WO2018207528A1

WO2018207528A1 - Structure abnormality diagnosis device

Info

Publication number: WO2018207528A1
Application number: PCT/JP2018/014956
Authority: WO
Inventors: 隆史森本; 基広浅野; 都築　斉一; 裕晶鈴木; 輝夫日置; 任晃堀田; 明史土岐
Original assignee: コニカミノルタ株式会社; 千代田化工建設株式会社
Priority date: 2017-05-10
Filing date: 2018-04-09
Publication date: 2018-11-15
Also published as: JP7048590B2; JPWO2018207528A1

Abstract

This structure abnormality diagnosis device comprises a vibration information extraction unit, an information storage unit, and an abnormal condition diagnosis unit. The vibration information extraction unit extracts vibration information for a structure reflected in a captured image of the structure. The information storage unit acquires the vibration information over a plurality of time periods, and stores said information. The abnormal condition diagnosis unit carries out an abnormal condition diagnosis of the structure using the vibration information stored by the information storage unit, and outputs the diagnosis results thereof.

Description

Structure abnormality diagnosis device

The present invention relates to a structure abnormality diagnosis apparatus, for example, a structure abnormality diagnosis apparatus that diagnoses the presence or absence of a structure abnormality from the vibration of the structure on a captured image.

In industrial equipment manufacturing facilities, factories, buildings, etc., in order to prevent accidents by detecting the corrosion / deterioration of the structures that make them up, aging of machinery, etc., the vibrations of the structures are observed. Detecting an abnormal condition is performed. A vibration sensor is generally used to detect this abnormal state. As a method of the vibration sensor, there are a capacitance method, a piezoelectric element method, a laser Doppler method, and the like, and it is necessary to install a vibration sensor at a point where observation is desired. For this reason, especially when many observation points are set up for the purpose of observing the deterioration of structures, the installation cost and maintenance management cost of the vibration sensor are incurred, and the line connection between the vibration sensor and the control monitoring device There is also the complexity.

As a method of determining whether or not a structure is in an abnormal state from vibration information, for example, a method of checking a change in amplitude of a specific frequency component is also known. For example, in the method described in Patent Document 1, whether or not a signal peak value is generated at a specific frequency by imaging an object to be monitored with an infrared camera, Fourier-transforming time-series data indicating a temporal change in temperature, and the like. By determining the above, an abnormality detection is performed.

Japanese Patent Laid-Open No. 2015-219092

However, in the method described in Patent Document 1, it is necessary to grasp in advance what kind of state will occur when a signal abnormality occurs. For this reason, it is impossible to cope with a vibration state that has not been experienced before or cannot be predicted.

The present invention has been made in view of such a situation, and the object of the present invention is to determine whether a structure is abnormal from vibration information of a captured image without grasping in advance the vibration state when the abnormality occurs in the structure. It is an object of the present invention to provide a structure abnormality diagnosis device capable of diagnosing whether or not a certain state exists.

In order to achieve the above object, a structure abnormality diagnosis device of the present invention includes a vibration information extraction unit that extracts vibration information of a structure reflected in an image from a captured image obtained by capturing the structure,
An information storage unit for acquiring and storing the vibration information for a plurality of times;
An abnormal state diagnosis unit that performs an abnormal state diagnosis of the structure using vibration information stored by the information storage unit, and outputs the diagnosis result;
It is characterized by having.

According to the present invention, the structure is configured to diagnose the abnormal state of the structure using the accumulated vibration information and output the diagnosis result, so the vibration state when the abnormality occurs in the structure is grasped in advance. Without doing so, it is possible to diagnose whether or not the structure is in an abnormal state from the vibration information of the captured image. Therefore, it is possible to detect an abnormal state that cannot be predicted by previous experience.

The block diagram which shows 1st Embodiment of a structure abnormality diagnostic apparatus. The block diagram which shows 2nd Embodiment of a structure abnormality diagnostic apparatus. The flowchart which shows the example of the 1st process process by 1st Embodiment. The flowchart which shows the 2nd process process example by 1st Embodiment. The flowchart which shows the 1st example of a process process by 2nd Embodiment. The flowchart which shows the 2nd process process example by 2nd Embodiment. The infrared image figure which shows the outdoor test place where the gas leak and the temperature change of the background generate | occur | produced in time series. The graph which shows the temperature change of two points SP1 and SP2 in an outdoor test site. The flowchart which shows an example of a gas area | region extraction process (# 10 of FIG. 5, FIG. 6). The graph which shows the data to which the time averaging process (# 11, # 14 of FIG. 9) was performed. 10 is a graph showing data on which difference processing (# 12, # 15 in FIG. 9) has been performed between averaged data and original data. The graph which shows the data by which the standard deviation process (# 13, # 16 of FIG. 9) was performed with respect to two types of difference data by 21 frames before and behind. The graph which shows the data in which the difference process (# 17 of FIG. 9) of 2 types of standard deviation data was performed. The standard deviation image figure which shows the display image which multiplied the standard deviation (# 13 of FIG. 9) of the difference with 21 frame average 5000 times. The standard deviation image figure which shows the display image which multiplied the standard deviation (# 16 of FIG. 9) of the difference with 3 frame average 5000 times. FIG. 10 is a standard deviation difference image diagram showing a display image obtained by multiplying a standard deviation difference (# 17 in FIG. 9) by 5000 times. The infrared image figure which shows an example of a structure. The graph which shows the temperature change of the edge part of a structure, and a non-edge part. The schematic diagram for demonstrating the principle of the temperature change in the edge part of a structure. The figure which shows the data by which the difference process (# 17 of FIG. 9) of the standard deviation data was performed about the edge part and non-edge part of a structure with a graph. The schematic diagram which shows the specific example of the recording method of a vibration detection area | region.

Hereinafter, a structural abnormality diagnosis device and the like embodying the present invention will be described with reference to the drawings. In addition, the same code | symbol is mutually attached | subjected to the part which is the same in each embodiment etc., and the corresponding part, and duplication description is abbreviate | omitted suitably.

FIG. 1 and FIG. 2 show schematic configurations of the structure

abnormality diagnosis apparatuses

10A and 10B according to the first and second embodiments. The structure abnormality diagnosis device 10A includes a vibration information extraction unit 1, an information storage unit 2, and an abnormal state diagnosis unit 3 as functional blocks. The structure abnormality diagnosis device 10B includes a vibration information extraction unit 1, an information storage unit. 2, the abnormal state diagnosis unit 3 and the gas distribution extraction unit 4 are provided as functional blocks. The vibration information extraction unit 1 receives a captured image obtained by capturing a structure, and extracts vibration information of the structure reflected in the image from the captured image. The information storage unit 2 acquires and stores vibration information for a plurality of times. The abnormal state diagnosis unit 3 performs an abnormal state diagnosis of the structure using the vibration information stored by the information storage unit 2, and outputs the diagnosis result. The gas distribution extraction unit 4 extracts gas distribution information existing in the space being imaged from the captured image. 1 and 2 indicate the flow of data between functional blocks.

An input signal (captured image information) to the structure abnormality diagnosis device 10A is input to the vibration information extraction unit 1 as shown in FIG. The output result of the vibration information extraction unit 1 is input to the information storage unit 2 and also input to the abnormal state diagnosis unit 3. Data is input from the information storage unit 2 to the abnormal state diagnosis unit 3 as necessary. An output signal (abnormal state diagnosis result) from the abnormal state diagnosis unit 3 is output to the outside as an output from the structure abnormality diagnosis device 10A and also used for input to the information storage unit 2.

An input signal (captured image information) to the structure abnormality diagnosis device 10B is input to the gas distribution extraction unit 4 and the vibration information extraction unit 1 as shown in FIG. Both the output results of the gas distribution extraction unit 4 and the vibration information extraction unit 1 are input to the information storage unit 2. The output result of the vibration information extraction unit 1 is also input to the abnormal state diagnosis unit 3, and data is input from the information storage unit 2 to the abnormal state diagnosis unit 3 as necessary. An output signal (abnormal state diagnosis result) from the abnormal state diagnosis unit 3 is output to the outside as an output from the structure abnormality diagnosis device 10B, and is also used for input to the information storage unit 2. That is, the information storage unit 2 stores information that associates the gas spatial distribution information extracted by the gas distribution extraction unit 4 with the structure abnormality state diagnosis result.

The gas distribution information can be effectively used by outputting the result extracted by the gas distribution extraction unit 4 to the outside of the structure abnormality diagnosis device 10B. For example, as shown in FIG. 2, it is preferable to use the gas leak alarm device 5 as an output device that performs gas leak notification, gas leak location display, and the like when a gas leak occurs. Examples of the gas leakage alarm device 5 include a control terminal device in a central monitoring room, a personal computer (stationary type, portable type, etc.), and a portable terminal (smart phone, touch pad, etc.).

The structure

abnormality diagnosis apparatuses

10A and 10B are a CPU (Central Processing Unit), a RAM (Random Access Memory), a ROM (Read Only Memory), an HDD in a digital device such as a personal computer or a portable device (smartphone, tablet terminal, etc.). (Hard Disk Drive) or the like, and the captured image information is input and the abnormal state diagnosis result is output. The CPU reads out a structural abnormality diagnosis processing program stored in the HDD, develops it in the RAM, and executes it, thereby realizing the functional block.

Here, the gas distribution information extraction processing by the gas distribution extraction unit 4 will be described. First, as an example of an input image to the structure

abnormality diagnosis apparatuses

10A and 10B, FIG. 7 shows a time series infrared image of an outdoor test site in which gas leakage and background temperature change occur. These are the infrared images G1 to G4 obtained by capturing a moving image with an infrared camera, and are actual captured data when the temperature suddenly decreases as a whole because it is shaded by clouds. The input image is an image capturing black body radiation generated by an object having an absolute temperature of 0 K or higher, and the signal intensity of the image represents the temperature of the subject. The wavelength region of the image is an infrared region, and in this image, the gas distribution is recorded as the strength of the signal based on the light absorption of the gas. In the outdoor test site, a point SP1 at which gas can be ejected is set, and for comparison with the point SP1, a point SP2 at which no gas is ejected is also set.

The infrared image G1 is an infrared image of the outdoor test site taken at time T1 immediately before the sunlight is blocked by the clouds. The infrared image G2 is an infrared image (with gas) of an outdoor test site taken at time T2 after 5 seconds from time T1. At time T2, sunlight is blocked by clouds, so the background temperature is lower than at time T1. Image G3 is an infrared image (with gas) of the outdoor test site taken at time T3, 10 seconds after time T1. From time T2 to time T3, the state in which the sunlight is blocked by the cloud is continued, so that the temperature of the background is lower at time T3 than at time T2. Image G4 is an infrared image (with gas) of the outdoor test site taken at time T4 15 seconds after time T1. From time T3 to time T4, the state in which sunlight is blocked by the cloud is continued, so that the background temperature is lower at time T4 than at time T3.

8A and 8B show temperature changes at two points SP1 and SP2 of the outdoor test site. In each graph, the vertical axis indicates the temperature, and the horizontal axis indicates the number of frames. The frame rate is 30 fps. Therefore, the time from the 45th frame to the 495th frame is 15 seconds.

As shown in FIG. 8A, gas ejection is started at a point SP1 at a time between time T1 and time T2. Therefore, for example, as can be seen from the range indicated by the broken line region, the graph (A) indicating the temperature change at the point SP1 is different from the graph (B) indicating the temperature change at the point SP2. Since no gas is ejected at the point SP2, the temperature change at the point SP2 shows only the background temperature change. On the other hand, since the gas is ejected at the point SP1, the gas is drifting at the point SP1. For this reason, the temperature change at the point SP1 indicates the temperature change obtained by adding the background temperature change and the temperature change caused by the leaked gas.

As shown in FIG. 8 (B), the background temperature has dropped by about 4 ° C. in 15 seconds from time T1 to time T4. For this reason, as shown in FIG. 7, it can be seen that the image G4 is generally darker (that is, darker) than the image G1, and the background temperature is lowered. However, it can be seen that the temperature change due to the gas ejected at the point SP1 is slight and not 0.5 ° C. For this reason, at time T2, time T3, and time T4, the gas is ejected at the point SP1, but since the background temperature change is much larger than the temperature change caused by the ejected gas, the image G2, the image G3 , It cannot be seen from the image G4 that gas is emitted from the point SP1.

From the graphs shown in FIGS. 8A and 8B, it can be seen that gas is ejected at the point SP1 (that is, gas leakage occurs at the point SP1). However, as described above, it is not known from the infrared image shown in FIG. 7 that gas is ejected at the point SP1 (that is, it is not known that a gas leak occurs at the point SP1). Therefore, image processing is performed on the infrared image in consideration of the temperature change of the background so that the state of gas leakage can be shown by the image. That is, a slight temperature change component (frequency component) due to the presence of gas is extracted by the method described below.

FIG. 9 shows an example of a processing flow of gas region extraction (# 10 in FIGS. 5 and 6). As the time averaging process for low frequency extraction (# 11 in FIG. 9) for data generation corresponding to the background temperature change, averaging is performed at the previous and next 21 frames, and the time averaging process for high frequency extraction is performed at the previous and next 3 frames. Averaging is performed (# 14 in FIG. 9). The graph of FIG. 10 shows data that has been subjected to time averaging processing (# 11, # 14 of FIG. 9). In FIG. 10, the original data (FIG. 8A) is shown by a one-dot chain line, the data (Ave3) subjected to time averaging processing (# 14 in FIG. 9) in three frames before and after is shown by a solid line, and time is shown in 21 frames before and after. Data (Ave21) subjected to the averaging process (# 11 in FIG. 9) is indicated by a broken line.

Furthermore, if differences from the original data are taken (# 12 and # 15 in FIG. 9), waveform data as shown in the graphs of FIGS. 11 (A) and 11 (B) is obtained. The graph of FIG. 11 (A) shows the difference processing between the data (Ave21) and the original data (FIG. 8 (A)) that have been subjected to the time averaging processing for low frequency extraction (# 11 in FIG. 9) in the previous and next 21 frames (FIG. 9 is the data subjected to # 12) (original-Ave21), and the graph of FIG. 11B shows the data subjected to high-frequency extraction time averaging processing (# 14 of FIG. 9) in three frames before and after. This shows data (original-Ave3) on which difference processing (# 15 in FIG. 9) has been performed between (Ave3) and original data (FIG. 8A).

As can be seen from FIG. 11 (A), gas is emitted from around the 90th frame. However, since the waveform data in FIG. 11 (B) is a waveform that takes out only a very high frequency component, it does not include information on the frequency corresponding to the fluctuation component of the fluctuation of the gas, and changes so much before and after the gas ejection. You can see that there is nothing. That is, in the difference between the high-frequency extraction time averaging process data and the original data, only high frequencies such as sensor noise are extracted, and no gas is extracted. In other words, the waveform data in FIG. 11A is a waveform in which high-frequency noise components such as gas and sensor noise are added, and the waveform data in FIG. 11B is a waveform of only high-frequency noise such as sensor noise. However, since the noise components of both waveforms do not have a complete correlation, the noise component cannot be removed even if the difference is obtained with the waveforms of FIGS. 11A and 11B (noise waveform). Subtraction by level works in much the same way as adding, leaving a noise waveform.)

Therefore, for the two types of waveforms (original-Ave21 and original-Ave3) shown in FIGS. 11A and 11B, standard deviations are calculated in 21 frames before and after (# 13 and # 16 in FIG. 9). In FIG. 12, the solid line indicates the standard deviation stdev21 of the original −Ave21, and the broken line indicates the standard deviation stdev3 of the original −Ave3. Further, a difference value Stdev21-3 between the standard deviation stdev21 and the standard deviation stdev3 is calculated (# 17 in FIG. 9). FIG. 13 shows the standard deviation difference value Stdev21-3. From FIG. 13, it can be seen that the value can be corrected to almost 0 until the 90th frame where no gas is emitted. In other words, by taking a standard deviation, a process having an effect of taking an absolute value in the middle is sandwiched so that a high frequency noise component can be actually subtracted. An example in which this processing is performed on the entire image is shown in FIGS.

FIG. 14 is a standard deviation image diagram showing a display image obtained by multiplying the standard deviation stdev21 (# 13 in FIG. 9) of the difference between the 21-frame average Ave21 and the original data by 5000. FIG. 15 shows the 3-frame average Ave3 and the original data. FIG. 10 is a standard deviation image diagram showing a display image obtained by multiplying the standard deviation stdev3 (# 16 of FIG. 9) by 5,000 times. FIG. 16 is a standard deviation difference image diagram showing a display image obtained by multiplying the standard deviation difference Stdev21-3 (# 17 in FIG. 9) by 5000 times. By performing leakage source specifying processing by image processing on the image shown in FIG. 16, the leakage location of the leakage gas PG can be obtained.

Next, vibration information extraction processing by the vibration information extraction unit 1 will be described. FIG. 17 shows an infrared image obtained by photographing an example of the structure ST. FIG. 17B is an enlarged view of the partial area PS existing in the imaging screen Io shown in FIG. In FIG. 17B, E1 indicates an edge portion that is an area including an edge, and E0 indicates a non-edge portion that is an area that does not include an edge.

FIG. 18 shows temperature changes in the edge portion and the non-edge portion. Specifically, in FIG. 18, a thick line indicates a temperature change of one pixel in the non-edge portion E0, and a thin line indicates a temperature change of one pixel corresponding to the edge in the edge portion E1. As can be seen from the graph of FIG. 18, a large temperature change occurs at the edge even when the temperature change is small at the non-edge portion. The principle of this temperature change will be described with reference to FIG. FIG. 19 shows the temperature values of three pixels near the top and bottom of the edge portion E1 in the frame F1 and the frame F2 after 1/30 second. In the frame F1 and the frame F2, the position on the structure corresponding to each pixel is shifted by 0.1 pixel downward due to the vibration of the structure. For this reason, the temperature difference between the center pixel and the bottom pixel in the frame F1 was 2 ° C., but in the frame F2 after 1/30 seconds, the temperature between the center pixel and the bottom pixel in the frame F1 It changes by 0.2 ° C. corresponding to 0.1 pixel of the difference of 2 ° C., and it appears that there is a temperature change. Therefore, when the same process as the gas detection is performed, the difference value of the standard deviation shows a large value in the pixel corresponding to the edge, as shown in FIG. On the other hand, as described above, since the temperature change caused by the vibration of the structure is small at the non-edge portion, a large temperature change is detected at the edge portion. You can see that it is vibrating. Thus, the vibration information can be acquired as a change in the value of the pixel corresponding to the edge of the structure ST reflected in the captured image.

FIG. 20A corresponds to the graph of FIG. 13, and standard deviation data difference processing (# 17 in FIG. 9) is performed on the pixels of the edge portion E1 and the non-edge portion E0 of the structure ST. Data Stdev21-3 is shown. FIG. 20B is an enlarged view of the region PS including the edge portion E1 and the non-edge portion E0, and FIG. 20C is 8.33 seconds of the difference value Stdev21-3 of the standard deviation of all the pixels in the region PS. 20D is an image obtained by binarizing the image of FIG. 20C.

As an evaluation value indicating the vibration amount, for example, an average value of standard deviation difference value Stdev21-3 for a predetermined time is used. FIG. 20C shows an example in which this average value is imaged. Furthermore, as shown in FIG. 20D, the value may be binarized with an appropriate threshold value, and the number of pixels exceeding the threshold value may be counted to obtain an evaluation value of the vibration amount. The greater the vibration, the greater the number of affected pixels. Therefore, vibration information may be acquired as the number of pixels indicating a change in pixel value (temperature) of the structure reflected in the captured image. Further, instead of the difference value of the standard deviation, the “standard deviation of the difference from the averaging in 21 frames before and after” (a large value in FIG. 12) before the process of subtracting the high frequency component may be used.

Next, an example of abnormality diagnosis processing by the structure abnormality diagnosis apparatus according to the present invention will be described. The flowchart of FIG. 3 shows a first processing step example by the structure abnormality diagnosis apparatus 10A (FIG. 1). An input image to the structure abnormality diagnosis device 10 </ b> A is input to the vibration information extraction unit 1. In the vibration information extraction unit 1, an edge existing in the input image is extracted by differentiating the input image (# 30). The vibration information is digitized for each extracted edge (# 32), and the location of the edge and the digitized vibration information are accumulated and recorded in the information storage unit 2 (# 34). In this case, the vibration information to be recorded is preferably an average value of the standard deviation difference value Stdev21-3 for a predetermined time. Then, for each extracted edge, the vibration information is compared with the vibration information at the same location in the accumulated information, and if there is a difference, it is determined that there is abnormal vibration, and abnormality detection processing is performed (# 36). The vibration information of the place determined as having abnormal vibration is recorded (# 38), and the abnormality diagnosis result is output (# 40). Note that the vibration information may be digitized or recorded for all pixels of the input image.

FIG. 4 is a flowchart showing a second processing step example by the structure abnormality diagnosis device 10A (FIG. 1). In this second processing step example, the operator confirms the photographed image on the monitor screen and designates a region for monitoring vibration. An input image to the structure abnormality diagnosis device 10 </ b> A is input to the vibration information extraction unit 1. The vibration information extraction unit 1 digitizes the vibration information for each designated area (# 42), and accumulates and records the location of the area and the digitized vibration information in the information storage unit 2 (# 44). In this case, the vibration information to be recorded may be the average value of the standard deviation difference value Stdev21-3 of each pixel in the region for a predetermined time, but the average value of the standard deviation difference value Stdev21-3 is a predetermined value. It may be the number of pixels in the area exceeding the threshold value. Then, the vibration information and the vibration information of the same area in the accumulated information are compared for each designated area, and if there is a difference, it is determined that there is abnormal vibration, and abnormality detection processing is performed (# 46). The area determined to have abnormal vibration is recorded (# 48), and the abnormality diagnosis result is output (# 50).

FIG. 5 is a flowchart showing a first processing process example by the structure abnormality diagnosis device 10B (FIG. 2). An input image to the structure abnormality diagnosis device 10 </ b> B is input to the gas distribution extraction unit 4 and the vibration information extraction unit 1. In the gas distribution extraction unit 4, gas distribution information is extracted (# 10). If the presence of the gas is confirmed, a gas leakage source specifying process is performed using image processing, and the calculated gas leakage location is recorded (# 20). The extracted result is effectively used by being output to the outside (# 19). For example, if the output destination is the gas leak alarm device 5, an alarm is issued based on the gas distribution information.

On the other hand, the vibration information extraction unit 1 performs the abnormality detection process by the same process as steps # 30 to # 36 in FIG. And a gas leak location and the vibration information of the area | region determined to have abnormal vibration are matched and recorded (# 52), and an abnormal diagnosis result is output (# 54).

The flowchart of FIG. 6 shows a second processing step example by the structure abnormality diagnosis device 10B (FIG. 2). An input image to the structure abnormality diagnosis device 10 </ b> B is input to the gas distribution extraction unit 4 and the vibration information extraction unit 1. In the gas distribution extraction unit 4, the same processing as steps # 10 to # 20 in FIG. 5 is performed, and the gas leakage location is recorded.

On the other hand, the vibration information extraction unit 1 performs an abnormality detection process by the same process as steps # 42 to # 46 in FIG. Then, the gas leakage location and the area determined to have abnormal vibration are recorded in association with each other (# 56), and the abnormality diagnosis result is output (# 58).

As a method for recording vibration information (# 34 in FIG. 3 and the like), there is a method of recording the vibration amount for each pixel. However, if vibration is detected in only a part of the area on the screen, the area is displayed. It is useful to record the location and the vibration information as the area when an abnormal vibration analysis is performed later. FIG. 21 shows a specific example of a method for recording an area where vibration is detected (hereinafter referred to as vibration detection area SA). FIG. 21A shows the vibration detection area SA in the partial area PS of the photographed image, and FIGS. 21B to 21D show a method of recording the vibration detection area SA.

In the recording method shown in FIG. 21 (B), the vibration detection area SA is recorded as a coordinate point SB composed of a coordinate set of a plurality of points surrounding the vibration detection area SA where the vibration is detected. In the recording method shown in FIG. 21C, the vibration detection area SA is recorded as a circumscribed circle SC that is centered on the center of gravity of the vibration detection area SA where the vibration is detected and circumscribes the vibration detection area SA. In the recording method shown in FIG. 21D, the vibration detection area SA is recorded as the coordinates and inclination (angle θ) of the circumscribed rectangle SD that circumscribes the vibration detection area SA where the vibration is detected. As vibration information of the vibration detection area SA, the average value of the standard deviation difference value Stdev21-3 of each pixel in the vibration detection area SA is averaged over the entire area of the vibration detection area SA, or the difference of the standard deviation. The number of pixels in the vibration detection area SA in which the average value of the values Stdev21-3 for a predetermined time exceeds the threshold is preferable.

When comparing vibration information and accumulated information, information in the same area is compared (# 60 in FIGS. 3 to 6). At that time, all accumulated data in the same area is extracted and compared. And a case where only data at substantially the same time are extracted for comparison. The former is effective when it is expected that the vibration method does not have time dependency. The latter is when the vibration method is time-dependent (for example, when periodic mechanical vibration is applied from another location), or when it is desired to eliminate the effect, or conversely, periodic mechanical vibration is applied. This is effective when it is desired to detect abnormal vibrations in an abnormal state.

As an output example of abnormality detection processing,
・ Record data indicating that an abnormality has been detected in addition to the stored information,
-Displaying on the monitoring device that an abnormality has been detected,
・ Displays that an abnormality has been detected on the monitoring device and issues an alarm,
Etc. In the above description, the case where the vibration information is information related to the amplitude of vibration has been described. However, the frequency of vibration may be used as the vibration information.

As can be seen from the above description, the above-described embodiments include the following characteristic configurations (C1) to (C7) and the like.

(C1): a vibration information extraction unit that extracts vibration information of a structure reflected in the image from a captured image obtained by imaging the structure;
An information storage unit for acquiring and storing the vibration information for a plurality of times;
An abnormal state diagnosis unit that performs an abnormal state diagnosis of the structure using vibration information stored by the information storage unit, and outputs the diagnosis result;
A structure abnormality diagnosis device comprising:

(C2): In the configuration (C1), the apparatus further includes a gas distribution extraction unit that extracts distribution information of gas existing in the space being imaged from the captured image, and the information storage unit includes the gas distribution extraction. The structure which further accumulate | stores the information which matched the spatial distribution information of the gas extracted by the part, and the said structure abnormal state diagnostic result.

(C3): Extracting vibration information of the structure reflected in the image from the captured image of the structure, acquiring and storing the vibration information for a plurality of times, and using the stored vibration information A structure abnormality detection method characterized by performing an abnormal state diagnosis of a structure and outputting the diagnosis result.

(C4): processing for extracting vibration information of a structure reflected in an image from a captured image obtained by capturing the structure, processing for acquiring and storing the vibration information for a plurality of times, and stored vibration information A structure abnormality diagnosis program for causing a computer to execute an abnormality state diagnosis of the structure using a computer and outputting a diagnosis result.

(C5): A configuration in which the vibration information in the above configurations (C1) to (C4) is acquired as a change in the pixel value of the edge portion of the structure reflected in the captured image.

(C6): A configuration in which the vibration information in the above configurations (C1) to (C4) is acquired as the number of pixels indicating a change in the pixel value of the structure reflected in the captured image.

(C7): In the above configurations (C1) to (C6), the vibration information at the same time is extracted from the vibration information stored by the information storage unit, and the extracted vibration information is used to extract the structure information. A configuration that diagnoses abnormal conditions and outputs the diagnosis results.

As can be seen from the above-described embodiments, according to the embodiment of the structure abnormality diagnosis device, the abnormality state diagnosis of the structure ST is performed using the accumulated information and the extracted vibration information. Since the diagnosis result is output, it is determined whether the structure ST is in an abnormal state from the vibration information of the captured image without grasping in advance the vibration state when the abnormality occurs in the structure ST. Diagnosis is possible. Therefore, it is possible to detect an abnormal state that cannot be predicted by previous experience. This is the same even when the structure abnormality detection method and the structure abnormality diagnosis program are used.

Then, by using the captured image in which the spatial distribution of the leakage gas PG is reflected as the strength of the signal, the spatial distribution of the leakage gas PG and the vibration information of the structure ST are extracted, thereby simultaneously detecting the gas leakage and the abnormal vibration. In addition, since it is not necessary to provide a large number of vibration sensors, installation costs, maintenance management costs, and the like can be reduced, and the complexity of line connection is eliminated. Furthermore, by recording the gas leakage detection result and the detection result of the abnormal vibration state in association with each other, it can be used as predictive maintenance data.

DESCRIPTION OF SYMBOLS 1 Vibration information extraction part 2 Information storage part 3 Abnormal state diagnostic part 4 Gas distribution extraction part 5 Gas

leak alarm apparatus

10A, 10B Structure abnormality diagnostic apparatus G1-G4 Infrared image (captured image)
Io Imaging Screen PG Leakage Gas SA Vibration Detection Area SB Coordinate Point SC circumscribed circle SD circumscribed rectangle E0 non-edge part E1 edge part ST structure

Claims

A vibration information extraction unit that extracts vibration information of the structure reflected in the image from a captured image obtained by imaging the structure;
An information storage unit for acquiring and storing the vibration information for a plurality of times;
An abnormal state diagnosis unit that performs an abnormal state diagnosis of the structure using vibration information stored by the information storage unit, and outputs the diagnosis result;
A structure abnormality diagnosis apparatus having
A gas distribution extraction unit that extracts distribution information of gas existing in the space being imaged from the captured image, and the information storage unit includes the spatial distribution information of the gas extracted by the gas distribution extraction unit; The structure abnormality diagnosis device according to claim 1, further storing information associated with the structure abnormality state diagnosis result.
3. The structure abnormality diagnosis device according to claim 1 or 2, wherein the vibration information is acquired as a change in a pixel value of an edge portion of the structure reflected in the captured image.
The structure abnormality diagnosis device according to claim 1 or 2, wherein the vibration information is acquired as a pixel number indicating a change in a pixel value of a structure reflected in the captured image.
2. The vibration information at the same time is extracted from the vibration information stored by the information storage unit, the abnormal state diagnosis of the structure is performed using the extracted vibration information, and the diagnosis result is output. 5. The structure abnormality diagnosis device according to any one of 1 to 4.