JP2006098283A - Method and apparatus for detecting defect in steel structure - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To detect defects, such as surface cracks, generated in a large steel structure, such as a bridge and each kind of cargo handling equipment, remotely, easily, and reliably by a simple apparatus configuration without assembling a scaffold for defect detection work, without requiring any heating means of the steel structure, and without requiring any means for applying a load to the steel structure, such as a shaking apparatus. <P>SOLUTION: In a defect detection method for remotely detecting defects S that are present in a girder 4 of a ceiling crane as a large steel structure, the girder 4 for causing repetitive stress variations by the drive of a crane carriage 7 is photographed by an infrared camera 9, and temperature distribution variations on the surface of the girder 4 are measured as images, thus detecting defects present in the ceiling crane. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to a method and an apparatus for detecting a defect in a steel structure, in particular, a steel structure without forming a scaffold for detecting a defect such as a surface crack generated in a large steel structure such as a bridge or various cargo handling machines. It is possible to detect easily and reliably from a remote location with a simple device configuration without the need for heating means and without the need for applying load to the steel structure such as a vibration device. The present invention relates to a steel structure defect detection method and apparatus.

  Aging of large steel structures such as bridges and various cargo handling machines has become a social problem, and there is a strong need for this countermeasure. In order to implement effective countermeasures against aging, it is necessary to know the degree of aging. For this reason, it is necessary to detect a defect in the target steel structure, and for example, a defect detection method using color check, magnetic particle flaw detection, ultrasonic flaw detection, or the like has been conventionally performed.

  Under such circumstances, in recent years, a defect detection method using an infrared camera has been performed. For example, as a means of detecting defects such as cracks and delamination in the concrete wall inside the tunnel, the tunnel wall surface is heated by some heat source instead of the conventional diagnostic method by the hammering test, and the temperature distribution between the normal part and the defective part A method of detecting a defective part from the difference is used. As a prior art of an active defect detection method using these infrared cameras, for example, Japanese Patent Application Laid-Open No. 2003-185608 (Patent Document 1) uses a means for uniformly irradiating infrared rays to a concrete structure, and uses a temperature distribution. A method for detecting an internal defect of a concrete structure by measuring the above is disclosed.

  In addition, JP 2004-37201 A (Patent Document 2) discloses defect identification using a difference between a temperature distribution image before and after heating and a difference between healthy and unhealthy sample thermal images for a steel structure. A method is disclosed.

  From another viewpoint, for example, as a method for detecting a crack generated in a test piece during processing of a metal material, a method using an infrared image using a thermoelastic property of the material is disclosed in Japanese Patent Laid-Open No. 10-82726 ( It is disclosed in Patent Document 3).

JP 2003-185608 A Japanese Patent Application Laid-Open No. 2004-37201 JP-A-10-82726

  However, the conventional defect detection method described above has the following problems.

  (1) Defect detection methods such as color check, magnetic particle flaw detection, and ultrasonic flaw detection cannot be measured unless a person approaches the vicinity of the structure to be measured in order to perform necessary measures. For example, in magnetic particle flaw detection, it is necessary to apply a magnetic field after removing rust and the like in the detection area to obtain a measurable surface property. For this reason, in the case of a large structure, it is impossible to perform work unless a scaffold is assembled for inspection and preparations are made for work at a high place so that workers can reach the inspection area. As a result, more manpower and costs must be spent on the work of building the scaffold than the defect detection costs. In order to solve these problems, a defect detection method using an infrared thermograph has been proposed in recent years in order to realize remote measurement, and for the convenience of inspecting a wide area at once.

  (2) However, the defect detection method by infrared thermograph disclosed in the above-mentioned Patent Document 1 or 2 is premised on infrared irradiation for heating. Although it can be applied to a slow object, it is difficult to create a temperature distribution that can be detected by an infrared camera unless the infrared ray is irradiated from a close distance to a steel structure having a high thermal diffusion rate. For this reason, in order to heat, the means to approach to a measuring object is needed, and it becomes necessary to assemble a scaffold etc. like the method of said (1).

  (3) The defect detection method by infrared thermograph disclosed in the above-mentioned Patent Document 3 is to take a thermal image with an infrared camera in order to detect cracks generated during processing such as pressing or punching a metal material, It is what you use. However, when this method is applied to the detection of defects in large steel structures, it is necessary to positively apply load load fluctuations to the large steel structures. The device cannot easily and reliably detect defects. This is because it is not only necessary to prepare a separate vibration device for giving load changes to large steel structures, but the load applied to the structure is naturally large enough not to damage the structure. The temperature change due to such a small load is minute, and in order to capture the minute temperature change as a thermal image, it is necessary to judge using multiple thermal image data, This is because the detection device becomes complicated.

  Accordingly, an object of the present invention is to solve the above-described problems, and defects such as surface cracks generated in large steel structures such as bridges and various cargo handling machines can be obtained without forming a scaffold for defect detection work. Detects easily and reliably from a remote location with a simple device configuration without the need for heating means for steel structures and without the need for applying load to steel structures such as vibration devices. An object of the present invention is to provide a method and apparatus for detecting defects in a steel structure that can be performed.

  The inventors of the present invention have obtained the following knowledge as a result of intensive studies to achieve the above-described object.

  (1) Focusing on the thermoelastic effect of the structure, if the thermoelastic effect that has been used as a means for visualizing the stress state is applied to the detection of defects in the steel structure, all the above-mentioned problems will be solved. Can do.

  (2) When the thermoelastic effect is applied to the detection of defects in steel structures, especially in large steel structures, even if load load fluctuations are given, the temperature change caused by them is very small, so it was difficult to capture as a thermal image. . However, recent improvements in the performance of infrared cameras have made it possible to capture even defects in metals that have good thermal diffusivity.

  (3) Load handling fluctuations such as bridges or overhead cranes naturally occur due to vibrations caused by running of the vehicle. Obtainable.

  The present invention has been made on the basis of the findings (1) to (3) described above, and is characterized by the following.

  The invention according to claim 1 is a defect detection method for detecting a defect in a steel structure from a remote location, and images the steel structure in which repeated stress fluctuations are generated with an infrared camera, and the surface of the steel structure is detected. This is characterized in that the temperature distribution fluctuation of the steel is measured as an image, thereby detecting defects present in the steel structure.

  The invention described in claim 2 is characterized in that, in the invention described in claim 1, the location and size of the defect are detected by obtaining the autocorrelation of the image data of the temperature distribution fluctuation.

  According to a third aspect of the present invention, in the first aspect of the present invention, a temperature distribution data that varies in synchronization with the reference signal is extracted using a part of the temperature variation data of the temperature distribution variation image data as a reference signal. The feature is to detect the location and size of the defect by integrating the temperature distribution data for each fluctuation cycle.

  According to a fourth aspect of the present invention, in the first aspect of the present invention, the information correlated with the stress fluctuation of the steel structure is sensed, this is used as a reference signal, and the fluctuating temperature distribution data is synchronized with the information. It is characterized by detecting the location and size of the defect by taking out and integrating the temperature distribution data for each fluctuation cycle.

  The invention described in claim 5 is characterized in that, in the invention described in claim 3 or 4, the location and size of the defect are detected by taking a correlation with image data other than the reference signal.

  The invention according to claim 6 is characterized in that, in the invention according to any one of claims 1 to 5, the steel structure is a bridge or a cargo handling machine.

  The invention according to claim 7 is an infrared camera that takes an image of a steel structure in which stress fluctuations repeatedly occur and measures the temperature distribution fluctuation of the surface of the steel structure as an image, and the temperature distribution fluctuation by the infrared camera. And a defect detection means for detecting defects existing in the steel structure.

  The invention described in claim 8 is characterized in that, in the invention described in claim 7, the defect detecting means detects the autocorrelation of the image data of the temperature distribution fluctuation and detects the location and size of the defect. is there.

  According to a ninth aspect of the present invention, in the seventh aspect of the invention, the defect detecting means uses the temperature variation data of a part of the image data of the temperature distribution variation as a reference signal and varies in synchronization with the reference signal. It is characterized by detecting the location and size of the defect by taking out the temperature distribution data and integrating the temperature distribution data for each fluctuation cycle.

  According to a tenth aspect of the present invention, in the seventh aspect of the invention, the defect detection means senses information correlated with the stress fluctuation of the steel structure, uses this as a reference signal, and changes in synchronization with the information. The temperature distribution data is extracted, and the temperature distribution data is integrated for each fluctuation cycle, thereby detecting the location and size of the defect.

  The invention described in claim 11 is characterized in that, in the invention described in claim 9 or 10, the defect detection means detects the location and size of the defect by taking a correlation with image data other than the reference signal. It is what you have.

  According to the present invention, defects such as surface cracks generated in large steel structures such as bridges and various cargo handling machines do not require a means for heating the steel structure without forming a scaffold for defect detection work, and Further, it is possible to easily and surely detect from a remote place with a simple device configuration without requiring a means for applying a load to the steel structure such as a vibration device.

  Next, an embodiment of a steel structure defect detection device of the present invention will be described with reference to the drawings.

  FIG. 1 is a perspective view showing a test piece, and FIG. 2 is a drawing-substituting photograph showing image data as a result of calculating autocorrelation using thermal image data of temperature distribution fluctuation when a variable load is applied to the test piece. It is.

  The test piece 1 is formed by welding a small piece 3 to a steel plate 2. This test piece 1 is subjected to a fatigue tester to generate an artificial crack in the weld. In this way, the surface temperature distribution of the test piece 1 is measured using the infrared thermograph when a crack is generated while applying a variable load to the test piece 1. Depending on the state of stress concentration in the cracked part, it is possible to recognize the crack from the thermal image of the temperature distribution fluctuation accompanying the repeatedly fluctuating load. When autocorrelation is calculated using the measured thermal image data in order to make the presence or absence of cracks clearer, image data as shown in FIG. 2 is obtained. In FIG. 2, it can be seen that a portion with a high luminance is a crack occurrence portion, and it is possible to identify a defect by the method of the present invention.

  A case where an actual overhead crane defect is detected by this method will be described with reference to the drawings.

  FIG. 3 is a schematic diagram when autocorrelation calculation is performed in the case of detecting an overhead crane defect by this method.

  In FIG. 3, 4 is the girder of the overhead crane, 5 is the girder traveling rail laid on the building 6, 7 is the crane carriage that runs on the girder 4, 8 is the luggage suspended from the crane carriage 7, Reference numeral 9 is an infrared camera, and 10 is a measuring device as defect detection means.

  When detecting the presence or absence of a defect (S) in the girder 4, the infrared camera 9 is set on the ground (the distance to the girder 4 is about 10 to 20 m), and the load 8 is raised and lowered in the same manner as when the crane is operated. For example, the lifting and lowering of the luggage 8 is suddenly stopped or actuated as necessary. In this way, the girder 4 undergoes stress fluctuation (usually the same frequency as the residual vibration due to the natural frequency of the girder 4), that is, a slight temperature distribution fluctuation. The minute temperature distribution fluctuation image at this time is taken into the personal computer by the infrared camera 9. If the crack is large or the stress concentration is sufficient, the crack can be detected from the real-time thermal image. When the defect detection location is far away, a telephoto lens is used.

  However, in the case of an initial crack or when the lifting load is relatively light, it is difficult to clearly recognize the change in temperature distribution, so the following method highlights the difference between the temperature fluctuation location (ie, the crack occurrence location) and the normal location. It becomes possible to make it.

  In the first method, data within a predetermined time is taken into a personal computer by the measuring device 10 and the autocorrelation is calculated (see FIG. 3).

  In the second method, a part of image data of temperature distribution fluctuation is used as a reference signal by the measuring device 10, temperature distribution data that fluctuates in synchronization with this is taken out, and this is integrated every fluctuation cycle. (See FIG. 4).

  In the third method, the measurement device 10 takes in the vibration data of the measurement object from the sensor, for example, the laser Doppler vibrometer 11 as the information correlated with the temperature fluctuation, and the fluctuation temperature is synchronized with the vibration data. Distribution data is taken out and integrated for each fluctuation cycle (see FIG. 5).

  As another method, in the second or third method, the location and size of the defect are detected by taking a correlation with image data other than the reference signal.

  By these methods, defect detection of large structures can be performed without taking measures such as, for example, building a scaffold around the periphery, using an inspection robot, or using a work vehicle with a lifting device. That is, it is possible to carry out easily and reliably from a distance without accessing the vicinity of the measurement object. For this reason, not only the cost required for the preparation for the inspection can be greatly reduced, but also the inspection time and the operation stop time of the cargo handling machine for inspection can be shortened.

  The above is a case where the present invention is applied to detection of defects in an overhead crane, but is also applicable to detection of defects in large steel structures such as bridges in which load fluctuations always occur due to vehicle travel.

It is a perspective view which shows a test piece. It is a drawing substitute photograph which shows the thermal image after autocorrelation calculation when a variable load is given to the test piece. It is a schematic diagram in the case of performing autocorrelation calculation when performing defect detection of an overhead crane by this method. When detecting overhead crane defects using this method, a part of the image data of temperature distribution fluctuation is used as a reference signal, temperature distribution data that fluctuates in synchronization with this is taken out, and this is taken for each fluctuation cycle. It is a schematic diagram in the case of integrating. When detecting overhead crane defects using this method, the laser Doppler vibrometer captures the vibration data to be measured, extracts the fluctuating temperature distribution data in synchronization with the vibration data, and integrates it for each fluctuation cycle. It is a schematic diagram in the case.

Explanation of symbols

1: Test piece 2: Steel plate 3: Small piece 4: Girder 5: Rail 6: Building 7: Crane truck 8: Luggage 9: Infrared camera 10: Measuring device 11: Laser Doppler vibrometer

Claims (11)

  In a defect detection method for detecting defects in a steel structure from a remote location, the steel structure that has repeatedly undergone stress fluctuations is photographed with an infrared camera, and the temperature distribution fluctuation on the surface of the steel structure is measured as an image. And the defect detection method of the steel structure characterized by detecting the defect which exists in the said steel structure by this.   2. The method for detecting a defect in a steel structure according to claim 1, wherein an autocorrelation of the image data of the temperature distribution fluctuation is obtained to detect a location and a size of the defect.   By using the temperature fluctuation data of a part of the image data of the temperature distribution fluctuation as a reference signal, taking out the temperature distribution data that fluctuates in synchronization with the reference signal, and integrating the temperature distribution data for each fluctuation cycle, The method for detecting a defect in a steel structure according to claim 1, wherein the location and size of the defect are detected.   By sensing information correlated with the stress fluctuation of the steel structure, using this as a reference signal, taking out the fluctuating temperature distribution data in synchronization with the information, and integrating the temperature distribution data for each fluctuation cycle The method for detecting a defect in a steel structure according to claim 1, wherein the location and size of the defect are detected.   The method for detecting a defect in a steel structure according to claim 3 or 4, wherein the location and size of the defect are detected by taking a correlation with image data other than the reference signal.   The method for detecting defects in a steel structure according to any one of claims 1 to 5, wherein the steel structure is a bridge or a cargo handling machine.   Taking an image of a steel structure in which stress fluctuations repeatedly occur, measuring the temperature distribution fluctuation of the surface of the steel structure as an image, and processing the image data of the temperature distribution fluctuation by the infrared camera, A defect detection device for a steel structure, comprising defect detection means for detecting a defect present in the steel structure.   The said defect detection means calculates | requires the autocorrelation of the image data of the said temperature distribution fluctuation | variation, and detects the location and magnitude | size of the said defect, The defect detection apparatus of the steel structure of Claim 7 characterized by the above-mentioned.   The defect detection means uses temperature variation data that is part of the image data of the temperature distribution variation as a reference signal, extracts temperature distribution data that varies in synchronization with the reference signal, and extracts the temperature distribution data for each variation cycle The defect detection device for a steel structure according to claim 7, wherein the defect location and size are detected by adding to the defect.   The defect detection means senses information correlated with stress fluctuation of the steel structure, uses this as a reference signal, takes out the fluctuating temperature distribution data in synchronization with the information, and converts the temperature distribution data into a fluctuating cycle. The defect detection device for a steel structure according to claim 7, wherein the defect location and size are detected by integrating each defect.   The defect of the steel structure according to claim 9 or 10, wherein the defect detection means detects the location and size of the defect by taking a correlation with image data other than the reference signal. Detection device.