• 대한의용생체공학회:의공학회지
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• 제16권1호
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• pp.95-100
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• 1995

본 논문에서는 발성기관인 후두(glottis)의 기능을 평가하기 위하여 후두신호의 주파수 변동과 진폭변동의 영향을 분석하였다. 정상인의 EGG(electroglottograph)신호는 매개변수 방법을 이용하여 최적의 차수가 9차인 자귀회귀형 모델로 설명할 수 있으며, 이것은 전달함수를 결정함으로써 해석이 가능하다. EGG신호의 진푹과 주파수의 변동은 변복조방식의 4전극 EGG 시스템으로 얻어지며, 후두기능 상태식별을 목적으로 개발하였다. EGG를 이용하여 후두의 닫힘구간과 열림구간을 계산함으로써 비정상 EGG신호는 비주기적이며 불안정한 상태라는 것을 구별할 수 있었다. 정상과 비정상의 피검자들을 구분할 수 있는 파라미터인 주파수 변동은 정상인의 m$\pm$0.5*sd이고, 진폭변동은 m$\pm$2*sd가 각각 되었다. 또한 EGG신호를 Hillbert 변환함으로써 얻어진 궤적의 패턴을 이용하여 정상과 비정상의 후두기능상태를 효과적으로 분류할 수 있었다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.600-602
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• 2006

Some abnormal signals diagnostics and analysis through an important equivalent circuits modeling for passive elements under severe accident conditions have been performed. Unlike the design basis accidents, there are inherently some uncertainties in the instrumentation capabilities under the accident conditions. So, the circuit simulation analysis and diagnosis methods are used to assess instruments in detail when they give apparently abnormal readings as an accident alternative method. The simulations can be useful to investigate what the signal and circuit characteristics would be when similar to a variety of symptoms that can result from the environmental conditions such as high temperature, humidity, and pressure condition. In this paper, a new simulator through an analysis of the important equivalent circuits modeling under temperature accident conditions has been designed, the designed simulator is composed of the LabVIEW code as a main tool and the out-put file of the Multi-SIM code as an engine tool is exported to in-put file of the LabVIEW code. The procedure for the simulator design was divided into two design steps, of which the first step was the diagnosis method, the second step was the circuit simulator for the signal processing tool. It has three main functions which are a signal processing tool, an accident management tool, and an additional guide from the initial screen. This simulator should be possible that it could be applied a output signal analysis to some transient signal by variation of the resistance passive elements in the R-L-C equivalent circuit modeling under various degraded conditions in NPPs.

• International Journal of Precision Engineering and Manufacturing
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• 제7권3호
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• pp.14-17
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• 2006

To diagnosis abnormal compressor conditions in an air-conditioner, the acoustic emission (AE) signal, which is derived from wear condition, compressed air, and assembly error, was analyzed experimentally. Burst and continuous type AE signals resulted from metal contact and compressed air, and the raw AE signal of compressors was acquired in the production line. After extracting samples using waveforms, the Early Life Test (ELT) was conducted and the waveform was classified as normal or abnormal. Efficient parameters in the waveform pattern were investigated in time and frequency domains and a diagnosis algorithm for air-conditioners using Neural Network estimation is suggested.

• 소음진동
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• 제10권2호
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• pp.331-337
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• 2000

The necessity of diagnosis of the rotating machinery which is widely used in the industry is increasing. Many research has been conducted to manipulate field vibration signal data for diagnosing the fault of designated machinery. As the pattern recognition tool of that signal, neural network which use usually back-propagation algorithm was used in the diagnosis of rotating machinery. In this paper, self-organizing feature map(SOFM) which is unsupervised learning algorithm is used in the abnormal defect diagnosis of rotating machinery and then learning vector quantization(LVQ) which is supervised learning algorithm is used to improve the quality of the classifier decision regions.

• Journal of Advanced Marine Engineering and Technology
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• 제30권2호
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• pp.247-252
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• 2006

Almost ship monitoring systems are event driven alarm system which warn only when the measurement value is over or under set point. These kinds of system cannot warn until signal is growing to abnormal state that the signal is over or under the set point. therefore cannot play a role for preventive maintenance system. This paper proposes fault diagnosis method which is able to diagnose and forecast the fault from present operating condition by analyzing monitored signals with present ship monitoring system without any additional sensors. By analyzing the data with high correlation coefficient(CC), correlation level of interactive data can be defined. Knowledge base of abnormal detection can be built by referring level of CC(Fault Detection CC. FDCC) to detect abnormal data among monitored data from monitoring system and knowledge base of diagnosis built by referring CC among interactive data for related machine each other to diagnose fault part.

• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2005년도 전기학술대회논문집
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• pp.281-286
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• 2005

Almost ship monitoring systems are event driven alarm system which warn only when the measurement value is over or under set point. These kinds of system cannot warn while signal is growing to abnormal state until the signal is over or under the set point and cannot play a role for preventive maintenance system. This paper proposes fault diagnosis method which is able to diagnose and forecast the fault from present operating condition by analyzing monitored signals with present ship monitoring system without additional sensors. By analyzing this data having high correlation coefficient(CC), correlation level of interactive data can be understood. Knowledge base of abnormal detection can be built by referring level of CC(Fault Detection CC, FDCC) to detect abnormal data among monitored data from monitoring system and knowledge base of diagnosis built by referring CC among interactive data for related machine each other to diagnose fault part.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권4호
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• pp.293-299
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• 2004

This paper is about the extraction of basis function for ECG signal processing. In the first step, it is assumed that ECG signal consists of linearly mixed independent source signals. 12 channel ECG signals, which were sampled at 600sps, were used and the basis function, which can separate and detect source signals - QRS complex, P and T waves, - was found by applying the fast fixed point algorithm, which is one of learning algorithms in independent component analysis(ICA). The possibilities of significant point detection and classification of normal and abnormal ECG, using the basis function, were suggested. Finally, the proposed method showed that it could overcome the difficulty in separating specific frequency in ECG signal processing by wavelet transform. And, it was found that independent component analysis(ICA) could be applied to ECG signal processing for detection of significant points and classification of abnormal beats.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2000년도 추계학술대회논문집
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• pp.796-801
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• 2000

The monitoring and diagnostics of the rotating machinery have been received considerable attention for many years. The objectives are to classify the machinery condition and to find out the cause of abnormal condition. This paper describes a signal classification method for diagnosing the rotating machinery using the artificial neural network and the wavelet transform. In order to extract salient features, the wavelet transform are used from primary noise signals. Since the wavelet transform decomposes raw time-waveform signals into two respective parts in the time space and frequency domain, more and better features can be obtained easier than time-waveform analysis. In the training phase for classification, self-organizing feature map(SOFM) and learning vector quantization(LVQ) are applied, and the accuracies of them are compared with each other. This paper is focused on the development of an advanced signal classifier to automatise the vibration signal pattern recognition. This method is verified by small reciprocating compressors, for refrigerator and normal and abnormal conditions are classified with high flexibility and reliability.

• 대한의용생체공학회:의공학회지
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• 제2권2호
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• pp.83-88
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• 1981

A method of detecting abnormal spikes occuring in the EEG of subjects suffering from epilepsy is studied. The detection scheme is to take the first derivative of EEG and to determine if it exceed some threshold value. This study is focused on the digital signal processing for detecting abnormal spikes using microcomputer.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 춘계학술대회 논문집
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• pp.186-189
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• 1997

To monitor abnormal state of rotary compressor, methods for acquisition and processing of Acoustic Emission(AE) signal are arranged and optimal AE parameter for monitoring is determined. The detecting method of abnormal compressor in real time is suggested and special-purpose minitoring system which can be applied to automatic manufacturing line is developed using one-chip microprocessor in low cost.