed in the crack diagnosis target 100 are used. Although it has a configuration including the arithmetic device 4, the arrangement of the strain sensor 2 and the strain sensors 3a and 3b is determined by a method different from that of the first embodiment.

That is, in the present embodiment, similarly to the first embodiment, the analysis model 5A in the state without cracks shown in FIG. 2A and the analysis model 5B in the state with cracks 8 shown in FIG. 2B are used. Based on the analysis results used, how the strain at each position of the analysis model 5B changes with reference to the strain at the same position of the analysis model 5A is determined as the strain change amount r.

Next, based on the information of the strain change amount r, as shown in FIG. 7, two regions 17 and 18 that satisfy the following conditions are defined for the analysis model 5B. Although not shown, it goes without saying that the regions 17 and 18 may be defined in the analysis model 5A.

The conditions for defining these two regions 17 and 18 are that the strain change amount r ₅ in the first region 17 is a decrease in strain, and the strain change amount r ₆ in the second region 18 is from the set value. Is also a condition that the change (increase / decrease) of the strain is small. And, the strain rate of change in the change amount r ₆ and strain variation r ₅ strain, conditions are provided that have a difference of various statistical techniques in significant differences detectable Such distortion change ratio. The reason why the difference in the strain change rate is set to be equal to or larger than the predetermined magnitude is the same as that in the first embodiment.

After the regions 17 and 18 in the analysis model 5B are defined, as shown in FIG. 6, in the actual crack diagnosis target 100, the locations corresponding to the arrangement of the regions 17 (see FIG. 7) of the analysis model 5B are located. , The area 19 for acquiring the crack detection data a is defined. Further, in the crack diagnosis target 100, a region 20 for acquiring comparison data b is defined at a location corresponding to the arrangement of the region 18 (see FIG. 7) of the analysis model 5B.

In FIG. 6, for convenience of illustration, the two regions 19 and 20 are indicated by a two-dot chain line, but in the actual crack diagnosis target 100, the arrangement of the regions 19 and 20 may be known. , There is no need to display it.

The same strain sensor 2 as in the first embodiment is installed in the region 19, and two strain sensors 3a and 3b are arranged and installed in the region 20 at two locations in the region. It should be noted that this embodiment is the same as the first embodiment in that a configuration including three or more strain sensors for acquiring comparative data b may be adopted.

The crack diagnostic apparatus 1B of the present embodiment having the above configuration can also be used in the same manner as in the first embodiment, and the same effect as in the first embodiment can be obtained.

Further, the installation areas of the strain sensor 2 and the strain sensors 3a and 3b can be set while avoiding the structural limitation of the crack diagnosis target 100.

[Fourth Embodiment]
FIG. 8A is a schematic view showing a fourth embodiment of the crack diagnostic apparatus. 8 (b), (c), and (d) are schematic views showing the relationship between the load acting on the structural member to be diagnosed as a crack and the stress distribution.

In FIGS. 8 (a), (b), (c), and (d), the same components as those shown in the first embodiment are designated by the same reference numerals, and the description thereof will be omitted.

In an actual structure, in addition to the case where only the uniaxial load shown by the arrow X in FIG. 1 works, a plurality of load modes such as the uniaxial load and the bending load may work in an overlapping manner.

As shown in FIG. 8B, for example, when the uniaxial load mode in the direction indicated by the arrow is activated for the flat plate-shaped structural member 101, a uniform stress distribution is generated in the plate width direction.

On the other hand, as shown in FIG. 8C, when the structural member 101 is subjected to the bending load mode as shown by the arrow, a stress distribution that is not uniform in the plate width direction occurs.

As described above, since the stress distribution is different in the different load modes, as shown in FIG. 8D, when the uniaxial load mode and the bending load mode are superimposed on the structural member 101, the stress distribution becomes non-uniform. turn into. Further, in general, since the frequencies of the uniaxial load mode and the bending load mode are different, even if each load mode is a simple vibration, the stress acting at a certain position is no longer a simple vibration.

Therefore, the crack detection data a acquired by the strain sensor 2 installed in the region 11 of FIG. 8A and the comparison data b acquired by the strain sensors 3a and 3b installed in the region 12 The relative relationship includes a proportional relationship regarding the uniaxial load mode and a proportional relationship regarding the bending load mode.

In this case, the proportional relationship of each load mode is not easily affected by disturbances such as temperature change and load change, and even if it is kept almost constant, cracks occur in the state where the two proportional relationships exist. It is difficult to detect the change in proportional relationship that occurs when a crack 104 occurs in the diagnosis target 100.

Therefore, as shown in FIG. 8A, the crack diagnostic apparatus 1C of the present embodiment has the same configuration as that of the first embodiment, and the arithmetic unit 4 is provided with the filter unit 21.

The filter unit 21 passes the frequency components corresponding to the set load modes for the crack detection data a input from the strain sensor 2 and the comparison data b input from the strain sensors 3a and 3b, while passing the frequency components. Other frequency components have a function to cut.

The filter unit 21 may appropriately select and use a bandpass filter, a lowpass filter, and a highpass filter according to the wavelength of the frequency component to be passed.

According to the crack diagnosis device 1C of the present embodiment having the above configuration, even when a plurality of load modes are superimposed on the crack diagnosis target 100, the arithmetic unit 4 passes through the filter unit 21. Based on the data of the frequency component corresponding to the predetermined load mode, it is possible to diagnose the presence or absence of the crack 104 in the crack diagnosis target 100.

Therefore, the same effect as that of the first embodiment can be obtained in the crack diagnostic apparatus 1C of the present embodiment.

[Arrangement of strain sensor]
In the second embodiment, for the crack diagnosis target 100, the region 16 in which the strain sensors 3a and 3b for acquiring the comparison data b are installed corresponds to the arrangement of the region 14 in the analysis model 5B shown in FIG. I decided. As one of the conditions for obtaining the region 14, the condition that the strain change amount r ₄ in the region 14 is a strain change (increase / decrease) smaller than the set value is provided.

However, in the analysis model 5B, if equal to or greater than the value distortion change ratio is set between the strain variation r ₄ of strain variation r ₃ and the region 14 of the region 13, the strain variation r ₄ regions 14 Is not necessarily required to have a strain change (increase / decrease) smaller than the set value. Even in this case, when a crack 104 occurs in the crack diagnosis target 100, the proportional relationship between the crack detection data a by the strain sensor 2 and the comparison data b by the strain sensors 3a and 3b is changed. The same changes as in the case of the first embodiment occur.

In the third embodiment, for the crack diagnosis target 100, the region 20 in which the strain sensors 3a and 3b for acquiring the comparison data b are installed corresponds to the arrangement of the region 18 in the analysis model 5B shown in FIG. I decided. As one of the conditions for obtaining the region 18, the condition that the strain change amount r ₆ in the region 18 is a strain change (increase / decrease) smaller than the set value is provided.

However, in the analysis model 5B, if the strain change rate _{between the strain change amount r 5} in the region 17 and the strain change amount r _{6 in the} region 18 is equal to or more than the set value, the strain change amount r _{6 in the region 18} Is not necessarily required to have a strain change (increase / decrease) smaller than the set value. Even in this case, when a crack 104 occurs in the crack diagnosis target 100, the proportional relationship between the crack detection data a by the strain sensor 2 and the comparison data b by the strain sensors 3a and 3b is changed. The same changes as in the case of the first embodiment occur.

Here, the points of arrangement of the strain sensor in the present invention will be summarized from the above points and the above-described embodiments and modifications. The arrangement of the area where the strain sensor 2 for acquiring the crack detection data a and the area for installing the strain sensors 3a and 3b for acquiring the comparison data b correspond to the arrangement of the two areas in the analysis model 5B. The basic conditions for defining these two areas are as follows.

This is a strain variation r _x in the first region, the strain rate of change of the strain variation r _y in the second region, various statistical techniques in significant differences detectable Such distortion change ratio The condition is that it is greater than or equal to the difference.

Long as this condition is satisfied, the strain variation r _x and strain variation r _y is a case of a decrease in the other strain with increasing one of strain, and when an increase in both strain, both It may be any case where the strain is reduced.

[Analysis model]
In each of the above-described embodiments and modifications, the analysis models 5A and 5B do not directly imitate the structure of the crack diagnosis target 100, but have a structure that standardizes the structure of the crack diagnosis target 100. Furthermore, this standardized structure is shown in the "Structure Fatigue Design Guideline / Explanation" of the Japanese Society of Steel Construction and the "Steel Road Bridge Fatigue Design Guideline" of the Japan Road Association. The structure of the out-of-plane gusset welded joint, which is one of the welded joints, was adopted.

Therefore, for various structures, when the portion created as the structure of the out-of-plane gusset welded joint is used as the reference for the fatigue design standard as described above, the analysis model 5A, A common one can be used as 5B. Further, with respect to the analysis model 5B, the regions 9 and 10 shown in FIG. 2C, the regions 13 and 14 shown in FIG. 5, and the regions 17 and 18 shown in FIG. 7 can be obtained in advance. ..

Of course, the analysis model may be changed according to the structure of the crack diagnosis target. In that case, as an analysis model of the structure that standardizes the structure to be diagnosed as a crack, for example, a load transmission type cross welded joint, an in-plane gusset welded joint, a joint in which a cover plate is attached by fillet welding, and the like. It goes without saying that the welded joint structure shown in the design guideline may be provided.

Further, an analysis model in a crackless state having a welded joint structure shown in each fatigue design guideline and an analysis model in a cracked state are prepared, and the strain change rate is equal to or higher than the set value. By obtaining information about the plurality of regions in advance, the crack diagnostic devices 1, 1A, 1B, and 1C can be quickly applied to various crack diagnosis targets.

As the analysis model in the crackless state and the analysis model in the cracked state, it is preferable to use the one having the standardized structure as described above, but the structure imitates the structure of the crack diagnosis target as it is. Of course, the one may be used. Even in this case, the crack diagnostic devices 1, 1A, 1B, and 1C can obtain the same effects as those of the above-described embodiments and modifications.

[Application example]
In each of the above-described embodiments and modifications, the crack diagnostic devices 1, 1A, 1B, and 1C have been described as diagnosing the presence or absence of crack 104 in the crack diagnosis target 100. It may be applied to detect the growth of the crack 104 that has occurred.

Here, considering the phenomenon of the growth of the crack 104, the growth of the crack 104 is regarded as a phenomenon in which the crack 104 is generated at a position on the extension of the tip side of the crack 104 that has already been generated. be able to.

Therefore, if the presence or absence of the occurrence of the crack 104 at the location is diagnosed by focusing only on the portion that is an extension of the tip side of the existing crack 104, the existing crack diagnosis target 100 as a whole can be used for the existing crack. The presence or absence of the growth of the crack 104 can be detected.

In view of this point, in the same configuration as the crack diagnostic devices 1, 1A, 1B, 1C of each of the above-described embodiments and modifications, the existing cracks to be diagnosed for the presence or absence of crack 104 are located. Set at a location that is an extension of the tip side of 104. That is, in the same configuration as the crack diagnostic devices 1, 1A, 1B, 1C of each of the above-described embodiments and modifications, the regions 11, 12, regions 15, 16, and regions where the strain sensor 2 and the strain sensors 3a, 3b are installed. 19 and 20 have a configuration in which the location to be diagnosed for the presence or absence of the occurrence of the crack 104 is shifted to the location on the extension of the tip side of the existing crack.

The data output from these strain sensors 2 and strain sensors 3a and 3b may be processed by an arithmetic unit having the same functions as the arithmetic unit 4 of each of the above-described embodiments and modifications.

According to this configuration, the arithmetic unit can detect the presence or absence of the growth of the existing crack 104 in the crack diagnosis target 100.

The present invention is not limited to each of the above-described embodiments and modifications, and the arithmetic unit 4 and the crack detection data a when performing a relative comparison of the diagnostic data group with the normal data group. A relative relationship other than the proportional relationship with the comparison data b may be used.

As a method of relative comparison of the diagnostic data group with the normal data group performed by the arithmetic unit 4, the proportional relationship between the crack detection data a and the comparison data b in the diagnostic data group is for crack detection in the normal data group. The equivalence of the proportional relationship between the data a and the comparison data b may be tested by any statistical test method other than the F test.

Further, as a method of relative comparison of the diagnostic data group with the normal data group performed by the arithmetic unit 4, a method of verifying the abnormality of the diagnostic data group with respect to the normal data group by the MT method (Mahalanobis Taguchi method) is adopted. May be good. In this case, the arithmetic unit 4 may be provided with a function of determining that the crack 104 has occurred in the crack diagnosis target 100 when it is determined that the abnormality is present.

The arithmetic unit 4 provided with the filter unit 21 shown in the fourth embodiment may be used in the modified example of the first embodiment, the second embodiment, and the third embodiment.

In FIGS. 1 to 8, the shapes of regions 9, 10, regions 11, 12, regions 13, 14, regions 15, 16, regions 17, 18, and regions 19, 20 are shown as ellipses and polygons, respectively. Of course, it may have any shape. Of course, the sizes of regions 9, 10, regions 11, 12, regions 13, 14, regions 15, 16, regions 17, 18, and regions 19, 20 may be sizes other than those shown in the drawings. ..

The crack diagnosis target 100 may be any part other than the joint portion by the welded joint between the structural members as long as the position where the crack occurs is known in the structure.

Of course, various other changes can be made without departing from the gist of the present invention.

2 Strain sensor, 3a, 3b Strain sensor, 4 Arithmetic logic unit, 5A, 5B analysis model, 8 cracks, 9 regions, 10 regions, 11 regions, 12 regions, 13 regions, 14 regions, 15 regions, 16 regions, 17 regions , 18 area, 19 area, 20 area, 21 filter part, 100 crack diagnosis target, 104 crack, a crack detection data (data), b comparison data (data)

Claims (9)

A first strain sensor installed in the first area of the crack diagnosis target,
A second strain sensor installed in the second region of the crack diagnosis target,
It is provided with an arithmetic unit for inputting crack detection data acquired by the first strain sensor and comparison data acquired by the second strain sensor.
The arithmetic unit
A function of accumulating the crack detection data and the comparison data, which are input when the crack diagnosis target is in a normal state where no crack has occurred, as a normal data group.
A function of setting the crack detection data and the comparison data input at the crack diagnosis time of the crack diagnosis target in the diagnostic data group, and
Regarding the proportional relationship between the crack detection data and the comparison data in the diagnostic data group, the equivalence of the proportional relationship between the crack detection data and the comparison data in the normal data group is statistically determined. It is provided with a function of determining the presence or absence of cracks in the crack diagnosis target by performing a test by a temporary test.
In each of the first region and the second region, stress acts in a predetermined direction in the first analysis model having a structure corresponding to the crack diagnosis target and in a state without cracks. By the result of the strain distribution by the stress analysis and the stress analysis when the stress acts in the predetermined one direction in the second analysis model having the structure corresponding to the crack diagnosis target and in the state where there is a crack. Depending on the amount of strain change, which is the result of comparison with the result of strain distribution, the relative change rate between the amount of strain change in the first region and the amount of strain change in the second region is equal to or greater than the set value. It is said that the two regions are set as the crack diagnosis target in the arrangement corresponding to the two regions determined to satisfy the condition of becoming.
Crack diagnostic device. The crack according to claim 1, wherein the test of equivalence is a test of equivalence between the response curved surface for the proportional relationship in the diagnostic data group and the response curved surface for the proportional relationship in the normal data group. Diagnostic device. The arithmetic unit has a filter unit having a function of passing the frequency components corresponding to the set load modes for the data input from the first and second strain sensors, while cutting the other frequency components. The crack diagnostic apparatus according to claim 1 or 2. The two regions defined so as to satisfy the condition that the relative change rate between the strain change amount in the first region and the strain change amount in the second region is equal to or more than a set value are set. either an increase of strain variation in one region is strain claim 1 crack diagnostic apparatus according strain variation is reduced in the strain in the other region. The two regions defined so as to satisfy the condition that the relative change rate between the strain change amount in the first region and the strain change amount in the second region is equal to or more than a set value are set. either an increase of strain variation is strain at one region, the other region of the strain variation is predetermined set crack diagnostic apparatus according to claim 1, wherein a small distortion change than value. The two regions defined so as to satisfy the condition that the relative change rate between the strain change amount in the first region and the strain change amount in the second region is equal to or more than a set value are set. any strain variation in one region is the strain decrease in the other region of the strain variation is predetermined set crack diagnostic apparatus according to claim 1, wherein a small distortion change than value. The crack detection data is acquired by the first strain sensor installed in the first region of the crack diagnosis target, and compared by the second strain sensor installed in the second region of the crack diagnosis target. Processing to acquire data for
A process of accumulating the crack detection data and the comparison data in a normal state in which no crack has occurred in the crack diagnosis target as a normal data group, and
A process of setting the crack detection data and the comparison data of the crack diagnosis time of the crack diagnosis target in the diagnostic data group, and
Regarding the proportional relationship between the crack detection data and the comparison data in the diagnostic data group, the equivalence of the proportional relationship between the crack detection data and the comparison data in the normal data group is statistically determined. by assaying the temporary test have rows and process for determining the presence or absence of the occurrence of cracks in the crack diagnosis target,
In each of the first region and the second region, stress acts in a predetermined direction in the first analysis model having a structure corresponding to the crack diagnosis target and in a state without cracks. By the result of the strain distribution by the stress analysis and the stress analysis when the stress acts in the predetermined one direction in the second analysis model having the structure corresponding to the crack diagnosis target and in the state where there is a crack. Depending on the amount of strain change, which is the result of comparison with the result of strain distribution, the relative change rate between the amount of strain change in the first region and the amount of strain change in the second region is equal to or greater than the set value. It is said that the two regions are set as the crack diagnosis target in the arrangement corresponding to the two regions determined to satisfy the condition of becoming.
Crack diagnosis method. The crack according to claim 7 , wherein the test of equivalence is a test of equivalence between the response curved surface for the proportional relationship in the diagnostic data group and the response curved surface for the proportional relationship in the normal data group. Diagnostic method. For data input from said first and second respective strain sensor, while passing a frequency component corresponding to the load mode set, the frequency component other than it claims 7 or 8, wherein further performs processing to cut How to diagnose cracks.

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