Nuclear power plant leakage prediction system based on acoustic emission test

Abstract

The present invention relates to a nuclear power plant leakage prediction system based on an acoustic emission (AE) test, comprising: a plurality of AE sensors that receive acoustic emission (AE) signals generated from a plurality of cracks; and a plurality of acoustic leak monitoring systems (ALMS) that determine whether a leak has occurred, on the basis of the AE signals received from the plurality of AE sensors, wherein locations at which the cracks have occurred are calculated through time difference analysis for the AE signals, the AE signals are classified according to a source generating the cracks, each of the plurality of cracks is evaluated by using the classified AE signals, and prediction is made on whether a leak has occurred, on the basis of the location where the cracks have occurred and a result of the evaluation of each of the plurality of cracks.

Description

Acoustic Emission Test-Based Nuclear Power Plant Leak Prediction System

[0001] The present invention relates to a system and method for providing sufficient time for maintenance of a nuclear power plant by detecting cracks in the nuclear power plant and predicting leakage of the nuclear power plant.

[0002]

[0003] A significant portion of nuclear power plants currently in operation worldwide have been in operation for over 35 years, causing equipment and components to deteriorate over time. Nuclear power plants are typically designed to withstand all potential accidents during operation, ensuring that all equipment and components function properly and maintain sufficient integrity even under the most severe of these events. One such accident previously assumed under these design standards was a double-ended guillotine break.

[0004] Figure 1 is a conceptual diagram illustrating a double-end instantaneous fracture. Referring to Figure 1, a double-end instantaneous fracture refers to a phenomenon in which a pipe splits in two due to a brittle fracture during operation, which can have serious consequences. Since the 1980s, the concept of Leak Before Break (LBB) has been applied to nuclear power plants based on the results of probabilistic fracture mechanics research. This method is a method to design pipes so that double-end instantaneous fracture does not occur immediately even if a penetrating crack (leakage) exists. At the same time, it detects and maintains cracks through the structural integrity monitoring system (NIMS: NSSS Integrity Monitoring System) to maintain the integrity of nuclear power plants. As a result, it is expected that the time from the occurrence of a leak to double-end instantaneous fracture has been shortened.

[0005] Figure 2 is an exemplary drawing illustrating an ALMS that detects leakage in equipment within a nuclear power plant (e.g., pressure vessels, pipes, or generators placed within the power plant). Referring to Figure 2, the reactor system structural integrity monitoring system is a facility that examines the integrity and abnormality of key equipment and structures of an operating nuclear power plant online, and the acoustic leak monitoring device (ALMS: Acoustic Leak Monitoring System) is a system that quickly detects acoustic emission (AE) signals generated at a pressure boundary surface online to determine whether there is a leakage.

[0006] To achieve this, a total of 13 or more Acoustic Emission Sensors (AE) are installed, and nuclear power plants equipped with structural integrity monitoring systems record Acoustic Emission (AE) signals. These acoustic leak monitoring devices enable early detection of reactor system leaks, thereby preventing major accidents, such as reactor coolant leaks, and thus ensuring the safety of nuclear power plants.

[0007] In particular, as the lifespan of nuclear power plants is extended, the time between detection of leaks in equipment within the nuclear power plant (e.g., pressure vessels, pipes, or generators located within the power plant) through acoustic leak monitoring devices and maintenance is expected to be further reduced.

[0008] FIGS. 3 and 4 are exemplary drawings for explaining a method of monitoring a leak in equipment (e.g., a pressure vessel, pipe, or generator placed within a nuclear power plant) and then performing maintenance (O&M) using only signals generated from the leak in equipment (e.g., a pressure vessel, pipe, or generator placed within a nuclear power plant).

[0009] Referring to FIGS. 3 and 4, conventional ALMSs only monitor signal changes to monitor leakage at the pressure boundary, making it impossible to determine where and to what extent the reliability of equipment within a nuclear power plant (e.g., pressure vessels, piping, or generators located within the power plant) has deteriorated. More specifically, as the average lifespan of nuclear power plants in South Korea increases and is extended, only the signals in the O&M section of FIG. 4 are used, resulting in a reduction in the available time for maintenance due to material deterioration.

[0010] To solve these problems, a method of monitoring crack growth by analyzing signals measured through existing ALMS and acoustic emission sensors has emerged, and a signal representing crack growth in the table of Fig. 3, i.e., a signal indicated as crack n (n is 1, 2, 3, 4, 쪋 n, Break) in Fig. 4 can be used to predict leakage in a nuclear power plant.

[0011]

[0012] The technical problem that the present disclosure seeks to solve is a method for detecting cracks in a nuclear power plant and predicting leakage of equipment (e.g., pressure vessels, pipes, or generators placed within the nuclear power plant) within the nuclear power plant, thereby providing sufficient time for maintenance of the nuclear power plant.

[0013]

[0014] According to some embodiments, a system for predicting a leak in a nuclear power plant (e.g., a pressure vessel, a pipe, or a generator placed in the power plant) based on an acoustic emission (AE) test includes: a plurality of AE sensors for receiving AE (Acoustic Emission) signals generated from a plurality of cracks; and a plurality of Acoustic Leak Monitoring Systems (ALMS) for determining whether a leak exists based on the AE signals received from the plurality of AE sensors, wherein the system calculates a location where the cracks have occurred through time-difference analysis of the AE signals, distinguishes the AE signals according to the source that generated the cracks, evaluates each of the plurality of cracks using the distinguished AE signals, and predicts whether the leak exists based on the location where the cracks have occurred and the evaluation results of the cracks.

[0015] Evaluating each of the plurality of cracks using the AE signals according to one embodiment includes evaluating each of the plurality of cracks after compensating for AE signals that are attenuated according to a distance along which the plurality of cracks propagate.

[0016] After compensating for the AE signals according to one embodiment, evaluating each of the plurality of cracks using the AE signals includes selecting a plurality of AE parameters for each of the plurality of AE signals, extracting a derivative for a graph in which each of the plurality of AE parameters serves as an axis, and evaluating each of the plurality of cracks using a slope calculated through the extracted derivative.

[0017] Calculating the slope through the extracted derivative according to one embodiment includes calculating the average and standard deviation for the extracted derivative, classifying it into a plurality of crack modes using the calculated average and standard deviation, and calculating the slope for each of the plurality of crack modes.

[0018] For the graph displayed as the above derivative according to one embodiment, the accumulated y-axis is changed to a log scale starting from the high value of the x-axis, and the multiple crack modes are displayed by distinguishing them using the calculated average and standard deviation.

[0019] The plurality of crack modes according to one embodiment include a micro crack mode, a micro / macro crack mode, and a macro crack mode.

[0020] According to one embodiment, the plurality of AE parameters are at least one of amplitude, energy, and number of impacts.

[0021] In one embodiment, the evaluation of the crack is performed by synthesizing the slope values ​​of the plurality of crack modes and evaluating them according to an evaluation index, wherein the evaluation index includes at least one of the evaluations of no damage, microscopic damage, moderate damage, severe damage, microscopic leak, moderate leak, and near to fracture.

[0022] According to some embodiments, a method for operating a leakage prediction system for equipment in a nuclear power plant (e.g., a pressure vessel, a pipe, or a generator placed in the power plant) based on an acoustic emission (AE) test receives AE (Acoustic Emission) signals generated from a plurality of cracks through a plurality of AE sensors, and predicts whether there is a leakage based on the AE signals received from the plurality of AE sensors through a plurality of Acoustic Leak Monitoring Systems (ALMS), calculates a location where the cracks have occurred through a time difference analysis of the AE signals, distinguishes the AE signals according to the source that caused the cracks, evaluates each of the plurality of cracks using the distinguished AE signals, and predicts whether there is a leakage based on the location where the cracks have occurred and the evaluation of each of the plurality of cracks.

[0023] Evaluating each of the plurality of cracks using the AE signals according to one embodiment includes evaluating each of the plurality of cracks after compensating for AE signals that are attenuated according to a distance along which the plurality of cracks propagate.

[0024] After compensating for the AE signals according to one embodiment, evaluating each of the plurality of cracks using the AE signals includes selecting a plurality of AE parameters for each of the plurality of AE signals, extracting a derivative for a graph in which each of the plurality of AE parameters serves as an axis, and evaluating each of the plurality of cracks using a slope calculated through the extracted derivative.

[0025] Calculating the slope through the extracted derivative according to one embodiment includes calculating the average and standard deviation for the extracted derivative, classifying it into a plurality of crack modes using the calculated average and standard deviation, and calculating the slope for each of the plurality of crack modes.

[0026] For the graph displayed as the above derivative according to one embodiment, the accumulated y-axis is changed to a log scale starting from the high value of the x-axis, and the multiple crack modes are displayed by distinguishing them using the calculated average and standard deviation.

[0027] The plurality of crack modes according to one embodiment include a micro crack mode, a micro / macro crack mode, and a macro crack mode.

[0028] According to one embodiment, the plurality of AE parameters are at least one of amplitude, energy, and number of impacts.

[0029] In one embodiment, the evaluation of the crack is performed by synthesizing the slope values ​​of the plurality of crack modes and evaluating them according to an evaluation index, wherein the evaluation index includes at least one of the evaluations of no damage, microscopic damage, moderate damage, severe damage, microscopic leak, moderate leak, and near to fracture.

[0030]

[0031] Through the present invention, sufficient time can be provided for maintenance of a nuclear power plant by detecting cracks in the nuclear power plant and predicting leakage of equipment within the nuclear power plant (e.g., pressure vessels, pipes, or generators placed within the power plant).

[0032] More specifically, conventional acoustic leak monitoring devices can only detect leaks in equipment within a nuclear power plant, but cannot determine specifically where and how much the leak occurred.

[0033] However, through the acoustic emission test-based nuclear power plant leakage prediction system of the present invention, it is possible to specifically determine the specific point where a leakage occurred in equipment within the power plant (e.g., a pressure vessel, pipe, or generator placed within the power plant) and how much the crack causing the leakage has grown.

[0034]

[0035] Figure 1 is a conceptual diagram for explaining a double-ended instantaneous fracture.

[0036] FIG. 2 is an exemplary drawing illustrating an ALMS that detects leaks in equipment within a nuclear power plant (e.g., pressure vessels, pipes, or generators located within the power plant).

[0037] FIGS. 3 and 4 are exemplary drawings for explaining a method of monitoring a leak in equipment (e.g., a pressure vessel, pipe, or generator placed within a nuclear power plant) and then performing maintenance (O&M) using only signals generated from the leak in equipment (e.g., a pressure vessel, pipe, or generator placed within a nuclear power plant).

[0038] Figure 5 is an exemplary drawing illustrating a method for evaluating the condition of a crack using one AE sensor.

[0039] FIG. 6 is an exemplary diagram illustrating a method of evaluating each crack after distinguishing the location of crack occurrence using a parallax analysis method using a plurality of AE sensors according to some embodiments of the present invention.

[0040] FIG. 7 is an exemplary drawing to further explain the method of FIG. 6 according to some embodiments of the present invention.

[0041] Fig. 8 is an exemplary diagram illustrating a method for solving an attenuation problem caused by the propagation characteristics of an AE signal.

[0042] Figure 9 is an exemplary table of multiple AE parameters.

[0043] Figure 10 is an exemplary graph for obtaining the average and standard deviation of derivatives of multiple AE parameters measured through multiple AE sensors.

[0044] Figure 11 is an exemplary graph for evaluating and distinguishing crack modes by adjusting the standard deviation scale in the graph of Figure 10.

[0045] Figure 12 is an exemplary graph showing the slope according to the crack mode.

[0046] Figure 13 is an exemplary drawing to explain an example in which a crack has occurred.

[0047] Fig. 14 is an exemplary graph showing the micro crack mode according to the example of Fig. 13.

[0048] FIG. 15 is an exemplary graph showing the micro / macro crack mode according to the example of FIG. 13.

[0049] Fig. 16 is an exemplary graph showing a macro crack mode according to the example of Fig. 13.

[0050] FIG. 17 is an exemplary evaluation index for explaining a leakage prediction method using Mb-value through a leakage prediction system of equipment (e.g., pressure vessels, pipes, or generators placed within a nuclear power plant) based on acoustic emission (AE) testing according to some embodiments.

[0051] FIGS. 18 to 20 are exemplary drawings for explaining a method of calculating Mb-value according to some embodiments of the present invention.

[0052] FIG. 21 is an exemplary flowchart illustrating a method of operation of a leakage prediction system for equipment (e.g., pressure vessels, pipes, or generators placed within a nuclear power plant) based on acoustic emission (AE) testing according to some embodiments.

[0053] FIG. 22 is another exemplary flowchart illustrating a method of operation of a leakage prediction system for equipment (e.g., pressure vessels, pipes, or generators located within a nuclear power plant) based on acoustic emission (AE) testing according to some embodiments.

[0054]

[0055] Hereinafter, preferred embodiments of the present disclosure will be described in detail with reference to the attached drawings. The advantages and features of the present disclosure, and methods for achieving them, will become clearer with reference to the embodiments described in detail below together with the attached drawings. However, the technical spirit of the present disclosure is not limited to the embodiments described below and may be implemented in various different forms. These embodiments are provided only to ensure that the present disclosure is complete and to fully inform those skilled in the art of the present disclosure of the scope of the present disclosure, and the technical spirit of the present disclosure is defined only by the scope of the claims.

[0056] When assigning reference numerals to components in each drawing, it should be noted that identical components are assigned the same numerals whenever possible, even if they appear on different drawings. Furthermore, when describing the present disclosure, if a detailed description of a related known configuration or function is deemed likely to obscure the gist of the present disclosure, such detailed description will be omitted.

[0057] Unless otherwise defined, all terms (including technical and scientific terms) used herein may be used in the same sense as commonly understood by those of ordinary skill in the art to which this disclosure pertains. Furthermore, terms defined in commonly used dictionaries are not to be interpreted ideally or excessively unless explicitly and specifically defined otherwise. The terminology used herein is for the purpose of describing embodiments and is not intended to limit the disclosure. In this specification, singular forms also include plural forms, unless specifically stated otherwise.

[0058] Spatially relative terms such as "below," "beneath," "lower," "above," and "upper" can be used to easily describe the relationship between one component and other components as depicted in the drawings. Spatially relative terms should be understood to include different orientations of the components during use or operation in addition to the orientations depicted in the drawings. For example, if a component depicted in the drawings were flipped over, a component described as "below" or "beneath" another component could end up "above" the other component. Thus, the exemplary term "below" can include both the above and below orientations. Components can also be oriented in other directions, and thus spatially relative terms can be interpreted accordingly.

[0059] Additionally, terms such as first, second, A, B, (a), (b), etc. may be used to describe components of the present disclosure. These terms are only intended to distinguish the components from other components, and the nature, order, or sequence of the components are not limited by the terms. When a component is described as being "connected," "coupled," or "connected" to another component, it should be understood that the component may be directly connected or connected to the other component, but another component may also be "connected," "coupled," or "connected" between each component.

[0060] The terms "comprises" and / or "comprising" as used in the specification do not exclude the presence or addition of one or more other components, steps, operations and / or elements.

[0061] Before explaining this specification, let us clarify some terms used in this specification.

[0062] Hereinafter, some embodiments of the present disclosure will be described in detail with reference to the attached drawings.

[0063] Figure 5 is an exemplary drawing illustrating a method for evaluating the condition of a crack using one AE sensor.

[0064] Referring to Fig. 5, a diagram illustrating a method for evaluating cracks using conventional AE signals, wherein AE signals are generated from cracks, propagate through the surface of a solid structure, and are then measured by an AE sensor. Fig. 6 is an exemplary diagram illustrating a method for evaluating each crack after identifying the location of crack occurrence using a time-difference analysis method using a plurality of AE sensors, according to some embodiments of the present invention.

[0065] However, the method of evaluating the state of a crack using all signals generated within a calculated area using a single AE sensor, as shown in Fig. 5, has a disadvantage in that an accurate evaluation cannot be performed on cracks that are far from the AE sensor.

[0066] Therefore, when performing time-lag analysis using multiple AE sensors, as in the method of FIG. 6 according to some embodiments, the location of crack occurrence can be calculated. AE signals can be separated based on the source of the crack and then analyzed by dividing them into individual signal groups. This can improve the accuracy and reliability of the evaluation of each crack condition.

[0067] Let us examine this in more detail with reference to FIG. 7. FIG. 7 is an exemplary drawing for explaining in more detail the method of FIG. 6 according to some embodiments of the present invention.

[0068] Referring to Fig. 7, AE signals sensed by a plurality of AE sensors can be generated from each of a plurality of cracks, propagate through the surface of a solid structure, and then be measured by each of the plurality of AE sensors. Using a plurality of AE sensors and using time difference analysis, the location of the crack occurrence can be calculated. After separating the AE signals according to the source that caused the crack (Crack 1, Crack 2, Crack 3), they can be classified into respective signal groups (Analysis 1, Analysis 2, Analysis 3). In order to evaluate the crack using the separated AE signals, it is first necessary to solve the attenuation problem caused by the propagation characteristics of the AE signal.

[0069] According to some embodiments, a system can be configured to predict leakage by estimating the location of a crack and monitoring the size of the crack, as well as the presence or absence of a crack through the system of FIG. 7.

[0070] That is, the AE signal measured through ALMS for each of multiple cracks (Crack 1 to Crack 3) detected through multiple AE sensors is used to separate the signal according to the location of the crack.

[0071] Afterwards, the Mb-value is extracted by solving the attenuation problem caused by the propagation characteristics of the AE signal according to Fig. 8, which will be described later, using multiple parameters for each signal.

[0072] Thereafter, the state of the crack is evaluated using the slope as described in FIGS. 13 to 16.

[0073] Through this, the ALMS of the leakage prediction system of equipment (e.g., pressure vessels, pipes, or generators placed within the power plant) in a nuclear power plant based on acoustic emission (AE) testing according to some embodiments of the present invention can prevent leakage in advance before a major accident occurs by predicting the location, occurrence, and size of cracks in real time.

[0074] In addition, since the operator of a nuclear power plant can identify the location, size, and condition of cracks without the help of an ALMS signal analysis expert, not only will the efficiency and safety of nuclear power plant operation be increased, but it can also be expanded to various fields such as positive / negative pressure piping and pressure vessels.

[0075] Fig. 8 is an exemplary diagram illustrating a method for solving an attenuation problem due to the propagation characteristics of an AE signal. Fig. 9 is an exemplary table of multiple AE parameters.

[0076] Referring to Fig. 8, the attenuation problem due to the propagation characteristics of the AE signal can be solved by selecting two AE parameters (AE parameter 1, AE parameter 2) with similar attenuation rates according to the propagation distance, expressing them as derivatives, and then using the slope. At this time, the AE parameters used can be, for example, amplitude, energy, hits, etc. The AE parameters can be as shown in Fig. 9 as an example.

[0077] Figure 10 is an exemplary graph for calculating the average and standard deviation of the derivatives of multiple AE parameters measured by multiple AE sensors. Figure 11 is an exemplary graph for evaluating and distinguishing crack modes by adjusting the standard deviation scale in the graph of Figure 10. Figure 12 is an exemplary graph for calculating the slope according to the crack mode.

[0078] Referring to FIGS. 10 and 11, the Mb-value value finally derived through the Mb-value analysis method of FIG. 8 is a slope value derived by calculating the average and standard deviation for the derivative values ​​for AE parameters measured through multiple AE sensors, as in FIG. 10, and adjusting the scale of the standard deviation. FIG. 10 can be, for example, a graph that represents the derivatives for each AE parameter (AE parameter 1, AE parameter 2) graph of FIG. 8 in the form of a frequency distribution table.

[0079] According to some embodiments, the Mb-value finally derived through the Mb-value analysis method may be evaluated as shown in Fig. 11 by dividing into a micro crack mode, a macro crack mode, and a micro / macro crack mode. The micro / macro crack mode may refer to a mode existing between the micro crack mode and the macro crack mode.

[0080] At this time, the graph according to Fig. 10 is accumulated from the high value of the x-axis as in Fig. 11 and then changed to a log scale. Thereafter, using the average and standard deviation calculated in Fig. 10, the mode can be defined as a micro crack mode, a micro / macro crack mode, and a macro crack mode.

[0081] Finally, the slope for each mode can be obtained as shown in Fig. 12. Since the slope obtained in Fig. 12 uses multiple AE parameters that decrease similarly with the propagation distance, the slope value may not change with the propagation distance.

[0082] The slope calculated in Fig. 12 can be collectively referred to as the final value, i.e., the Mb-value value, derived through the Mb-value analysis method described through Figs. 8 to 12.

[0083] For example, in the micro / macro crack mode, the interval where the value increases can be defined as the interval where micro cracks occur. Additionally, in the micro / macro crack mode, the interval where the value decreases can be defined as the interval where macro cracks occur.

[0084] This is explained in detail through examples shown in Figures 13 to 16 below.

[0085] Fig. 13 is an exemplary drawing illustrating an example in which a crack has occurred. Fig. 14 is an exemplary graph in which the AE parameters are expressed as derivatives and then the average and standard deviation are calculated.

[0086] Referring to Fig. 13, the mechanical testing device (Instron in this experiment) ® Using 5982, a destructive experiment is conducted. A: Single edge specimens are formed at the stable yield point. At this time, AE testing according to some embodiments is performed together with the destructive experiment according to Fig. 13.

[0087] AE signals generated from this crack can propagate along the surface of solid materials such as pipes. At this time, since the AE signal is attenuated in proportion to the propagation distance, the crack cannot be accurately evaluated with a single AE parameter through a single AE sensor, as shown in Fig. 5.

[0088] Accordingly, in the present invention, multiple AE parameters measured through multiple AE sensors, as in the method according to FIG. 6 according to some embodiments, can be expressed as derivatives as in FIG. 8, and then the average and standard deviation can be calculated.

[0089] Fig. 14 is an exemplary graph showing the micro crack mode according to the example of Fig. 13. Fig. 15 is an exemplary graph showing the micro / macro crack mode according to the example of Fig. 13. Fig. 16 is an exemplary graph showing the macro crack mode according to the example of Fig. 13.

[0090] In the graphs of Figs. 14 to 16, the thick line represents the Stress [MPa] value, which is the vertical axis on the left, and the thin line connected by circles represents the evaluation of the Mb-Value value, which is the vertical axis on the right. That is, the Stress in the destructive experiment according to Fig. 13 applies the same Stress to the experimental subject for the same period of time as shown in the graphs of Figs. 14 to 16.

[0091] The measured signals of FIGS. 14 to 16 can be calculated through the Mb-value values ​​described above through FIG. 8.

[0092] In order to predict the size and condition of a crack using the Mb-value according to FIG. 8 according to some embodiments, at least 10 destructive tests must be performed using the same material as the target structure.

[0093] A system for predicting leakage of equipment within a nuclear power plant (e.g., pressure vessels, pipes, or generators placed within the power plant) based on acoustic emission (AE) testing according to some embodiments of the present invention can verify the Mb-value through a destructive experiment such as that shown in FIG. 13 and generate an index for evaluating the size and condition of cracks.

[0094] The evaluation indicators according to this may be as shown in Figure 17 below.

[0095] FIG. 17 is an exemplary evaluation index for explaining a leakage prediction method using Mb-value through a leakage prediction system of equipment (e.g., pressure vessels, pipes, or generators placed within a nuclear power plant) based on acoustic emission (AE) testing according to some embodiments.

[0096] Referring to Fig. 17, as described above, the crack mode can be divided into micro, micro / macro, and macro modes.

[0097] And, the micro mode can be divided into cases where the Mb-value value (the Mb-value value finally derived using the Mb-value analysis method described above through Figs. 8 to 11) from the 1st to the 7th rows is less than 0.02, greater than 0.04 and less than 0.02, greater than 0.07 and less than 0.04, greater than 0.075 and less than 0.07, greater than 0.08 and less than 0.075, greater than 0.085 and less than 0.08, and greater than 0.85.

[0098] Additionally, micro / macro mode can be defined as increasing from row 1 to row 2, and decreasing from row 3 to row 7.

[0099] In addition, the macro mode can be divided into cases where the Mb-value from row 1 to row 7 is greater than 0.16, greater than 0.12 and less than 0.16, greater than 0.1 and less than 0.12, greater than 0.007 and less than 0.1, greater than 0.06 and less than 0.07, greater than 0.05 and less than 0.06, and greater than 0.05.

[0100] Afterwards, by synthesizing all the information about Micro Mode, Micro / Macro Mode, and Macro Mode, the condition evaluations can be divided into No Damage, Microscopic Damage, Moderate Damage, Severe Damage, Microscopic Leak, Moderate Leak, and Near to Fracture from Rows 1 to 7, respectively.

[0101] That is, for the micro mode, the Mb-value tends to increase from the 1st row to the 7th row, that is, as the crack becomes more severe, the comparative reference value tends to increase. In addition, for the macro mode, the Mb-value tends to decrease from the 1st row to the 7th row, that is, as the crack becomes more severe, the comparative reference value tends to decrease.

[0102] The Mb-value values ​​for each classified crack mode described through the above-described Fig. 17 are exemplary, and the numerical values ​​can be any values ​​as long as they satisfy the tendency according to the above-described Fig. 17. For example, the standard for the Mb-value in the micro mode of row 1 may be Mb<0.01.

[0103] FIGS. 18 to 20 are exemplary drawings for explaining a method of calculating Mb-value according to some embodiments of the present invention.

[0104] Referring to FIGS. 18 to 20, an AE signal is received through an AE sensor. In this drawing, amplitude information over time is received as an example.

[0105] Afterwards, the mean and standard deviation of the derivative for the corresponding amplitude can be obtained. Afterwards, the mean and standard deviation of the derivative for the number of hits according to the amplitude can be obtained, and a graph of the logarithmic function for the number of hits according to the amplitude can be obtained. Then, the trend of the Mb-value over time can be checked, and the cracks can be evaluated as micro-cracks and macro-cracks in sequence according to the time order.

[0106] FIG. 21 is an exemplary flowchart illustrating a method of operation of a leakage prediction system for equipment (e.g., pressure vessels, pipes, or generators placed within a nuclear power plant) based on acoustic emission (AE) testing according to some embodiments.

[0107] Referring to Fig. 21, AE (Acoustic Emission) signals generated from multiple cracks are received through multiple AE sensors (S10). Based on the AE signals received from the multiple AE sensors through multiple Acoustic Leak Monitoring Systems (ALMS), whether there is a leak is determined, and AE parameters for each of the multiple AE signals are expressed as a derivative, and then the average and standard deviation are calculated (S20). For the graph expressed as the derivative, the y-axis accumulated from the high value of the x-axis is converted to a logarithmic scale (S30). The calculated average and standard deviation are used to distinguish and display multiple crack modes (S40). The slope for each of the multiple crack modes is calculated, and an evaluation of the crack is performed based on an evaluation index (S50).

[0108] FIG. 22 is another exemplary flowchart illustrating a method of operation of a leakage prediction system for equipment (e.g., pressure vessels, pipes, or generators located within a nuclear power plant) based on acoustic emission (AE) testing according to some embodiments.

[0109] Referring to FIG. 22, an operation method of a leakage prediction system in a nuclear power plant (e.g., a pressure vessel, a pipe, or a generator placed in the power plant) based on an acoustic emission (AE) test according to some embodiments includes receiving AE (Acoustic Emission) signals generated from a plurality of cracks through a plurality of AE sensors (S100), determining whether there is a leakage based on the AE signals received from the plurality of AE sensors through a plurality of Acoustic Leak Monitoring Systems (ALMS), calculating the location where the crack occurred through time difference analysis of the AE signals (S200), distinguishing the AE signals according to the source that generated the crack (S300), evaluating each of the plurality of cracks using the distinguished AE signals (S400), and predicting whether there is a leakage based on the distinction according to the location where the crack occurred and the degree of the crack (S500).

[0110] Although the embodiments of the present disclosure have been described with reference to the attached drawings, those skilled in the art will appreciate that the present disclosure can be implemented in other specific forms without altering the technical spirit or essential features thereof. Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive. The scope of protection of the present disclosure should be interpreted by the claims below, and all technical ideas within a scope equivalent thereto should be interpreted as being included within the scope of the rights of the present disclosure.

Claims

1. Multiple AE (Acoustic Emission) sensors receiving AE (Acoustic Emission) signals generated from multiple cracks; and Including a plurality of acoustic leak monitoring devices (ALMS: Acoustic Leak Monitoring Systems) that determine whether there is a leak based on AE signals received from the plurality of AE sensors. By analyzing the time difference of the above AE signals, the location where the crack occurred is calculated, The AE signals are distinguished according to the source that caused the above cracks, Each of the plurality of cracks is evaluated using the above-described AE signals, A leakage prediction system for a nuclear power plant based on acoustic emission (AE) testing that predicts whether or not a leakage occurs based on the location where the above cracks occurred and the evaluation results of each of the plurality of cracks.

2. In paragraph 1, Evaluating each of the plurality of cracks using the above AE signals is as follows: A leakage prediction system for a nuclear power plant based on acoustic emission (AE) testing, which evaluates each of the plurality of cracks after compensating for AE signals that are attenuated according to the distance over which the plurality of cracks propagate.

3. In paragraph 2, After compensating the above AE signals, evaluating each of the plurality of cracks using the above AE signals is performed. A leakage prediction system for a nuclear power plant based on acoustic emission (AE) testing, which selects a plurality of AE parameters for each of the plurality of AE signals, extracts a derivative for a graph in which each of the plurality of AE parameters is an axis, and evaluates each of the plurality of cracks using a slope calculated through the extracted derivative.

4. In paragraph 3, Calculating the slope through the above extracted derivative is as follows: For the above extracted derivatives, calculate the mean and standard deviation, Using the above calculated average and standard deviation, it is divided into multiple crack modes, A leakage prediction system for a nuclear power plant based on acoustic emission (AE) testing that calculates a slope for each of the above multiple crack modes.

5. In paragraph 4, For the graph shown above as a derivative, change the accumulated y-axis to a logarithmic scale starting from the high value of the x-axis, A leakage prediction system for a nuclear power plant based on acoustic emission (AE) testing, which is classified into multiple crack modes using the calculated average and standard deviation.

6. In paragraph 4, The above multiple crack modes include a micro crack mode, a micro / macro crack mode, and a macro crack mode, and a leakage prediction system for a nuclear power plant based on acoustic emission (AE) testing.

7. In paragraph 3, A leakage prediction system for a nuclear power plant based on acoustic emission (AE) testing, wherein the above plurality of AE parameters are at least one of amplitude, energy, and number of impacts.

8. In paragraph 4, The evaluation of the above crack is evaluated according to an evaluation index by synthesizing the slope values of the multiple crack modes. A leakage prediction system for facilities in a nuclear power plant based on acoustic emission (AE) testing, wherein the above evaluation indicators include at least one of the following condition evaluations: no damage, microscopic damage, moderate damage, severe damage, microscopic leak, moderate leak, and near to fracture.

9. Receive AE (Acoustic Emission) signals generated from multiple cracks through multiple AE sensors, Predicting whether there is a leak based on AE signals received from multiple AE sensors through multiple Acoustic Leak Monitoring Systems (ALMS). By analyzing the time difference of the above AE signals, the location where the crack occurred is calculated, The AE signals are distinguished according to the source that caused the above cracks, Each of the plurality of cracks is evaluated using the above-described AE signals, A method for operating a leakage prediction system for a nuclear power plant based on acoustic emission (AE) testing, comprising predicting whether or not a leakage occurs based on an evaluation of each of the plurality of cracks and the location where the crack occurred.

10. In paragraph 9, Evaluating each of the plurality of cracks using the above AE signals is as follows: A method for operating a leakage prediction system that evaluates each of the plurality of cracks after compensating for AE signals that are attenuated according to the distance over which the plurality of cracks propagate.

11. In paragraph 10, After compensating the above AE signals, evaluating each of the plurality of cracks using the above AE signals is performed. A method for operating a leakage prediction system for a nuclear power plant based on acoustic emission (AE) testing, wherein, for each of the plurality of AE signals, a plurality of AE parameters are selected, a derivative for a graph in which each of the plurality of AE parameters is an axis is extracted, and a slope calculated through the extracted derivative is used to evaluate each of the plurality of cracks.

12. In paragraph 11, Calculating the slope through the above extracted derivative is as follows: For the above extracted derivatives, calculate the mean and standard deviation, Using the above calculated average and standard deviation, it is divided into multiple crack modes, A method for operating a leakage prediction system for a nuclear power plant based on acoustic emission (AE) testing, which calculates a slope for each of the above-mentioned multiple crack modes.

13. In paragraph 12, For the graph shown above as a derivative, change the accumulated y-axis to a logarithmic scale starting from the high value of the x-axis, A method for operating a leakage prediction system for a nuclear power plant based on acoustic emission (AE) testing, which is divided into multiple crack modes and displayed using the above-described calculated average and standard deviation.

14. In paragraph 12, The above multiple crack modes include a micro crack mode, a micro / macro crack mode, and a macro crack mode. A method for operating a leakage prediction system for a nuclear power plant based on acoustic emission (AE) testing.

15. In paragraph 11, A method for operating a leakage prediction system for a nuclear power plant based on acoustic emission (AE) testing, wherein the above plurality of AE parameters are at least one of amplitude, energy, and number of impacts.

16. In paragraph 12, The evaluation of the above crack is evaluated according to an evaluation index by synthesizing the slope values of the multiple crack modes. A method of operating a leakage prediction system for a nuclear power plant based on acoustic emission (AE) testing, wherein the above evaluation indicators include at least one of the following condition evaluations: No damage, Microscopic damage, Moderate damage, Severe damage, Microscopic Leak, Moderate Leak, and Near to Fracture.

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