• 한국안전학회지
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• 제15권2호
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• pp.1-7
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• 2000

This paper presents the approach involving fault detection and diagnosis of gears using pattern recognition and Wavelet transform. It describes result of the comparison between KDI (Kullback Discrimination Information) with the nearest neighbor classification rule as one of pattern recognition methods and Wavelet transform to know a way to detect and diagnosis of gear damages experimentally. To model the damages 1) Normal (no defect), 2) one tooth is worn out, 3) All teeth faces are worn out 4) One tooth is broken. The vibration sensor was attached on the bearing housing. This produced the total time history data that is 20 pieces of each condition. We chose the standard data and measure distance between standard and tested data. In Wavelet transform analysis method, the time series data of magnitude in specified frequency (rotary and mesh frequency) were earned. As a result, the monitoring system using Wavelet transform method and KDI with nearest neighbor classification rule successfully detected and classified the damages from the experimental data.

• 한국소음진동공학회논문집
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• 제21권11호
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• pp.1020-1028
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• 2011

As rotating machines play an important role in industrial applications such as aeronautical, naval and automotive industries, many researchers have developed various condition monitoring system and fault diagnosis system by applying artificial neural network. Since using obtained signals without preprocessing as inputs of neural network can decrease performance of fault classification, it is very important to extract significant features of captured signals and to apply suitable features into diagnosis system according to the kinds of obtained signals. Therefore, this paper proposes a neural-network-based fault diagnosis system using AR coefficients as feature vectors by LPC(linear predictive coding) and EIV(errors-in variables) analysis. We extracted feature vectors from sound, vibration and current faulty signals and evaluated the suitability of feature vectors depending on the classification results and training error rates by changing AR order and adding noise. From experimental results, we conclude that classification results using feature vectors by EIV analysis indicate more than 90 % stably for less than 10 orders and noise effect comparing to LPC.

• 한국컴퓨터정보학회논문지
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• 제21권10호
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• pp.125-133
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• 2016

In this paper, we propose a real-time fault monitoring and dual system design of the countdown time-generating system, which is the main component of the mission control system. The countdown time-generating system produces a countdown signal that is distributed to mission control system devices. The stability of the countdown signal is essential for the main launch-related devices because they perform reserved functions based on the countdown time information received from the countdown time-generating system. Therefore, a reliable and fault-tolerant design is required for the countdown time-generating system. To ensure system reliability, component devices should be redundant and faults should be monitored in real time to manage the device changeover from Active mode to Standby mode upon fault detection. In addition, designing different methods for mode changeover based on fault classification is necessary for appropriate changeover. This study presents a real-time fault monitoring and changeover system, which is based on the dual system design of countdown time-generating devices, as well as experiment on real-time fault monitoring and changeover based on fault inputs.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 제39회 하계학술대회
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• pp.263-264
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• 2008

A method for high impedance fault(HIF) detection based on wavelet transform is presented in this paper. HIF is detected and classified by obtaining the energy distribution curve from the wavelet coefficients at each level. The energy distribution of each transient disturbance has unique deviation from sinusoidal wave in particular energy level, which is adopted to provide reliable classification of the type of transient.

• International Journal of Control, Automation, and Systems
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• 제4권3호
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• pp.365-371
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• 2006

This paper presents an algorithm based on a combination of Discrete Wavelet Transforms and neural networks for detection and classification of internal faults in a two-winding three-phase transformer. Fault conditions of the transformer are simulated using ATP/EMTP in order to obtain current signals. The training process for the neural network and fault diagnosis decision are implemented using toolboxes on MATLAB/Simulink. Various cases and fault types based on Thailand electricity transmission and distribution systems are studied to verify the validity of the algorithm. It is found that the proposed method gives a satisfactory accuracy, and will be particularly useful in a development of a modern differential relay for a transformer protection scheme.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 ICEIC The International Conference on Electronics Informations and Communications
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• pp.371-375
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• 2004

Fault diagnosis is one of the most studied problems in process engineering. Recently, great research interest has been devoted to approaches that use classification methods to detect faults. This paper presents an application of a newly developed classification method - support vector machines - for fault diagnosis in an industrial case. A real set of operation data of a motor pump was used to train and test the support vector machines. The experiment results show that the support vector machines give higher correct detection rate of faults in comparison to rule-based diagnostics. In addition, the studied method can work with fewer training instances, what is important for online diagnostics.

• 반도체디스플레이기술학회지
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• 제9권4호
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• pp.93-97
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• 2010

Fast incipient fault diagnosis is becoming one of the key requirements for economical and optimal process operation management in high-tech industries. Artificial neural networks have been used to detect faults for a number of years and shown to be highly successful in this application area. This paper presents a novel test technique for fault detection and classification for module process of TFT-LCD manufacture using support vector machines (SVMs). In order to evaluate SVMs, this paper examines the performance of the proposed method by comparing it with that of multilayer perception, one of the artificial neural network techniques, based on real benchmarking data.

• 한국주조공학회지
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• 제42권6호
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• pp.369-376
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• 2022

다이캐스팅 공정은 다양한 산업군의 인프라 역할을 수행하는 중요한 공정이지만, 높은 불량률로 인하여 관련 기업들의 수익성 및 생산성의 한계가 있는 상황이다. 이를 타개하기 위하여, 본 연구에서는 다이캐스팅 공정의 불량 검출을 위한 산업인공지능 기반 모듈을 구성하였다. 개발된 불량 검출 모듈은 제공되는 데이터의 특징에 따라서 3단계로 동작되는 모델로 구성된다. 1단계 모델은 비지도학습 기반 이상 검출을 진행하며, 레이블이 없는 데이터셋을 대상으로 작동한다. 2단계 모델은 반지도학습 기반으로 이상 검출을 진행하며, 양품 데이터의 레이블만 존재하는 데이터셋을 대상으로 작동하며, 3단계 모델은 소수의 불량 데이터가 제공된 상황의 지도학습 모델을 기반으로 작동한다. 개발된 모델은 실제 다이캐스팅 양품 데이터를 바탕으로 96% 이상의 우수한 양품 검출 성능을 보였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1989년도 추계학술대회 논문집 학회본부
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• pp.372-375
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• 1989

In this paper, a new FTCS with the ability to perform original control objective without considerable loss of control performance in the face of any fault is proposed. The FTCS is composed of two interacting units, Adaptive Controller Unit and Fault Detection/Classification, where ACU performs primary control objective with basic process information(I/O) and environmental information fed by FDU and where FDU detect and classify faults and make decision on remidial action by the use of information provided by ACU.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2011년도 제42회 하계학술대회
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• pp.774-775
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• 2011

Recently, by increasing of devices which are sensitive to power quality, microprocessor and power electronics, the deterioration of power quality is accelerated. Accordingly, various methods for analysis, detection, compensation and countermeasure on power quality are proposed. For this, the study of distinction of various disturbances and fault which is occurred in power system is necessary to prior. Therefore, in this paper, the classification algorithm among steady state and single line to ground fault which is commonly occurred fault is proposed using S-Transform.