KR101404027B1 - System, apparatus, method and computer readable recording medium of estimating precise source location for power plant structure by using a 3-d point location technique - Google Patents

Abstract

The present invention relates to a system for estimating the defect location of a large structure using a three-dimensional point location estimating technology. The system comprises multiple sensors mounted on each different locations of an object to detect target signals including acoustic emission signals generated by cracks and acoustic vibration signals generated by leakage, and a data collection device for collecting the signals detected by the sensors and estimating the defect locations using an intersection point between a first normal line calculated from a pair of first sensors and a second normal line calculated from a pair of second sensors; wherein the cracks and leakage are caused by elastic waves due to the plastic deformation of the object.

Description

TECHNICAL FIELD [0001] The present invention relates to a system, a method, and a computer readable recording medium for a large structure using a three-dimensional point position estimation technique, and a computer readable recording medium. LOCATION TECHNIQUE}

More particularly, the present invention relates to a system for estimating the position of a defect in a large structure, and more particularly, to a system for estimating the position of a defect in a large structure, A method and a computer-readable recording medium for estimating a defect location of a large structure using a three-dimensional point location technique for estimating a defect location of a large structure such as a large boiler, a steel company's funace, and the like.

In recent years, as the society has rapidly become urbanized, industrialized, and specialized, the need for high-power industrial facilities based on efficient energy distribution is increasing. In response to this, energy generation facilities such as water pipes and gas pipes, It continues.

However, as the construction of these large-scale industrial facilities increases, there is a growing concern about stability. Especially, when defects are found in the internal structures of the facilities concealed from outside, stability and efficiency are seriously threatened . Therefore, it is very important to detect early defects such as micro-deformation and micro-cracks of internal structures for industrial facilities and cope with them appropriately. Accordingly, non-destructive inspection, which can check the defect without damaging the object, is receiving attention.

Generally, the nondestructive testing method is a collective term of technical actions that investigate and judge incompleteness without damaging the object by using the physical phenomenon of the material, and specific examples are radiation penetration method, ultrasonic flaw detection method, magnetic detection method, , Electromagnetic induction method, and the like. However, most of these non-destructive testing methods are not applicable to internal structures with limited access because they take the form of direct and one-time energy application to the object to check the presence or absence of defects. In addition, It is difficult to detect defects only after considerable damage has occurred.

On the other hand, the conventional defect diagnosis system is a simple surveillance system that receives an appropriate signal from a sensor installed in an object and generates an alarm when the size and characteristic value of the signal are higher than the reference signal and informs the management.

For example, the defective signal of the rotating body can be determined by examining the fault frequency of the object and by ascending the value of the Root Mean Square (RMS) value higher than the reference signal, and determining the severity thereof.

Such a conventional system is characterized in that a characteristic signal is acquired at a point of time when a defect has progressed considerably and is dependent on post-maintenance. Therefore, since it is difficult to detect the early defect, the system is shut down at the time when the failure is determined to be obvious, which causes a problem that the loss scale is large.

In addition, it is difficult to integrate and diagnose data with a structure that is diagnosed by a separate system such as a conventional low-frequency measurement device, a high-frequency measurement device, and a temperature measurement device.

In addition, the location of the defect can be identified in the approximate area of the signal input from the sensor located closest to the signal source so that the approximate position can be grasped. However, due to the noise included in the surrounding environment and the attenuation characteristics of the structure, There is a limit to the location of fragmented defects.

Meanwhile, the conventional methods generate a pseudo crack signal by using a pencil lead break (PLB) on a target object, and generate a pseudo-crack signal by using a time-of-arrival (TOA) Dimensional and three - dimensional position estimation. However, the TOA-based position estimation method can not be applied to the position estimation of continuous signals such as leakage, and is applied to actual objects such as large structures as well as research on structures and laboratory environments such as compact concrete rather than actual large structures There is a limit.

An object of the present invention is to provide a fault location estimation system, method, and computer program for a large structure using a three-dimensional point location technique for automatically detecting a leak time in the early stage of occurrence to increase the operation efficiency of a large structure And to provide a readable recording medium.

It is another object of the present invention to accurately estimate a distance to a leakage position using a root mean squared (RMS) ratio of a sensing signal between sensors, and to easily detect a leakage A method and a computer-readable recording medium for estimating a defective position of a large structure using a three-dimensional point position estimation technique capable of estimating a position on a plane.

In order to achieve the above-described object of the present invention and to achieve the specific effects of the present invention described below, the characteristic structure of the present invention is as follows.

According to an aspect of the present invention, there is provided a fault location estimation system for a large structure using a three-dimensional point position estimation technique, the system comprising: A plurality of sensors for sensing a target signal including a signal and an acoustic vibration signal due to leakage generation; And a data collection device for collecting signals sensed by the sensors and estimating a defect position using an intersection of a first normal line calculated from the first sensor pair and a second normal line calculated from the second sensor pair.

Advantageously, the data collection device is configured to calculate the first normal by the mean square root (RMS) of the first pair of sensors, and the second normal to the mean square root (RMS) of the second sensor pair .

Advantageously, the data acquisition device estimates a position in the z-axis direction of the defect location from a third sensor group comprising at least two sensors.

Preferably, the data collecting device estimates a position on the three-dimensional plane of the defect from a position estimated using the intersection and a position with respect to the z-axis direction.

Preferably, the data acquisition device calculates a position in the z-axis direction by a mean square root (RMS) of the third sensor group.

Preferably, the third sensor group is arranged in a zigzag manner with the first sensor pair or the second sensor pair.

Preferably, the data collecting apparatus displays points where defects of the object occur, and groups the points so that defects are generated in three dimensions.

According to another aspect of the present invention, there is provided a method of estimating a defect position of a large structure using a three-dimensional point position estimation technique, wherein each step performed by a data collection device for estimating a defect position of a target object comprises: Collecting a sensed signal from a plurality of mounted sensors; Calculating a first normal from the first sensor pair of the sensors; Calculating a second normal from the second sensor pair among the sensors; And estimating a defect position using the calculated intersections of the first and second normal lines.

Preferably, the step of calculating the first normal may calculate the first normal by the mean square root (RMS) of the first sensor pair.

Preferably, the step of calculating the second normal line calculates the second normal line by the mean square root (RMS) of the second sensor pair.

Advantageously, the method further comprises estimating a position in the z-axis direction of the defect location from a third sensor group comprising at least two sensors.

Advantageously, the method further comprises estimating a position on the three-dimensional plane of the defect from the position estimated with the intersection point and the position relative to the z-axis direction.

Preferably, the step of estimating the position with respect to the z-axis direction calculates a position with respect to the z-axis direction by the mean square root (RMS) of the third sensor group.

Preferably, the third sensor group is arranged in a zigzag manner with the first sensor pair or the second sensor pair.

Advantageously, the method further comprises the step of displaying the position where the defect of the object has occurred as a point, clustering the displayed point, and displaying the occurrence position of the defect in three dimensions.

Meanwhile, the information for performing the defect location estimation method of the large structure using the three-dimensional point location technique can be stored in a recording medium readable by the server computer. Such a recording medium includes all kinds of recording media in which programs and data are stored so that they can be read by a computer system. Examples include ROMs (Read Only Memory), Random Access Memory, CD (Compact Disk), DVD (Digital Video Disk) -ROM, magnetic tape, floppy disk, optical data storage device, (For example, transmission over the Internet). Such a recording medium may also be distributed over a networked computer system so that computer readable code in a distributed manner can be stored and executed.

As described above, according to the present invention, it is possible to accurately estimate the position of a defect by applying the present invention to a leakage monitoring technology of a large structure.

In addition, according to the present invention, the estimated position applied to a large structure leakage detection system using a vibration and acoustic composite sensor is located within a distance of about 3 m in diameter from the actual leakage position. From this result, The validity was confirmed experimentally.

In addition, according to the present invention, there is an advantage in that the operation efficiency can be improved by replacing the existing system which only indicates leakage or leakage, thereby shortening the stopping time of the power plant.

1A to 1F are views showing a leakage detection method in various large structures according to the present invention.
2 is a diagram showing a detailed configuration of a system according to an embodiment of the present invention.
3 is a diagram illustrating an actual hardware configuration according to an embodiment of the present invention.
4 is a diagram illustrating an input signal and a processed signal according to an embodiment of the present invention.
5 is a block diagram showing functional blocks for estimating a defect position of a large structure according to an embodiment of the present invention.
6 is a flowchart illustrating a defect location estimation procedure of a large structure according to an embodiment of the present invention.
7 is a graph showing the determination of a defect signal according to an embodiment of the present invention.
8A and 8B are views showing a concept of a two-dimensional position estimation method using an RMS size ratio according to an embodiment of the present invention.
9 is a diagram showing a two-dimensional position estimation method.
10 is a diagram illustrating a one-dimensional position estimation method using an RMS size ratio.
11 is a diagram showing a concept of a position estimation method using a parabolic intersection method.
12 is a diagram illustrating a concept of a position estimation method using a normal intersection method according to an embodiment of the present invention.
13 is a diagram showing an example of calculation of two-dimensional position estimation by the normal intersection method according to the embodiment of the present invention.
14 is a diagram illustrating a concept of a three-dimensional position estimation method according to an embodiment of the present invention.
15 is a diagram showing an example of a sensor arrangement structure according to an embodiment of the present invention.
16A and 16B are views showing an example of a sensor installation covering a water cooling wall of a boiler according to an embodiment of the present invention.
17 to 19 are diagrams showing RMS changes in various situations according to an embodiment of the present invention.
20 is a diagram showing a result of position estimation using actual leakage data according to an embodiment of the present invention.
21 is a diagram showing a result of three-dimensional position estimation displayed on client software according to an embodiment of the present invention.
FIG. 22 is a diagram showing a result of three-dimensional position estimation in front and plan view according to an embodiment of the present invention. FIG.
23A and 23B are views showing the shape of a tube that is filed as a result of boiler tube leakage.

The following detailed description of the invention refers to the accompanying drawings, which illustrate, by way of illustration, specific embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention. It should be understood that the various embodiments of the present invention are different, but need not be mutually exclusive. For example, certain features, structures, and characteristics described herein may be implemented in other embodiments without departing from the spirit and scope of the invention in connection with an embodiment. It is also to be understood that the position or arrangement of the individual components within each disclosed embodiment may be varied without departing from the spirit and scope of the invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is to be limited only by the appended claims, along with the full scope of equivalents to which the claims are entitled, if properly explained. In the drawings, like reference numerals refer to the same or similar functions throughout the several views.

The present invention relates to a large-sized structure (for example, a sealed large-sized storage container such as an LNG ship, a general gas storage container, a transportable pressure vessel, an underground piping, a wind power generator, a large boiler for power generation, A system and method for estimating a defect location of a large structure capable of estimating a three-dimensional position of generated defects, and the like.

Meanwhile, the method of estimating a defect position of a large structure according to an embodiment of the present invention can be applied to various types of large structures, not only to specific types of structures. For example, as shown in FIG. 1A, it is possible to estimate a three-dimensional position according to internal and external defects (leakage, crack, corrosion) of a large-scale structure (boiler) for a power plant. It is possible to estimate the three-dimensional position according to the defects of the structure. Also, as shown in FIG. 1C, it is possible to estimate the three-dimensional position according to leakage of the underground pipe, and as shown in FIG. 1D, it is possible to estimate the three-dimensional position according to defects of a structure such as a wind turbine. As shown in FIGS. 1A to 1D, a position expression for a defect signal is indicated as a point, and a defect is found at a high clustering point by using a three-dimensional point location technique can do.

FIG. 1E is a diagram illustrating a method for detecting faults in a wind turbine according to an embodiment of the present invention. Referring to FIG. 1E, the interior of the wind turbine can be monitored by attaching acoustic, vibration, acoustic emission sensors, temperature, and lubrication sensors to the interior of the wind turbine. In addition, external structures of wind turbines can be monitored simultaneously by attaching acoustic emission sensors and corrosion detection sensors. The data collected from the sensors can be collectively collected and processed according to embodiments of the present invention to assess the health as well as diagnosis and prediction of defects.

For example, gears, bearing damage, misalignment, oil, etc. can be monitored, temperature, fire monitoring, turbine blade monitoring, turbine tower health monitoring, and so on. In this way, pre-failure information becomes possible through real-time monitoring and diagnosis of the failure. In addition, when a fire is detected using a fire monitoring sensor, the pressure valve is automatically released, the fire extinguishing liquid is sprayed, and the fire extinguisher is sprayed to the fire occurrence area. In addition, a regular report can be automatically generated and sent, and the maintenance time can be notified to each terminal.

Meanwhile, as shown in FIG. 1E, the severity of the wind power generator can be evaluated based on a risk matrix by internal monitoring and external monitoring data according to an embodiment of the present invention.

As such, the various embodiments of the present invention can be applied to a method of estimating a defect position of various types of large structures. Hereinafter, in order to facilitate understanding of the present invention, a large-sized power generation boiler will be described as an example among various types of large-scale structures.

On the other hand, defects are generally classified into microscopic and macroscopic viewpoints. Defects from a microscopic point of view are signals with a size of a few micrometers that are difficult to see with the naked eye, such as micro cracks, and can be detected by microscopy and other methods. Defects from the macroscopic point of view are classified as corrosion, crack, pin hole, leak, rupture, etc., which can be observed with the naked eye.

In general, the generation of defects is accompanied by a series of processes such as corrosion through oxidation, micro crack growth, small leakage such as pin hole, and rupture. The system according to the present invention can improve the economic efficiency by performing all the defects monitoring during the entire process from the growth of the micro defect of the object to the destruction and further detecting the early defect.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings, so that those skilled in the art can easily carry out the present invention.

First, the structure of a system and an apparatus according to an embodiment of the present invention will be described with reference to FIGS. 1F to 5, and the procedure according to an embodiment of the present invention will be described in detail with reference to FIG.

1F is a view showing a leakage detection method in a boiler structure for large power generation according to an embodiment of the present invention. In order to perform leakage detection in a large structure such as a power generation facility, first of all, structural characteristics and signal propagation path of the signal are analyzed and then the characteristics of the leakage signal ) And confirmation of detection.

FIG. 1F shows a method of detecting leakage when a leakage occurs in a furnace in a large power generation boiler structure. Referring to FIG. 1F, the boiler structure of the power generation facility is divided into four main tubes: a superheater (S / H), a reheater (R / H), an economizer Economizer (ECO), Water-cooled Wall (Waterwall).

Leaks occur mainly in main tubes located inside the furnace of the boiler. The leakage signal is transmitted to the outer wall of the boiler in the form of an air borne signal through the air in addition to the vibration signal through the pipe. At this time, the leakage signal generated from the boiler waterwall is transmitted to an acoustic waveguide welded on the membrane tube of the outer wall in a structure borne signal system through the structure as shown in the right side of FIG. 1F, The signal is transferred to the composite sensor.

When a leak occurs, the resulting gush gas generates acoustic energy in the supersonic class. This acoustic emission (AE) can be continuously applied to a wide band of frequencies (e.g., 1 kHz-1 MHz) and a narrow band of high frequency (e.g., 175 kHz-750 kHz) band

Most of the conventional leakage monitoring apparatuses detect the leakage signal after the occurrence of an accident and generate an alarm, so that there is a limit in allowing the user to detect the defect at an early stage. Generally, a stage in which leakage occurs in a boiler tube can be divided into crack growth, pinhole generation, pinhole size increase, leakage area expansion, and tube rupture. Most leakage signals are detection methods for leakage signals after the pinhole magnitude increase. In addition, the leakage in the furnace is empirically heard in the human ear within the audible frequency range (20 kHz), and the signal due to the burst due to the growth of cracks in the outer wall, 200 kHz) may not be heard. Therefore, it is necessary to analyze the leakage and leakage characteristics of the leakage signal with both low frequency and high frequency characteristics. As for the characteristics of the leakage signal, the high pressure leakage on the pipe wall vibrates inside the pipe wall with the wide frequency range. Therefore, when only low frequency signals are transmitted over a considerable distance through the gas layer in the pipeline, the signal has a unique acoustical characteristic. Therefore, in the present invention, an acoustic emission and vibration composite sensor capable of simultaneously acquiring acoustic emission of a high frequency (for example, 100 kHz or more) band and a vibration signal of a low frequency (for example, 20 kHz) band is used.

On the other hand, the system according to the present invention can be used as a large-sized storage container such as an LNG ship, a general gas storage container, a transport pressure vessel, an underground piping, a wind power generator, a large boiler for power generation, This is a system for estimating the position of defects in a structure. The site consists of a sensing device for collecting signals from these objects, a signal processing device, and a technology for data collection and analysis and diagnosis.

Therefore, in order to sense leakage, crack, corrosion, temperature, etc. of the object, various sensing devices are attached to the object in the field. Corrosion, cracks, and the like of a specific part of the object based on information such as sound, vibration, and temperature sensed by these sensors.

Thus, the signal sensed from each sensor is signal-processed by a signal processing technique including an amplifier module, a filter module, a trigger module, and the like.

The signal-processed data is collected by a high-speed data signal collection technique and collected remotely by high-speed communication through a wired / wireless interface. The collected data is stored and managed in a large-capacity database. At this time, 3D position estimation, state-based maintenance, defect prediction and diagnosis can be performed according to the present invention from the collected data. On the other hand, physical property information, failure information, past history information, and the like can be structured and stored in the large-capacity database.

2 is a diagram showing a detailed configuration of a system according to an embodiment of the present invention. Referring to FIG. 2, a system configuration according to an embodiment of the present invention is divided into a field, an equipment room, and a main control room according to installation locations, A signal conditioner section, a data acquisition system (DAS) section, and a human machine interface (HMI) section, for example.

In detail, the sensing device portion may include a waveguide 202 attached to each object, sensors 203 and 206, and the signal conditioner portion may include a preamplifier 204 and a main amplifier 205 . The data collection system portion may include an input / output unit (I / O) 207 (or a terminal unit, a DAS 208, a server 209, etc.) The user machine interface portion includes a remote client terminal 210 and can confirm the 3D source position through the remote client terminal 210. [

The processing procedure of the system will be described with reference to FIG.

First, a defect signal generated in the object 201 is input through the various sensors 202 and 206. At this time, when the input object 201 is difficult to directly attach the sensor to a high-temperature region or the like, a waveguide 202 is attached (for example, welded) to the object 201 and the sensor 203 is coupled to the waveguide 202 . On the other hand, when direct installation is possible, the sensor 206 inputs all signals through the input / output unit 207.

On the other hand, when the sensor 202 is an acoustic emission (AE) sensor for detecting cracks, a pre-amplifier 204 pre-amplifies weak signals of several μV to several mV signals.

Next, the main amplifier 205 amplifies the preamplified signal by the preamplifier 204 and outputs the amplified signal as a root mean square (RMS) signal filtered at a specific frequency .

The amplified and filtered signal is input to the DAS 208 through the input / output unit 207, and the DAS 208 performs high-speed signal processing for three-dimensional position estimation of the defect position according to the embodiment of the present invention . At this time, it is possible to perform the estimation of the defect position by extracting the characteristic variable of the AE signal from the dedicated software, performing the signal processing and analyzing it. On the other hand, if the analysis target facility is a power generation facility, it may determine the signal level and provide an alarm and an alarm function.

The collected data can be configured to perform real-time monitoring and diagnosis at the headquarters and regional offices using a monitoring PC installed in the field as well as a remote monitoring system.

The sensing device may be a waveguide or a sensor or may be a waveguide or a preamplifier or a main amplifier, Amp; amp; amp; Amp).

A signal input from the sensor in the field is input to a data acquisition device (DAS) 208 through an input / output unit (I / O) unit (207) provided in an equipment room.

The signal conditioner part amplifies the weak signal input from the sensing device part, extracts only the specific frequency range by the filter, and simultaneously supplies the power to the sensor at the remote location and the signal acquisition can do.

In the meantime, all the signals input in the field are collectively input by the input / output unit 207, and the high-speed data acquisition and signal processing is performed through the data acquisition device (DAS) 208. The server 209 And collects and manages all the input data.

In the main control room, data input from the server 209 is collected from a remote PC to a client PC, and diagnosis of defects and location of defects are performed.

Examples of the sensors 202 and 206 are as follows, but the present invention is not limited thereto.

1) Acoustic sensor for detection of fault diagnosis sound: Monitoring of abnormal noise (joint) and plosive sound using a microphone

2) Acoustic emission sensor for structure defect detection: sensor for receiving and monitoring low frequency acceleration signal (2 ~ 20kHz) and high frequency acoustic emission signal (20kHz ~ 2MHz) at the same time

3) Temperature sensor: A sensor to monitor the temperature change due to defects by attaching a thermoelectric sensor to the main part of the object.

4) Other sensors: Sensor for detecting the normal operation of the system by the external input signal (temperature, pressure, force, displacement, etc.) input during normal operation and operation,

3 is a diagram illustrating an actual hardware configuration according to an embodiment of the present invention.

3, detailed configuration of hardware for defect diagnosis according to an embodiment of the present invention includes a sensor 301 attached to a target object, a preamplifier 302, a main amplifier 303, a signal conditioner 304, And the data collected by the data collection device 305 may be analyzed by the real-time post-processing analysis software 306 to provide features such as feature extraction and selection, clustering, source location estimation, etc. Function can be performed. The data collection device 305 may include an ADC (Analog Digital Convert), a DSP (Digital Signal Processor), an FPGA (Field Programmable Gate Array), and the like.

More specifically, when a stimulus is applied to a target object, a signal is transmitted to the sensor 301 from the target object.

At this time, the signal input from the sensor 301 performs signal conditioning suitable for the type and characteristics of the sensor, and the feature extraction and calculation are performed through the ADC, DSP, and FPGA in the data acquisition device 305 can do.

The signal input from the sensor 301 is composed of a burst signal and a continuous signal as shown in FIG. 4, and the input of the signal is filtered and / It is important to amplify. This role can be achieved through preamplifiers and main amps.

As described above, the conventional system is configured as a system for collecting and processing low frequency and high frequency signals using respective sensors, but the system of the present invention is configured as a system capable of simultaneously collecting characteristic signals for low frequency and high frequency do. That is, the input signal is processed by a method of separating the low-frequency and high-frequency components and processing the corresponding signal (amplification and filtering) to distinguish the feature signals.

In the meantime, the respective components of the apparatus are separately shown in the drawings to show that they can be functionally and logically separated, and do not necessarily mean physically separate components or separate codes.

In this specification, each functional unit (or module) may mean a functional and structural combination of hardware for carrying out the technical idea of the present invention and software for driving the hardware. For example, each functional unit may refer to a logical unit of a predetermined code and a hardware resource for executing the predetermined code, and may be a code physically connected to the functional unit, But can be easily deduced to the average expert in the field of the invention.

Also, in this specification, a database may mean a functional and structural combination of software and hardware that stores information corresponding to each database. The database includes all data storage media and data structures capable of storing information corresponding to the database.

FIG. 5 is a block diagram illustrating each functional block for defect location estimation according to an embodiment of the present invention, and FIG. 6 is a flowchart illustrating a detailed procedure according to an embodiment of the present invention. That is, the functional blocks according to the embodiment of the present invention include various sensors 502 attached to the object 501, a data acquisition unit 503, a data preprocessing unit 504, A Feature Representation 505, a Feature Extraction 506, a Feature Selection 507, an Integrity Evaluation 508, and the like.

Referring to FIG. 5, various signals are input from a sensor 502 installed in a target object 501 in order to estimate a three-dimensional position of a defect.

The input signal is input to the multi-channel signal through the data collecting unit 503 and subjected to data preprocessing such as noise elimination and filtering of the signal input from the data preprocessing unit 504.

As described above, the preprocessed signal has features that can be used to determine the defects of the target object in a feature representation 505, a feature extraction unit 506, a feature selection unit 507, The signal is classified and displayed as a feature signal through the extraction process. For example, the leakage frequency analysis (FFT), the root mean square (RMS), and the like may be displayed by the feature expression unit 505 as representative feature information.

Then, the feature extraction unit 506 extracts a feature signal from the defined feature information (feature extraction). At this time, the feature selecting unit 507 selects a feature signal that responds well to the defect among the extracted signals.

The integrated evaluation unit 508 can perform defect location estimation based on the feature signal thus selected according to an embodiment of the present invention.

Thus, the structure of the system and the apparatus according to the embodiment of the present invention has been described with reference to FIGS. Hereinafter, a procedure according to an embodiment of the present invention will be described in detail with reference to FIG.

FIG. 6 is a flowchart illustrating a process of dividing a procedure according to an embodiment of the present invention into hardware and software. On the other hand, the hardware means that the processing is performed in the internal / external memory through the FPGA or the DSP chip located in the hardware internal memory.

First, unprocessed signals (hereinafter, referred to as 'raw data') of signals input from sensors are input to the sensors.

At this time, since the input signal is weak, an analog signal processing is performed on each of the amplifier (AMP) for performing amplification, the filter for extracting only a specific signal, and the Root Mean Square (RMS) value for signal averaging.

On the other hand, since the primary processed signal includes a large number of noise signals, signal processing using a digital filter and denoising are performed through a signal processing algorithm.

The analog and digital signal processed signals include a feature extraction process for finding feature signals (e.g., Hit, ASL, Energy, Count, Duration, FFT, etc.) that can best represent a defect signal possessed by a target object .

Then, a feature selection process is performed with a signal that best represents the defect in the extracted signal.

And makes a determination based on the determination algorithm for the final defect signal from the selected feature signal.

As a result of the determination, if the defect signal is determined, the 2D and 3D position estimation based on the position estimation algorithm according to the embodiment of the present invention to be described later is performed to grasp the position of the defect with respect to the object. The source location of the defect may be displayed in the form of 1D, 2D, or 3D by the location estimation.

In the meantime, the discrimination of the defect signal can be performed as follows with reference to FIG.

For example, the signals collected in the field are mixed with background noise. Therefore, the background noise signal and the actual defect signal should be distinguished from each other. Accordingly, when the noise signal exceeds a threshold voltage as shown in FIG. 7, it is regarded that an event has occurred from this time, and the characteristic calculation of the signal is performed.

7, characteristics such as amplitude, AE count, rise time, time interval, AE energy, RMS, hit frequency, and the like can be extracted.

More specifically, the amplitude refers to the maximum amplitude as the absolute value of the voltage value of the signal, and the AE count means the number of times the threshold exceeds the threshold during the duration of the signal. Also, the rise time means a time from the beginning of the signal to the peak amplitude, and the time duration means the time from the start of the signal to the end of the signal.

Also, AE energy refers to the integrated value of the rectified signal within the hit period, and RMS means the time-averaged square root of the signal energy in the duration. The hit indicates a signal exceeding a threshold value and indicating a data scale to a system channel and a frequency is a time frequency of a time waveform to convert a frequency to a frequency to determine a leakage center frequency.

Meanwhile, when it is determined that the defect is generated by the feature extraction as described above, the 3D position estimation is performed from the defect signal according to the embodiment of the present invention.

Referring again to FIG. 6, a signal is received from a plurality of sensors (S601) according to an embodiment of the present invention. At this time, the normal line can be calculated from the signal of the first sensor pair (e.g., two sensors) (S602). Further, the normal line can be calculated from the signals of the second sensor pair (e.g., two sensors) (S603).

Then, the intersection of the normal line calculated from each sensor pair is estimated as the source position on the plane (S604). Then, one-dimensional estimation on the Z-axis (S605) is performed. Finally, the three-dimensional position is finally calculated (S606) by summing the source position estimation result on the two-dimensional plane and the one-dimensional estimation result on the Z-axis. Finally, the calculated position is displayed as a cluster on three dimensions (S607).

The method according to an embodiment of the present invention can be implemented in the form of a program command that can be executed through various computer means and recorded in a computer readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions recorded on the medium may be those specially designed and constructed for the present invention or may be available to those skilled in the art of computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape; optical media such as CD-ROMs and DVDs; magnetic media such as floppy disks; Magneto-optical media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine language code such as those produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the present invention, and vice versa.

The defect location estimation procedure of the large structure according to the embodiment of the present invention has been described in detail above. Hereinafter, a method for estimating a three-dimensional position of a leakage position using the RMS size ratio will be described in detail with reference to the embodiments of the present invention.

A leak source location on a two dimensional plane is estimated using at least three sensors and their RMS size ratio (RMS3> RMS2> RMS1) as shown in FIG. 8A .

 FIG. 8A shows the propagation path of the leakage signal measured at the leakage point, and FIG. 8B shows the propagation path of the leakage signal measured by the sensor.

The geometric relationship between these sensors and the position (x, y) on the plane of the leakage source in the position estimation using a pair of sensors among these three sensors can be considered as shown in FIG. The position estimated as the leakage position can be estimated as the intersection point between the parabolic trajectories of the sensor pair (S ₁ , S ₂ ) positioned in the vicinity of the sensor pair (S ₁ , S ₂ ).

In this case, the ratios r ₁ and r ₂ of the distances from the position (x, y) estimated as a leaking source to the two sensors can be expressed by the following Equation 1 by Pythagorean theorem.

Further, if the y coordinate from the two sensors of the leakage location is close to 0, each of (θ _1, θ ₂₎ to the leak location x is organized as follows are as close to ₀ x <Equation 2>.

In Equation (2), D denotes a distance between two sensors S ₁ and S ₂ , and x and y denote leakage position coordinates on a two-dimensional plane. Also, r ₁ and r ₂ denote the distance from the sensors to the leakage point.

The relationship of Equation (1) and Equation (2) can be summarized as Equation (3) below.

Equation (3) can be summarized as Equation (4) as follows.

In addition, it can be summarized as shown in the <Equation 4> to In short for x ² <Equation 5>.

The above Equation (5) can be summarized as follows. Equation (6) can be expressed by the following Equation (6): &quot; (6) &quot; D = (A- It becomes an equation.

Equation (6) can be summarized for y to be an orbit equation as shown in Equation (7) below.

Here, x ₀ can be calculated from the ratio of the RMS values measured from the two sensors, and the distance D between them can be known from the placement information. That is, the RMS value measured at the sensors S ₁ and S ₂ is measured as the distance from the sensor is largely measured, and x ₀ is calculated from the characteristics of the less measured and the positional arrangement information of the sensors as shown in Equation (8) Can be obtained.

Therefore, A can be regarded as a constant in the orbit equation (Equation (7)), and in the case of one-dimensional position estimation (when y = 0), its position can be obtained as shown in Equation (9).

In the equation (9), when A = ((Dx ₀ ) / x ₀ ) ² is substituted, x = x ₀ can be confirmed when y = 0 in the orbit equation.

Therefore, in order to obtain the x value, x ₀ is first obtained and x can be obtained from the above Equation (9).

Hereinafter, a method of estimating a leakage position using the parabolic intersection method and the normal intersection method according to an embodiment of the present invention will be described in detail with reference to FIGS.

FIG. 11 shows an example of position estimation on a two-dimensional plane using the intersections of two parabolic trajectories R1 and R2 produced by the two pairs of sensors S1 / S2 and S1 / S3. The ratio of the sensing sensitivity of a pair of sensors to the leakage occurring at the same position is inversely proportional to the distance from the leakage source. If the position of the signal source is not known, the positions at which the pair of sensors S1 / S2 maintain their daily maintenance of the signal may be at various points rather than at one point on the two-dimensional plane. For example, the positions may be represented by a continuous circle / parabola R1 as shown in FIG.

Likewise, the positions of the other pair of sensors (S1 / S3) that maintain the uniformity of the signal due to leakage occurring at the same position can also form a circle / parabola (R2) The point can be estimated as the position of the leak source.

Such a parabolic intersection location estimation method has an advantage that it can be applied to a wider area than the triangular method of estimating the position using the AE signal time difference between existing sensors.

On the other hand, the position estimation method using the time arrival difference has a disadvantage in that it can not find the leakage position when the leakage occurs outside the triangle due to the restriction that the estimation target position must exist inside the triangle formed by three sensors.

In order to overcome this disadvantage, in the embodiment of the present invention, as shown in FIG. 12, a leakage position estimation method using a normal intersection method of each pair of two sensor pairs is proposed. Referring to FIG. 12, by applying an open triangular sensor arrangement apart from an existing sensor arrangement forming a triangle, it is possible to effectively find a leakage position even when leakage occurs outside the triangle.

On the other hand, as shown in FIG. 12, the positional estimation using the open triangular sensor arrangement type using the normal line intersection method has the same leakage position as compared with the method of FIG.

The normal intersection method according to the embodiment of the present invention will be described in more detail. The intersection of two normal lines (L1, L2) produced by the two sensors S1 / S2, S3 / The position estimation can be more accurately performed.

The ratio of the sensing sensitivity of a pair of sensors to the leakage occurring at the same position is inversely proportional to the distance from the leakage source. By using this, it is possible to find positions that maintain the daily maintenance of the signal received from each sensor in a line segment connecting a pair of sensors (S1 / S2). When the normal line is drawn at the position where the line maintenance is maintained on the line segment connecting S1 and S2, it is equal to L1. In the same way, in the line segment connecting the other pair of sensors (S3 / S4), it is equal to L2 by drawing the normal line at the position where the signal is received from each sensor. According to an embodiment of the present invention, a point at which the two normal lines L1 and L2 intersect can be estimated as a leakage position.

13 shows an example of two-dimensional position estimation calculation by the above-mentioned normal intersection method. Referring to FIG. 13, the result of calculating the two-dimensional arrangement type of the sensor and the final leak estimation position can be known. In FIG. 13, the sensor coordinates and the RMS size measured by the sensor are shown, and the linear equations L1 and L2 relating to the normal are shown. At this time, the intersection of two normal lines or two straight lines becomes a final estimated position.

Meanwhile, the normal method can be calculated as shown in FIG. 13 using the coordinates of the sensor and the RMS value, there is no restriction according to the sensor arrangement type, and the calculation method is simple and quick.

As described above, more precise position estimation is possible by using the normal intersection method according to the embodiment of the present invention to estimate the position of a large structure. In addition, according to various embodiments of the present invention, the three-dimensional position estimation is finally possible by reflecting the result of summing in the one-dimensional z-axis direction on the association result on the two-dimensional plane. When applied to various large structures, it can be estimated more precisely (for example, within 3 meters) as a point. Although the precision position estimation technique according to the embodiment of the present invention has been described as an example of a power plant, it can be equally applied to various large structures such as an underground pipeline and an LNG ship as described above.

FIG. 14 shows a three-dimensional position estimation method using a normal intersection method applied to an actual boiler.

In order to apply the three-dimensional position estimation to the large structure, as shown in FIG. 14, four sensors S1, S2, S3 and S4 located on the front side on the x and y planes are used according to the embodiment of the present invention , And the final three-dimensional position coordinates can be obtained by combining the results of the one-dimensional position estimation on the z-axis.

In order to estimate the 1-dimensional position of the z-axis, the RMS average values of S5, S6, S7 and S8 located on the rear side, which is the sensor having the largest RMS signal response, Dimensional position estimation is performed to obtain a three-dimensional position finally.

In this case, the origin is defined as the origin of the center of the tube of the boiler structure, the sensor installed in the front direction is arranged in a zigzag manner with the sensor in the rear direction, Can be arranged so as to be able to perform a position estimation operation on the received signal.

According to the embodiment of the present invention, when the average value is obtained and applied, the position error according to the signal size can be reduced, and the precision of the position calculation can be improved when the sensor is located at a distance from the leakage position.

Finally, when the position of the three-dimensional image displayed as described above is determined to be a leakage condition that deviates from one specific signal range, it may be indicated as a point, and a place where the cluster degree is high may be regarded as a leakage position. Such a point location method can more accurately and quickly estimate a defect location for a large structure.

Hereinafter, the experimental results when the actual tube leakage occurs will be described with reference to Figs. 15 to 23. Fig. Hereinafter, the reliability of the system installation and 3D position estimation algorithm is verified, and the 3D position estimation result is shown based on the data when the tube leakage of the actual power plant occurs.

As shown in FIG. 15, a three-dimensional modeling was carried out through a boiler structure drawing and an on-site exploration to apply a large structure leakage monitoring system (for example, BTLD (Boiler Tube Leak Detect)) to a thermal power plant. Generally, in the case of 500 MW coal-fired power plant standard, leakage detection can be performed with 16 to 28 sensors, and FIG. 15 shows a case where 28 sensors (for example, mirror type sensor) channels are applied.

 In the case of a large facility such as a fire department, the system is divided into three areas as field, equipment room, and main control room as shown in FIG.

16A and 16B show a detailed installation example of a sensing equipment, in which a waveguide is installed by welding on the outer wall of a boiler, and a sensor is integrally mounted on the waveguide.

Figure 17 shows the RMS trends of the signals collected from the ten sensors installed on the waterwall, which is the outer wall of the boiler, which is well characterized by the presence or absence of leakage before and after the boiler tube leakage. Generally, based on the zone location, it can be assumed that the signal is largely responsive to the source nearest to the source of leakage, so that leakage has occurred around it.

FIG. 18 shows the tendency of selecting the signals of the four channels having the largest signal change during monitoring among the ten sensor signals shown above. If the leakage occurs, the sensors around the sensor will react most sensitively and show a large change. Therefore, it is expected that there is a high probability of leakage around these sensors. As shown in FIG. 17, in the sensors near the leakage, it is confirmed that the signal level is gradually increased by 5 to 8 dB or more from the normal background noise level, and it is estimated that the cause of the leakage is leakage have. Also, trends of these signals are linearly increasing up to just before tube rupture as time elapses, allowing estimation of the presence of leakage around these.

Figure 19 shows the RMS record of CH5 to CH26 from tube rupture due to leakage to plant shutdown. The large structure leakage monitoring system according to the embodiment of the present invention can generate an alarm signal 18 days after the start of recording, that is, 15 days before stoppage due to leakage. As shown in FIG. 19, the RMS signals of the sensor have increased by about 15 dB or more on average after 20 days from the initial leak point. In particular, the signals of the four sensors estimated to be around the leakage increase by about 20 dB or more from 80 dB to 100 dB . If the tube is ruptured after about 18 days after the leakage, it is possible to stop the equipment by the operator.

Large-scale structure leakage monitoring system is composed of main operation data such as power, feed water flow, main steam flow (Main) of the power plant boiler from pin hole occurrence time to boiler tube rupture, steam pressure, furnace pressure, and condense flow from DCS, but there was no change until the tube rupture.

FIG. 20 shows a result of three-dimensional position estimation by a normal intersection method according to an embodiment of the present invention using MATLAB based on actual leakage data. At this time, it can be seen that two clusters are formed around the 16th channel and the 20th and 24th channels, respectively.

If it is confirmed that the signal is a temporary noise component due to a soot blower signal periodically generated in a funace around the 16th channel, it may not be regarded as leakage.

In order to perform three-dimensional position estimation in the vicinity of channels 20 and 24, the position of the two-dimensional leakage is estimated using sensors 18, 20, 24, and 26 located in the x and y axis directions, Dimensional position estimation.

17 to 19, the channels associated with leakage can be identified as 20, 22, 24, and 25, respectively.

In this signal trend, the position of an area suspected of leakage may be estimated based on the 24th signal having the largest signal size. However, in the case of channels 22 and 25, it is not the sensor coordinates located on the same x, y plane and should be replaced with another channel. That is, in order to apply the normal intersection method, the position estimation is performed in the x and y directions by replacing channels 20 and 24 and adjacent channels 18 and 26. As a result, the position estimation can be confirmed around the 24th channel near the intersection of the normal line as shown in Fig. Then, the position estimation for the z-axis direction is performed to finally perform the three-dimensional position estimation.

On the other hand, the three-dimensional position estimation can be performed in the z-axis direction in the tube inside the boiler, and in the case where the tube is absent such as a water-wall, . Therefore, in this case, leakage occurs around the water-wall and the position is estimated in the zone of the corresponding position. In addition, according to the embodiment of the present invention, three-dimensional position estimation is possible near the actual leakage position by performing one-dimensional position calculation in the z-axis direction.

The large-scale structure leakage monitoring system shown in FIG. 21 is a diagram in which a real thermal power generation boiler is reduced to a 1/10 size and constructed into a client software (3D software). The estimated leak points may represent their location on the 3D map of the client software as points. As a result, it can be confirmed that the area having a high degree of clustering is a suspected leak region.

On the other hand, since the leakage signal continues to be input from the sensors until the power plant is stopped, the position estimation result thereof can be displayed as a cluster cumulatively displayed on the 3D screen since it is continuously displayed at regular intervals.

Figs. 21 and 22 show 3D position estimation results according to the sensor signals measured in Figs. 17 to 19. Fig. FIG. 21 shows a position estimation result screen actually seen in software, and FIG. 22 schematically shows a side view and a plan view of the estimated position in the seventh layer of the power plant where the three-dimensional position estimation result is estimated to have caused leakage.

The leakage sources estimated from the results of the software with the implemented algorithm according to the embodiment of the present invention are displayed as two clusters on the screen. In the first cluster, there are about 10 points in the vicinity of channel 16, and the cluster degree is not so large. The second cluster was shown to have a large cluster of about 40 points around channels 20, 22, 24, and 25.

On the other hand, as a result of confirming the leakage area at the actual site, the tube rupture was confirmed at the front surface of 7.5F, which is the water cooling wall area, as shown in Figs. The actual burst position occurred at a position about 3 meters away from the position of the second cluster, which was highly dense in the algorithm implemented according to the embodiment of the present invention.

FIGS. 23A and 23B show the outer shape of the tube and the shape of the welded portion of the 7F ruptured when the tube is leaked, and it can be confirmed that it is possible to sense even a small-sized leak such as a pin hole.

The first clusters can be estimated as a result of the noise signal rather than the actual leakage signal. As described above, the noise is a signal generated by a shuttle blower inside the power plant. Since the characteristic is similar to the leakage signal, the algorithm corresponds to a leakage alarm condition. Therefore, the noise is considered as a temporary leakage, do.

Therefore, in the embodiment of the present invention, by adding the signal size ratio to the area position, the point location technique can be applied to the thermal power plant more precisely, and the location where the cluster degree is high can be estimated as the leakage position. These results are obtained by the three-dimensional position estimation method according to the embodiment of the present invention, and it can be seen that it is near the actual leakage point.

As described above, according to the embodiment of the present invention, it is possible to grasp the leakage position in three dimensions when the boiler tube leaks in a large structure such as a thermal power plant, etc., and it is possible to shorten the power generation stop time and increase the efficiency of the facility. The advantages of the present invention are summarized as follows.

According to the embodiment of the present invention, the position estimation limit that can not be performed by the conventional time arrival difference method can be overcome by using the magnitude ratio of the RMS signal characteristic of the leakage signal for continuous signals such as leakage.

In the case of the parabolic method, it is difficult to estimate the position of the sensor when the outer edge of the sensor is tilted. However, when the proposed method is applied, it is possible to estimate the position of the sensor.

In addition, when the power plant is in operation, the boiler tube leakage monitoring system applying the three-dimensional point positioning technique to the tube leakage can be applied to allow the user to easily determine leakage 20 days before the leakage.

In addition, the system according to the embodiment of the present invention intuitively grasps the position of the place where the leakage occurs, compared with the conventional system in which only the simple alarm signal is generated in the sensor located closest to the position where the leakage occurs, The downtime can be minimized and the power generation efficiency can be maximized. On the other hand, in order to realize more precise position estimation for leakage, it is possible to improve the position estimation on the x, y, and z axes by installing the sensors on the boiler side portions (front, rear) and the headers.

The invention has been described above with the aim of method steps illustrating the performance of certain functions and their relationships. The boundaries and order of these functional components and method steps have been arbitrarily defined herein for convenience of description. Alternative boundaries and sequences may be defined as long as the specific functions and relationships are properly performed. Any such alternative boundaries and sequences are therefore within the scope and spirit of the claimed invention. In addition, the boundaries of these functional components have been arbitrarily defined for ease of illustration. Alternative boundaries can be defined as long as certain important functions are properly performed. Likewise, the flow diagram blocks may also be arbitrarily defined herein to represent any significant functionality. For extended use, the flowchart block boundaries and order may have been defined and still perform some important function. Alternative definitions of both functional components and flowchart blocks and sequences are therefore within the scope and spirit of the claimed invention.

The invention may also be described, at least in part, in the language of one or more embodiments. Embodiments of the invention are used herein to describe the invention, aspects thereof, features thereof, concepts thereof, and / or examples thereof. The physical embodiment of an apparatus, article of manufacture, machine, and / or process for implementing the invention may include one or more aspects, features, concepts, examples, etc., described with reference to one or more embodiments described herein . Moreover, in the entire drawings, embodiments may incorporate the same or similarly named functions, steps, modules, etc. that may use the same or different reference numerals, and so forth, Steps, modules, etc., may be the same or similar functions, steps, modules, etc., or the like.

As described above, the present invention has been described with reference to particular embodiments, such as specific elements, and specific embodiments and drawings. However, it should be understood that the present invention is not limited to the above- And various modifications and changes may be made thereto by those skilled in the art to which the present invention pertains.

Accordingly, the spirit of the present invention should not be construed as being limited to the embodiments described, and all of the equivalents or equivalents of the claims, as well as the following claims, belong to the scope of the present invention .

201: object 202: waveguide
203: sensor 204: preamplifier
205: main amplifier 207: I / O unit
208: DAS 209: Server
210: remote client 301: object
302: Preamplifier 303: Main amplifier
304: Signal conditioner 305: Data collecting device
306: Real-time post-processing analysis S / W 501: Object
502: sensor 503: data collecting unit
504: Data preprocessing unit 505:
506: Feature extraction unit 507: Feature selection unit
508: Integrated evaluation section

Claims (16)

A system for estimating a defect location of a large structure,
A plurality of sensors mounted on different positions of the object to sense a target signal including an acoustic emission signal due to cracking and an acoustic vibration signal due to leak generation from elastic waves according to plastic deformation of the object; And
And a data collection device for collecting signals sensed by the sensors and estimating a defect position using an intersection of a first normal line calculated from the first sensor pair and a second normal line calculated from the second sensor pair, Fault Location Estimation System for Large Structures Using Dimension Point Location Estimation Technique.
The data acquisition system according to claim 1,
Calculating the first normal by the mean square root (RMS) of the first sensor pair, and calculating the second normal by the mean square root (RMS) of the second sensor pair, Fault Location Estimation System for Large Structures Using.
The data acquisition system according to claim 1,
And estimating a position in the z-axis direction of the defect position from a third sensor group including at least two sensors.
4. The data collecting apparatus according to claim 3,
And estimating a three-dimensional position of the defect based on the estimated position using the intersection and the position with respect to the z-axis direction.
4. The data collecting apparatus according to claim 3,
and calculating a position in the z-axis direction by a mean square root (RMS) of the third sensor group.
The apparatus of claim 3, wherein the third sensor group comprises:
Wherein the first sensor pair or the second sensor pair is staggered with respect to the first sensor pair or the second sensor pair.
The data acquisition system according to claim 1,
Wherein the position of the defects of the target object is represented by a point and the points are grouped so as to display the location of defects in a three-dimensional manner.
A method for estimating a defect position of a large structure using a three-dimensional point position estimation technique, the method comprising the steps of:
Collecting a sensed signal from a plurality of sensors mounted at different positions of the object;
Calculating a first normal from the first sensor pair of the sensors;
Calculating a second normal from the second sensor pair among the sensors; And
And estimating a defect position using the calculated intersection points of the first and second normal lines.
9. The method of claim 8, wherein the calculating the first normal comprises:
And calculating the first normal by the mean square root (RMS) of the first sensor pair.
9. The method of claim 8, wherein the calculating the second normal comprises:
And calculating the second normal by the mean square root (RMS) of the second sensor pair.
9. The method of claim 8,
Estimating a position in the z-axis direction of the defect position from a third sensor group including at least two sensors.
12. The method of claim 11,
Estimating a three-dimensional position of the defect from a position estimated using the intersection point and a position with respect to the z-axis direction; and estimating a defect position of the large structure using the three-dimensional point position estimation technique.
12. The method of claim 11, wherein estimating the position in the z-
And calculating a position in the z-axis direction by a mean square root (RMS) of the third sensor group.
12. The apparatus of claim 11, wherein the third sensor group comprises:
Wherein the first sensor pair or the second sensor pair is staggered with respect to the first sensor pair or the second sensor pair.
9. The method of claim 8,
The method according to claim 1, further comprising the steps of: displaying a position where a defect of the target object is generated as a point and grouping the displayed points so as to display a defect occurrence position in three dimensions; .
A computer-readable recording medium having recorded therein a program for executing the method according to any one of claims 8 to 15.

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