Method and system for identifying leakage phenomena using 3D image analysis

Abstract

To provide a method and system for identifying a leakage phenomenon by three-dimensional image analysis, which can identify a leakage phenomenon of a fluid in an existing pipeline by detecting the deformation behavior of a pipe material in the pipeline by an image analysis method without processing the pipeline.SOLUTION: The method for identifying a leakage phenomenon of a fluid in a pipeline is provided with at least an analysis surface installing step for installing an image analysis surface on the surface of a pipe material of the pipeline, an analysis surface photographing step for photographing the image analysis surface by photographing means, a displacement amount measuring step for measuring a circumferential displacement amount of the pipe material based on the image photographed by the photographing means, and a leakage phenomenon determining step for performing an analysis based on the measured circumferential displacement amount and determining at least the presence or absence of the leakage phenomenon of fluid.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present invention relates to a leakage phenomenon identification method and identification system using three-dimensional image analysis, which is capable of identifying a fluid leakage phenomenon in a pipeline. [Background technology]

[0002] In pipeline systems that transmit and distribute water, leaks and water hammer phenomena affect the longevity of the facilities. However, since these phenomena are unavoidable in water utilization, it is essential to establish inspection techniques for pipeline systems from the perspective of maintenance and management.

[0003] Under these circumstances, the technologies disclosed in Non-Patent Documents 1 and 2 are considering establishing a detailed detection method for leaks in irrigation pipelines by adding information about the leak to the water hammer pressure caused by valve operation and using numerical simulation.

[0004] Furthermore, in the technology disclosed in Non-Patent Document 3, a direct measurement method is being considered in which a capsule-type detection device is placed inside a water pipe and the location of a leak in the pipeline is identified from the leak sound acquired by the detection device. [Prior art documents] [Non-patent literature]

[0005] [Non-Patent Document 1] Yohei Asada and four others, "Elucidation of the damping mechanism of pressure waveforms in pipelines," [online], Japan Society of Civil Engineers, [Retrieved October 20, 2022], Internet<https: / / www.jstage.jst.go.jp / article / jscejhe / 74 / 5 / 74_I_769 / _article / -char / ja / > [Non-patent document 2] Yohei Asada and four others, "Applicability of a leak detection method using energy attenuation in pipes," [online], Japan Society of Civil Engineers, [Retrieved October 20, 2022], Internet<https: / / www.jstage.jst.go.jp / article / jscejhe / 75 / 2 / 75_I_799 / _article / -char / ja / > [Non-patent document 3] Isamu Asano and five others, "Development of a capsule-type leak detection device for pipelines," [online], Japan Society of Irrigation, Drainage and Forestry Engineering, [Retrieved October 20, 2022], Internet<https: / / www.jstage.jst.go.jp / article / jjsidre / 86 / 6 / 86_513 / _article / -char / ja / > Summary of the Invention [Problem to be solved by the invention]

[0006] However, the methods disclosed in Non-Patent Documents 1 and 2 require the installation of a water pressure gauge in the pipeline to measure the water pressure, and it is expected that installing a water pressure gauge will be difficult, particularly in existing aging pipelines.

[0007] Furthermore, the method disclosed in Non-Patent Document 3 requires the injection of a φ55 mm × 165 mm capsule with a built-in hydrophone into the pipeline, making it difficult to apply to pipelines with small diameters in particular. In addition, the capsule must be retrieved, which poses challenges such as the need to process the existing pipeline for injection and retrieval, and the risk of being unable to retrieve the capsule in the unlikely event that it becomes impossible to retrieve it.

[0008] Therefore, the present invention aims to provide a method and system for identifying leakage phenomena using three-dimensional image analysis, which can identify fluid leakage phenomena in an existing pipeline by capturing the deformation behavior of the pipe material of the pipeline using an image analysis technique, without performing any processing on the pipeline. [Means for solving the problem]

[0009] The present invention is a method for identifying fluid leakage phenomena in a pipeline, characterized by comprising at least an analysis surface installation step of installing an image analysis surface on the surface of a pipe material of the pipeline, an analysis surface photographing step of photographing the image analysis surface with an imaging means, a displacement amount measurement step of measuring the circumferential displacement of the pipe material based on the image photographed by the imaging means, and a leakage phenomenon determination step of performing analysis based on the measured circumferential displacement amount, thereby making it possible to determine at least the presence or absence of a fluid leakage phenomenon.

[0010] According to the configuration of the present invention, it is possible to capture the deformation behavior of the pipe material of a pipeline using image analysis techniques and identify fluid leakage phenomena in the pipeline, for example, in the limited space inside a manhole, without performing any processing on the existing pipeline. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a flow diagram of the process from experiment to analysis and non-contact detection using a model pipeline according to the present invention. [Figure 2] (a) is a table showing the specifications of the pipe material of the model pipeline, and (b) is a table showing the detailed breakdown of the cases studied in the experiment. [Figure 3] FIG. 2 is a schematic diagram illustrating the configuration of a model pipeline. [Figure 4] FIG. 10 is a schematic diagram illustrating an imaging mode for a pipeline. [Figure 5] FIG. 1 is a schematic diagram illustrating a cylindrical coordinate system model for a pipeline. [Figure 6] (a) is a visible image of image measurement analysis plane A, and (b) is a three-dimensional analysis image of image measurement analysis plane A. [Figure 7] FIG. 1 is a schematic diagram showing measurement conditions in a model pipeline. [Figure 8](a) is a graph showing the water pressure fluctuations in the pipe and the circumferential displacement of the pipe body over time for Case A, and (b) is a graph showing the water pressure fluctuations in the pipe and the circumferential displacement of the pipe body over time for Case B. [Figure 9] (a) shows the water pressure fluctuations in the pipe and the circumference of the pipe in Case C, and (b) shows the water pressure fluctuations in Case D. [Figure 10] (a) is a graph showing the water pressure fluctuations in the pipe and the circumferential displacement of the pipe body over time for Case E, and (b) is a graph showing the water pressure fluctuations in the pipe and the circumferential displacement of the pipe body over time for Case F. [Figure 11] (a) is a graph showing the water hammer pressure in Case C and Case D over time, and (b) is a graph showing the frequency distribution of the water hammer pressure. [Figure 12] (a) is a graph showing the circumferential displacement amount of Case C and Case D over time, and (b) is a graph showing the frequency distribution of the circumferential displacement amount. [Figure 13] (a) is a graph showing the water hammer pressure in Case E and Case F over time, and (b) is a graph showing the frequency distribution of the water hammer pressure. [Figure 14] (a) is a graph showing the circumferential displacement amount of Case E and Case F over time, and (b) is a graph showing the frequency distribution of the circumferential displacement amount. [Figure 15] These are scalograms of the water pressure fluctuations inside the pipe and the circumferential displacement of the pipe body, showing the time-frequency characteristics of water hammer pressure for Case C (a) and Case D (b). [Figure 16] These are scalograms of the water pressure fluctuations inside the pipe and the circumferential displacement of the pipe body, where (a) shows the time-frequency characteristics of the circumferential displacement for Case C and (b) for Case D. [Figure 17] These are scalograms of the water pressure fluctuations inside the pipe and the circumferential displacement of the pipe body, showing the time-frequency characteristics of water hammer pressure for Case E (a) and Case F (b). [Figure 18] These are scalograms of the water pressure fluctuations inside the pipe and the circumferential displacement of the pipe body, where (a) shows the time-frequency characteristics of the circumferential displacement for Case E and (b) shows the time-frequency characteristics of the circumferential displacement for Case F. DETAILED DESCRIPTION OF THE INVENTION

[0012] Hereinafter, with reference to the drawings, an embodiment of the leakage phenomenon identification method and identification system using three-dimensional image analysis of the present invention will be described, taking as an example a method for identifying a leakage phenomenon in a water transmission / distribution pipeline.

[0013] (Model pipeline experiment) Prior to the present invention, a model pipeline was fabricated and experiments were conducted to detect the presence or absence of simulated water leaks in the model pipeline and the behavior of the pipe body that occurs with valve operation in a non-contact manner using digital image correlation (hereinafter referred to as the "DIC method"), which is one of the image analysis techniques.

[0014] That is, as shown in the flow diagram in Figure 1, in this experiment, different cases of water pressure fluctuations were set up depending on whether there was a leak and the valve opening, and the water pressure and image data acquisition were synchronized to measure the water pressure fluctuations and pipe deformation (the deformation behavior of the pipe was evaluated by comparing it with the water pressure fluctuations inside the pipe).

[0015] The specifications of the pipe material of the above model pipeline are as shown in Figure 2(a). The appearance of the model pipeline is as shown in Figure 3. A water tank (not shown) is installed at the most upstream point, and the flow rate is adjusted by operating valve B at the most downstream point. The water tank is fixed to a frame 2.9 m above ground level, and a pressure head of approximately 7.8 m is ensured at the most downstream point.

[0016] The simulated water leak in the model pipeline was reproduced by creating two 5mm diameter water leak holes H on the downstream side, as shown in Figure 3. In addition, to measure the water pressure inside the pipe and analyze the deformation behavior of the pipe body, a water pressure gauge P and an image analysis surface A (described later) were installed at the positions shown in the figure.

[0017] Next, the cases examined in this experiment are shown in Figure 2(b), and six types of cases were set up, taking into consideration the presence or absence of simulated water leakage and the valve opening. The measurement conditions are as follows:

[0018] In Case A, valve B was closed and the pipe was filled with water in a still water state, and the water pressure and deformation behavior of the pipe were measured for 60 seconds. In Case B, simulated water leakage was initiated from two leak holes H 10 seconds after the start of the still water state, and the water pressure and deformation behavior of the pipe body were measured for a total of 60 seconds, continuing for the next 50 seconds. In Case C, valve B was opened (opening angle 16.9°) 10 seconds after the start of the still water state to allow water to flow, and when the water level in the tank dropped by approximately 1 m, valve B was closed to generate water hammer pressure, and the water pressure and deformation behavior of the pipe were measured until the pressure had decayed. In Case D, 10 seconds after the start of the still water state, a simulated water leak was initiated from two leak holes H, and 10 seconds later, valve B was opened (opening angle 16.9°) to allow water to pass through. When the water level in the tank dropped by approximately 1 m, valve B was closed to generate a water hammer pressure, and the water pressure and deformation behavior of the pipe body were measured until the pressure had decayed. In Case E, valve B was opened (opening angle 5.6°) 10 seconds after the start of the still water state to allow water to flow, and when the water level in the tank dropped by approximately 1 m, valve B was closed to generate water hammer pressure, and the water pressure and deformation behavior of the pipe were measured until the pressure had decayed. In Case D, 10 seconds after the start of the still water state, a simulated water leak was initiated from two leak holes H, and 10 seconds later, valve B was opened (opening angle 5.6°) to allow water to pass through. When the water level in the tank had dropped by approximately 1 m, valve B was closed to generate a water hammer pressure, and the water pressure and deformation behavior of the pipe body were measured until the pressure had decayed.

[0019] In the water pressure measurements described above, small pressure sensors (HTV-100KP manufactured by Senses) were installed in two locations: near the water tank at the most upstream position, and near valve B at the most downstream position. Recording was performed using a voltage data logger (WCR-4V manufactured by T&D) (sampling: 100 Hz) and also via synchronous connection in conjunction with image measurement of the deformation behavior of the pipe body (sampling: 20 Hz). In other words, the water pressure data obtained via synchronous connection was used in the analysis described below to enable mutual comparison with the image analysis results of the deformation behavior of the pipe body.

[0020] Furthermore, in order to detect the deformation behavior of the above-mentioned tube body without contact, the deformation behavior of the tube body is image-measured in the manner shown in Figure 4. The DIC method described above is used to identify the deformation behavior of the tube body in three dimensions. This method applies a random pattern to image analysis surface A, which is placed on the surface of the tube material to be measured, and then photographs the movement of dots in the random pattern with a CCD camera, and calculates the amount of displacement from the dynamics of the pixel group in the digital image.

[0021] Therefore, as shown in Figure 4, image analysis surface A with a random pattern was installed at a position corresponding to the top of the pipe, and two CCD cameras 11 and 12 were installed so as to look down on image analysis surface A from directly above. Note that the random pattern in this example was created as a rectangle measuring 55 mm (width) x 80 mm (height). The measurement conditions were a shutter speed of 5 ms, an aperture of 4, and a frame rate of 20 Hz, so that water pressure data could also be captured via synchronous connection.

[0022] In addition, in the DIC method, the brightness of the image analysis plane A has a significant effect on the analysis accuracy, so measurements are taken after sunset when there is less external disturbance, and brightness is adjusted by installing lighting 20 consisting of a stand light and a clip light as shown in the figure.

[0023] The software used for the DIC method was Vic Snap (Correlated Solutions) for measurements and Vic 3D (Correlated Solutions) for analysis. The circumferential displacement (rad) in a cylindrical coordinate system (represented as dTheta in the results of the study described below) was used as an index to evaluate the deformation behavior of the tube using the DIC method.

[0024] Figure 5 shows a cylindrical coordinate system model, and this index represents the amount of displacement in the circumferential direction θ from the random pattern on the initial image analysis plane A. The counterclockwise direction is the positive direction and the clockwise direction is the negative direction relative to the positive direction of the tube axis Z.

[0025] An example of the analysis results of the above experiment is shown in Figure 6. Time series data of the amount of circumferential displacement at the center coordinates of the random pattern on image analysis surface A, which is surrounded by a rectangle, is extracted and analyzed.

[0026] (Analysis method) The analysis method will be explained below according to the flow chart in Figure 1.

[0027] The calculation of the natural frequency based on the specifications, physical properties and installation conditions of the pipe body will be explained. The natural frequency is calculated assuming that the vibration of the pipe is that of a beam. In calculating the natural frequency, as shown in Figure 7, the length of the pipe is set to approximately 7.4 m between the downstream bend and the upstream bend, and the boundary condition is simple support at both ends. The natural angular frequency ω * is expressed by the following Equation 1.

[0028]

number

[0029] where l is the length (m) and E is the elastic modulus (N / m 2 ), I is the second moment of area (m 4 ), ρ is density (kg / m 3 ), A is the cross-sectional area (m 2 ) and from the natural angular frequency above, the natural frequency f * is expressed by the following formula 2.

[0030]

number

[0031] In this analysis, the model pipeline specifications shown in the table in Figure 2(a) were taken into consideration, with l = 7.4 (m) and E = 3.33 × 10 9 (N / m 2 ), I=3.22×10 -6 (m 4 ), ρ=1.43×10 3 (kg / m 3), A=2.23×10 -3 (m 2 ) and the calculation results in the natural frequencies of Mode 1 being 1.67 Hz and Mode 2 being 6.67 Hz. (In the following analysis, the analysis will be carried out using 1.7 Hz and 6.7 Hz, respectively.)

[0032] Discrete Fourier transform will now be explained. To evaluate the frequency characteristics of the water pressure fluctuations inside the pipe and the circumferential displacement of the pipe, a discrete Fourier transform is performed. The discrete Fourier transform of the sampled signal x[n] (n=0, 1, ..., N-1) of length N is shown in Equation 3 below.

[0033]

number

[0034] Here, k is the frequency number associated with discretization, and the k-th frequency fk is expressed by the following Equation 4 using the sampling frequency fs.

[0035]

number

[0036] In this analysis, the sampling rate for the image measurement and its synchronized water pressure measurement was 20 Hz, and 512 data points of water pressure and circumferential displacement, including water hammer pressure due to the blockage of valve B, were targeted.

[0037] The continuous wavelet transform will now be described. A continuous wavelet transform is performed to evaluate the time-frequency characteristics of water pressure fluctuations and circumferential displacement. This wavelet transform can obtain frequency information while preserving time information. The continuous wavelet transform for the time signal f(t) is shown in Equation 5 below (however, it is treated as discrete in the numerical calculations).

[0038]

number

[0039] Here, a is the extension parameter, b is the position parameter, ψ(t) is the mother wavelet, and the asterisk represents the complex conjugate. In this analysis, the complex Morlet wavelet is used as the mother wavelet. The mother wavelet is shown in Equation 6 below.

[0040]

number

[0041] (Results of the study) The time series of water pressure fluctuations inside the pipe and the circumferential displacement of the pipe body will be explained. Figures 8 to 10 show the water pressure fluctuations inside the pipe and the circumferential displacement of the pipe body over time for each of the aforementioned cases. As shown, in the still water state of Figure 8(a), it can be seen that the water pressure and circumferential displacement fluctuate steadily. Additionally, in the leaking state of Figure 8(b), it can be seen that the circumferential displacement fluctuates to a negative value as the water pressure drops when the leak begins. This is because the release of water pressure due to the leak causes the resultant water pressure inside the pipe to be biased to the side opposite the leak hole H, and this follows a negative circumferential direction.

[0042] The time series fluctuation of the circumferential displacement increases when water is flowing (valve opening 16.9°) as shown in Figure 9. Comparing Figure 9(c) and Figure 9(d), it can be seen that the amplitude of the fluctuation of the circumferential displacement is small when water is flowing during leakage.

[0043] When water is flowing as shown in Figure 10 (valve opening of 5.6°), the amplitude of the fluctuation in circumferential displacement is smaller than when water is flowing as shown in Figure 9 (valve opening of 16.9°). When there is no water leakage as shown in Figure 10(e), the circumferential displacement fluctuates in a negative value while water is flowing, and when there is water leakage as shown in Figure 10(f), it can be seen that there is a tendency for the circumferential displacement to fluctuate in a positive value.

[0044] From the above, it can be seen that the actual state of the pipe can be detected non-contactly from the time series of the circumferential displacement of the pipe obtained by image analysis, such as the presence or absence of a leak and differences in the flow conditions inside the pipe depending on the valve opening degree.

[0045] (Frequency characteristics of water hammer pressure and circumferential displacement depending on whether or not there is a water leak) Next, we will evaluate the amount of water leakage and circumferential displacement, focusing on the water hammer pressure, which is the sudden rise in water pressure that occurs when valve B is closed. Here, we will evaluate Cases C to F by extracting 512 data points (sampling: 20 Hz) from 25.6 seconds before 8 seconds before valve closure.

[0046] Figures 11(a) to 14(a) show the circumferential displacement of the pipe body and the water hammer pressure over time. As shown, the maximum water pressure was 134 kPa in Case C, 108 kPa in Case D, 79 kPa in Case E, and 80 kPa in Case F. Comparing the time series of water pressure shown in Figure 11(a) and Figure 13(a) shows that the rise in water pressure was greater when the valve was closed in Cases C and D, where the valve opening was larger at 16.9°.

[0047] Furthermore, when comparing the presence and absence of leakage at the same valve opening, comparing Cases C and D, and Cases E and F, it can be seen that the damping of water pressure fluctuations is faster when there is leakage.

[0048] Comparing the circumferential displacement of the pipe body in Figure 12(a) and Figure 14(a), it can be seen that in both cases the value of the circumferential displacement increases in the positive direction as the water pressure increases due to water hammer pressure.

[0049] Figures 11(b) to 14(b) show the frequency distribution of the circumferential displacement of the pipe body and the water hammer pressure. In the frequency distribution of the water hammer pressure in Figures 11(b) and 13(b), it can be seen that the amplitude spectrum tends to concentrate around 1.7 Hz. It can be seen that in cases with water leakage (Cases D and F), the value of the amplitude spectrum concentrated around 1.7 Hz is smaller than in cases without water leakage (Cases C and E).

[0050] In the frequency distribution of the circumferential displacement shown in Figure 12(b) and Figure 14(b), it can be seen that the amplitude spectrum tends to concentrate at the natural frequencies of 1.7 Hz (mode 1) and 6.7 Hz (mode 2), which were calculated taking into account the installation conditions of the model pipeline. This shows that the natural vibration of the pipe can be detected using the parameters of the circumferential displacement of the pipe obtained by image analysis.

[0051] Based on these results, the frequency band around 1.7 Hz in the water pressure frequency distribution is likely a frequency band originating from water hammer pressure generated by the pipe material and flow conditions under the experimental conditions. Since water hammer pressure is assumed to be compressible and its propagation speed depends on the physical properties and pipe thickness of the pipe material that constrains the water, its frequency characteristics are not uniform and are likely to vary depending on the pipe material and flow conditions. Considering water hammer as a complex phenomenon involving water and pipe material, monitoring the behavior of the pipe material may enable estimation of the characteristics of the flow conditions within the pipe. Specifically, focusing on the natural vibration, which is the vibration characteristic of the pipe (composite of water and pipe material), may enable detection of the frequency characteristics of water pressure fluctuations within the pipe. Therefore, natural vibration can be detected from the frequency distribution of the circumferential displacement of the pipe obtained by image analysis, and thereby the frequency characteristics of water hammer pressure within the pipe can be identified non-contact.

[0052] (Water hammer pressure and circumferential displacement attenuation with and without water leakage in the time-frequency domain) Next, Figures 15 to 18 show scalograms (wavelet transform results) of water pressure fluctuations inside the pipe and the circumferential displacement of the pipe body. That is, Figures 15 and 17 show the time-frequency characteristics of water hammer pressure, and Figures 16 and 18 show the time-frequency characteristics of circumferential displacement. This represents the time series shown in Figures 11(a) to 14(a) in the time-frequency domain using wavelet transform. This makes it possible to obtain frequency information while maintaining the time information.

[0053] As a result of the analysis shown in Figures 15 to 18, the scalograms of all water pressure fluctuations and circumferential displacement show relatively strong signals in the natural vibration frequency bands of 1.7 Hz (mode 1) and 6.7 Hz (mode 2) due to the occurrence of water hammer pressure caused by valve blockage.

[0054] Furthermore, as shown in Figures 15 and 17, the scalograms of water pressure fluctuations detect a decay in signal strength as the water hammer pressure increases over time. A rapid decay in the amplitude of water pressure fluctuations over time can be confirmed when there is a leak (Figures 11(a) and 13(a)), and similarly, in the time-frequency domain, a faster decay in signal strength when there is a leak is detected in the same frequency band as the natural vibration. Focusing on this tendency, the scalograms of circumferential displacement detect a decay in signal strength over time of water hammer pressure in the frequency band of the natural vibration, as shown in Figures 16 and 18. This is particularly noticeable in Cases C and D, where the valve opening is large (Figure 16), and the rapid decay associated with the leak is also clearly detected.

[0055] In Cases E and F, where the valve opening is small, no attenuation of the water hammer pressure was detected at the natural frequency of 6.7 Hz (mode 2) to the same extent as in Cases C and D.

[0056] The experimental results for the model pipeline as described above confirmed that the time-frequency domain of circumferential displacement can detect vibrations caused by water hammer pressure in the natural frequency band, as well as differences in damping due to the presence or absence of water leakage. This demonstrates that the actual state of water leakage in pipelines can be detected and evaluated without contact by using the circumferential displacement obtained from the image analysis results using the DIC method as an index.

[0057] (System Configuration) The configuration of a leakage phenomenon identification system 100 according to an embodiment of the present invention will be described below.

[0058] As shown in Figure 4, the leakage phenomenon identification system 100 in an embodiment of the present invention at least includes an image analysis surface A installed on the surface of a pipeline pipe material, an imaging means (CCD cameras 11, 12) for imaging the image analysis surface A, a displacement amount measurement means 101 for measuring the circumferential displacement of the pipe material based on the image captured by the imaging means, and a leakage phenomenon discrimination means 103 that performs analysis based on the measured circumferential displacement amount and is capable of determining at least the presence or absence of a fluid leakage phenomenon.

[0059] 4, at least two or more CCD cameras 11 and 12 are provided as the imaging means to capture images of the image analysis plane A from different directions, but the number of imaging cameras including the CCD camera may be one or more as long as the imaging means is capable of capturing a three-dimensional image of the image analysis plane A. The imaging means (CCD cameras 11 and 12) are connected to a PC incorporating the leakage phenomenon determination means 103 and the like.

[0060] Furthermore, the leakage phenomenon determination means 103 in the PC includes an analysis means 104 and a learning means 105 that performs machine learning (learning step) on at least the amount of circumferential displacement of the pipe material, and the analysis is performed based on a predetermined algorithm of the learning means 105. Then, based on the analysis results, identification information of the leakage phenomenon is output to the leakage information output unit 102.

[0061] Furthermore, the leakage phenomenon determination means 103 is connected to a measurement facility information input means 106, which makes it possible to input, for example, the diameter of the actually discovered water leak hole H, the distance from the image capture position to the water leak hole H, and the like, in addition to the diameter and material of the pipeline. By using such various information and the circumferential displacement of the pipe material as learning data, it becomes possible to identify not only the presence or absence of a water leak hole H from the acquired 3D image, but also the location and scale of the leak, and furthermore, it becomes possible to improve the accuracy of identifying the water leak phenomenon.

[0062] (Method for identifying water leakage phenomena) Since most existing pipelines are buried underground, an image analysis surface A is set on the surface of the exposed pipe material of the pipeline inside a manhole where a water stop valve, etc. is located (analysis surface setting step). Then, photographing means (CCD cameras 11, 12) are installed, and the image analysis surface A is photographed by the photographing means (analysis surface photographing step).

[0063] The images captured by the imaging means are sent to a PC, which measures the circumferential displacement of the pipe material based on this (displacement measurement step). Analysis is performed based on the measured circumferential displacement of the pipe material, making it possible to determine whether or not a fluid leakage phenomenon has occurred (leakage phenomenon determination step). That is, the deformation behavior of the pipe body is evaluated using the DIC method described above, the circumferential displacement in the cylindrical coordinate system ("θ" in Figure 5) is obtained, and the attenuation behavior in both time and frequency (time-frequency domain) is determined, making it possible to identify leakage phenomena in the pipeline.

[0064] (Other embodiments) The above describes an embodiment of the method and system for identifying leakage phenomena using three-dimensional image analysis according to the present invention, taking as an example a method for identifying leakage phenomena in a water transmission / distribution pipeline. However, the present invention is not necessarily limited to the configuration described above, and various modifications such as those described below are possible.

[0065] For example, the method and system for identifying leakage phenomena using three-dimensional image analysis of the present invention is not necessarily limited to water supply and distribution pipelines, but can be applied to pressurized pipelines that transport various fluids, such as fuel pipes, gas pipes, and steam pipes, and by taking into account the characteristics of the fluid and the characteristics of the pipe body, it is possible to identify fluid leakage phenomena with high accuracy.

[0066] Furthermore, the magnitude of deformation of the pipe material depends on the elastic modulus and second moment of area of ​​the pipe material. Therefore, the identification method and identification system of the present invention can be more suitably applied to PVC pipe material than to cast iron pipe or other steel pipe.

[0067] Although the embodiments of the present invention have been described above with reference to the drawings, the specific configurations are not limited to these embodiments. The scope of the present invention is defined by the claims rather than the description of the above embodiments, and includes all modifications within the meaning and scope of the claims. Furthermore, the specific materials, dimensions, shapes, etc. described in the above examples can be modified within the scope of solving the problems of the present invention. [Explanation of symbols]

[0068] A. Image analysis H Water leak hole P Water pressure gauge B Valve 11 CCD camera 12 CCD cameras 20. Lighting 100 Leakage phenomenon identification system 101 Displacement measurement means 102 Leaked information output means 103 Leak phenomenon determination means 104 Analysis means 105 Learning Tools 106 Measurement facility information input means

Claims

1. 1. A method for identifying a fluid leakage phenomenon in a pipeline, comprising: an analysis surface setting step of setting an image analysis surface on a surface of the pipeline pipe material; an analysis surface photographing step of photographing the image analysis surface with a photographing means; a displacement amount measuring step of measuring a circumferential displacement amount of the pipe material based on the image captured by the imaging means; a water pressure measuring step of measuring water pressure in the pipeline; a damping behavior determination step of determining the damping behavior of the circumferential displacement amount in the time-frequency domain based on the frequency of the water hammer pressure measured in the water pressure measurement step and the circumferential displacement amount measured in the displacement amount measurement step; and a leakage phenomenon determination step capable of determining whether or not at least a leakage phenomenon of the fluid has occurred based on the determination result in the damping behavior determination step. A method for identifying a leakage phenomenon by three-dimensional image analysis.

2. The photographing means is at least two photographing cameras that photograph the image analysis surface from different directions. The method for identifying a leakage phenomenon by three-dimensional image analysis according to claim 1.

3. a learning step of machine learning at least the circumferential displacement amount, In the leakage phenomenon determination step, the damping behavior determination step is performed based on the predetermined algorithm by the machine learning.

3. The method for identifying a leakage phenomenon by three-dimensional image analysis according to claim 1 or 2.

4. 1. A system for identifying a fluid leakage phenomenon in a pipeline, comprising: an image analysis surface installed on a surface of the pipeline pipe; an imaging means for imaging the image analysis surface; a displacement amount measuring means for measuring a circumferential displacement amount of the pipe material based on the image captured by the imaging means; a pressure sensor for measuring the water pressure in the pipeline; An analysis means for determining the attenuation behavior of the circumferential displacement in the time-frequency domain based on the frequency of the water hammer pressure measured by the pressure sensor and the circumferential displacement measured by the displacement measurement means; and a leakage phenomenon determination means capable of determining whether or not there is a leakage phenomenon of the fluid based on the analysis result of the analysis means. A leakage phenomenon identification system using three-dimensional image analysis.

5. The photographing means is at least two photographing cameras that photograph the image analysis surface from different directions.

5. The system for identifying a leakage phenomenon by three-dimensional image analysis according to claim 4.

6. a learning means for machine learning at least the circumferential displacement amount, The leakage phenomenon determination means determines the attenuation behavior of the circumferential displacement amount by the analysis means based on a predetermined algorithm of the learning means.

6. The system for identifying a leakage phenomenon by three-dimensional image analysis according to claim 4 or 5.

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