• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.5
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• pp.466-472
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• 2009

An acoustic leak monitoring system(ALMS) using acoustic emission(AE) technique was applied for leakage detection of nuclear power plant's pipeline which is operated in high temperature and pressure condition. Since this system only monitors the existence of leak using the root mean square(RMS) value of raw signal from AE sensor, the difficulty occurs when the characteristics of leak size and shape need to be evaluated. In this study, dual monitoring system using AE sensor and accelerometer was introduced in order to solve this problem. In addition, artificial neural network(ANN) with Levenberg.Marquardt(LM) training algorithm was also applied due to rapid training rate and gave the reliable classification performance. The input parameters of this ANN were extracted from varying signal received from experimental conditions such as the fluid pressure inside pipe, the shape and size of the leak area. Additional experiments were also carried out and with different objective which is to study the generation and characteristic of lamb and surface wave according to the pipe thickness.

• Journal of the Korean Magnetics Society
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• v.13 no.2
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• pp.70-75
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• 2003

Non-linear anisotropic materials were used to simulate the effects of bulk tensile stress in 3D finite element analysis (FEA). FEA was used to calculate the effects of near and far-side racetrack pit depth and simulated bulk tensile stress on magnetic flux leakage (MFL) signals. The axial and radial MFL signals were depended on near and far-side racetrack pit depth and on the bulk stress, but the circumferential MFL signal was not depended on them. The axial and radial MFL signals increased with greater pit depth and applied bulk stress, but the circumferential MFL signal was scarcely changed.

• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.433-440
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• 2015

In stochastic hyperspectral target detection algorithms, the target signal components may be included in the background characterization if targets are not rare in the image, causing target leakage. In this paper, the effect of target leakage is analysed and an improved hyperspectral target detection method is proposed by excluding the pixels which have similar reflectance spectrum with the target in the process of background characterization. Experimental results using the AISA airborne hyperspectral data and simulated data with artificial targets show that the proposed method can dramatically improve the target detection performance of matched filter and adaptive cosine estimator. More studies on the various metrics for measuring spectral similarity and adaptive method to decide the appropriate amount of exclusion are expected to increase the performance and usability of this method.

• The Journal of Society for e-Business Studies
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• v.23 no.2
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• pp.111-121
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• 2018

Many problems in rotating machinery such as aircraft engines, wind turbines and motors are caused by bearing defects. The abnormalities of the bearing can be detected by analyzing signal data such as vibration or noise, proper pre-processing through a few signal processing techniques is required to analyze their frequencies. In this paper, we introduce the condition monitoring method for diagnosing the failure of the rotating machines by analyzing the vibration signal of the bearing. From the collected signal data, the normal states are trained, and then normal or abnormal state data are classified based on the trained normal state. For preprocessing, a Hamming window is applied to eliminate leakage generated in this process, and the cepstrum analysis is performed to obtain the original signal of the signal data, called the formant. From the vibration data of the IMS bearing dataset, we have extracted 6 statistic indicators using the cepstral coefficients and showed that the application of the Mahalanobis distance classifier can monitor the bearing status and detect the failure in advance.

• Biomolecules & Therapeutics
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• v.8 no.1
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• pp.63-72
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• 2000

We intended to show some basic requirements for spectrum analysis of electroencephalogram (EEG) by visualizing the differences of the results according to different values of some parameters for analysis. Spectrum analysis is the most popular technique applied for the quantitative analysis of the electroen- cephalographic signals. Each step from signal acquisition through spectrum analysis to presentation of parameters was examined with providing some different values of parameters. The steps are:(1) signal acquisition; (2) spectrum analysis; (3) parameter extractions; and (4) presentation of results. In the step of signal acquisition, filtering and amplification of signal should be considered and sampling rate for analog-to-digital conversion is two-time faster than highest frequency component of signal. For the spectrum analysis, the length of signal or epoch size transformed to a function on frequency domain by courier transform is important. Win dowing method applied for the pre-processing before the analysis should be considered for reducing leakage problem. In the step of parameter extraction, data reduction has to be considered so that statistical comparison can be used in appropriate number of parameters. Generally, the log of power of all bands is derived from the spectrum. For good visualization and quantitative evaluation of time course of the parameters are presented in chronospectrogram.

• Journal of the Korean Society for Nondestructive Testing
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• v.25 no.6
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• pp.439-445
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• 2005

The stud bolt is one of crucial parts for safe operation of reactor vessels in nuclear power plants, Crack initiation and propagation were reported in stud bolts that arc used for closure of reactor vessel and head, Stud bolts are inspected by ultrasonic technique during overhaul periodically for the prevention of stud bolt failure which could induce radioactive leakage from nuclear reactor, In conventional ultrasonic testing for inspection of stud bolts, cracks are detected by using shadow effect It takes too much time to inspect stud bolts by using conventional ultrasonic technique. In addition, there were numerous spurious signals reflected from every oblique surfaces of thread, In this study, the signal processing technique for enhancing conventional ultrasonic technique was introduced for inspecting stud bolts. The signal processing technique provides removing spurious signal reflected from every oblique surfaces of thread and enhances detectability of defects. Detectability for small crack was enhanced by using this signal processing in ultrasonic inspection of stud bolts in Nuclear Power Plants.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.184-192
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• 2022

Valve internal leakage is caused by damage to the internal parts of the valve, resulting in accidents and shutdowns of the piping system. This study investigated the possibility of a real-time leak detection method using the acoustic emission (AE) signal generated from the piping system during the internal leakage of a butterfly valve. Datasets of raw time-domain AE signals were collected and postprocessed for each operation mode of the valve in a systematic manner to develop a data-driven model for the detection and classification of internal leakage, by applying machine learning algorithms. The aim of this study was to determine whether it is possible to treat leak detection as a classification problem by applying two classification algorithms: support vector machine (SVM) and convolutional neural network (CNN). The results showed different performances for the algorithms and datasets used. The SVM-based binary classification models, based on feature extraction of data, achieved an overall accuracy of 83% to 90%, while in the case of a multiple classification model, the accuracy was reduced to 66%. By contrast, the CNN-based classification model achieved an accuracy of 99.85%, which is superior to those of any other models based on the SVM algorithm. The results revealed that the SVM classification model requires effective feature extraction of the AE signals to improve the accuracy of multi-class classification. Moreover, the CNN-based classification can be a promising approach to detect both leakage and valve opening as long as the performance of the processor does not degrade.

• 전자공학회논문지 IE
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• v.46 no.4
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• pp.25-30
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• 2009

The cooling water circulation pipes had been used to drop the temperature of refractory outside shell of blast furnace by cooling plate or stave type. They were attacked by surrounding CO gas and it was the cause that they were corroded and the water inflow in the refractory due to leakage of water. So, the life of refractory material was shorten and changed for the worse the conditions of blast furnace. The automatic sensing device for water leakage of cooling pipe was developed to check the position of trouble by use the micro-process system when cooling water leak and then CO gas will be inflowed into the cooling pipe at the leakage position. The inflowed CO gas will be detected in the micro-process system and delivered the detected position of cooling plate or stave to main control room through the wireless-radio relay station. This system can be possible to detect the position of cooling plate or stave the water leakage part immediately and then deliver the signal to main control room by use the micro-process system and wireless-radio relay station. This system will develop the working condition from manual system to unmanned auto alarm system.

• Steel and Composite Structures
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• v.19 no.6
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• pp.1549-1560
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• 2015

This paper presents the characterization of fatigue crack in the A283 Grade C steel using the MMM method by identifying the effects of magnetic flux leakage towards the crack growth rate, da/dN, and crack length. The previous and current research on the relation between MMM parameters and fatigue crack effect is still unclear and requires specific analysis to validate that. This method is considered to be a passive magnetic method among other Non-Destructive Testing (NDT) methods. The tension-tension fatigue test was conducted with a testing frequency of 10 Hz with 4 kN loaded, meanwhile the MMM response signals were captured using a MMM instrument. A correlation between the crack growth rate and magnetic flux leakage produces a sigmoid shape curve with a constant values which present the gradient, m value is in the ranges of 1.4357 to 4.0506, and the y-intercept, log C in the ranges of $4{\times}10^{-7}$ to 0.0303. Moreover, a linear relation was obtained between the crack length and magnetic flux leakage which present the R-Squared values is at 0.830 to 0.978. Therefore, MMM method has their own capability to investigate and characterize the fatigue crack effects as a main source of fracture mechanism for ferrous-based materials.

• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.3
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• pp.262-267
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• 2007

Detection of gas leak from a tube is a very important issue in the quality control of machines such as the heat exchanger of an air-conditioner, because leakage of operating gas directly reduces the performance of machines. The acoustic emission (AE) method is a common way to detect leak of gas, however its application under the environment of mechanical vibration is restricted since most AE detectors are very sensitive to external vibration noise. In order to overcome this problem, we propose a method based on the mode analysis of the Lamb wave. In this method, the dominant Lamb mode and its frequency are found first, and then a proper band-pass filter is used to retain only this frequency component. In this way, we could improve the SNR (signal-to-noise ratio) of AE signal generated by gas leak from the tube even under vibratory environment.