• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2756-2758
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• 2005

본 논문에서는 제어시스템의 신뢰성 향상을 위한 고장허용제어계를 실현하기 위하여, 함수관측자를 도입한 장치고장검출(IFD)기법 및 고장보상알고리즘을 제안하고, 이를 전형적인 불안정 비선형시스템인 역진자제어계에 적용하였다. 제안된 IFDS는 기존의 다중관측자기법과 동일한 구조를 가지지만 최저차 설계가 가능한 미지입력 함수관측자를 채택함으로서 알고리즘의 단순화를 가하였을 뿐 아니라 고장량의 추정 등 다양한 설계목적을 고려하여 설계될 수 있다는 특징을 가진다. 제안된 고장 검출식별 기법의 제안된 역진자제어계를 위한 제어기, 고장검출식별 및 보상알고리즘을 모두 포함한 상태에서 고장허용제어의 가능성을 검증하였다.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.243-245
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• 2005

Condition monitoring is desirable for increasing machinery availability, reducing consequential damage, and improving operational efficiency. In this paper, a model-based method using neural network modeling of induction noter in vibration spectra is proposed for machine fault detection and diagnosis. The short-time Fourier transform (STFT) is used to process the quasi-steady vibration signals to continuous spectra so that the neural network model can be trained with vibration spectra. And the faults are detected from changes in the expectation of vibration spectra modeling error. The effectiveness of the proposed method is demonstrated through experimental results.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.953-958
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• 1993

We develop a neural network control algorithm for the ACS (Advanced Control System). The ACS is an extended version of the DCS (Distributed Control System) to which functions of fault detection and diagnosis and advanced control algorithms are added such as neural networks, fuzzy logics, and so on. In spite of its usefulness proven by computer simulations, the neural network control algorithm, as far as we know, has no tool which makes it applicable to process control. It is necessary that the neural network controller should be turned into the function code for its application to the ACS. So we develop a general method to implement the neural network control systems for the ACS. By simulations using the simulator for the boiler of 'Seoul fire power plant unit 4', the methodology proposed in this paper is validated to have the applicability to process control.

• The Transactions of the Korea Information Processing Society
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• v.7 no.8S
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• pp.2620-2630
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• 2000

본 논문에서는 규칙 기반 추론 기법을 이용하여 LAN 상의 지연 장애 검출 알고리즘과 장애를 유발시킨 호스트에 대한 위치 확인 알고리즘 및 장애 원인 분석 알고리즘을 제시하고자 한다. 이를 위해 지연 장애 검출 모델과 RBR 기반 장애 검출 규칙 모델을 제시하고 있다. 또한 충돌율 검출 규칙과 이용률 검출 규칙을 적용하여 지연 장애 검출 알고리즘을 설계하였고, 최대 패킷 출력 호스트 파악 규칙을 적용하여 장애 위치 탐색 알고리즘을 설계하였다. 그리고 패킷 유형 분석 규칙과 장애 원인 파악 규칙을 적용하여 장애 원인 분석 알고리즘을 설계하였다. 이를 통하여 LAN 상의 지연 장애를 검출하고 진단하는 기법을 제시하고자 한다. 이와 같이 제시한 지연 장애 검출 및 진단 기법을 실제 네트워크 환경에 직접 적용시켜 봄으로써 본 논문에서 제시한 장애 검출 및 진단 기법의 정확성과 적용성을 확인하였다. 이러한 기법은 네트워크 관리자가 LAN 상의 장애를 진단하고 원인을 해결하는데 큰 도움을 줄 것으로 기대된다.

• Journal of IKEEE
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• v.6 no.1 s.10
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• pp.102-110
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• 2002

An effective scheme to detect and diagnose multiple failures in a dynamic system is described for the case where the measurement noise is correlated sequentially in time. It is based on the modified interacting multiple model (MIMM) estimation algorithm in which a generalized decorrelation process is developed by employing the autoregressive (AR) model for the colored noise and applying measurement difference method.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.7
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• pp.868-875
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• 1999

In this paper, digital motor control center using protection relay is developed in order to protect power systems by means of timely fault detection and diagnosis during operation for induction motor which have various load environments and capacities in power systems. Digital motor control center is employed by power supervisory control systems without separate remote terminal unit and transducers adding communicational ability. Also we develope a maximum demand controller to control the load effectively at peak status and a power factor controller to minimize real power losses and improve the power factor. Therefore, when using the developed controller, real time computation is possible by loading DSP in hardware and applying real-time kernel which can convert each algorithm to task module.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.1102-1107
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• 2000

The signals that can be obtained from a rotating machine often convey the information of machine. For example, if the machine under investigation has faults, then we can measure the signal which has a pulse train, embedded in noise. Therefore the ability to detect the fault signal in noise determines the degree of diagnosis level of rotating machine. In this paper, minimum variance cepstrum (MV cepstrum), which can easily detect impulse in noise, has been applied to detect the type of faults of ball bearing system. To test the performance of this technique, experiment has been performed for ball bearing elements that have man made faults. Results show that minimum variance cepstrum can easily detect the periodicity due to faults.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.7
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• pp.98-105
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• 2000

In this study, we developed rotating machine vibration condition monitoring system based upon Windows NT and DSP Board. Developed system includes signal analysis module, trend monitoring and simple diagnosis using threshold value. Trend analysis and report generation are offered with database management tool which was developed in MS-ACCESS environment. Post-processor, based upon Matlab, is developed for vibration signal analysis and fault detection using statistical pattern recognition scheme based upon Bayes discrimination rule and neural networks. Concerning to Bayes discrimination rule, the developed system contains the linear discrimination rule with common covariance matrices and the quadratic discrimination rule under different covariance matrices. Also the system contains k-nearest neighbor method to directly estimate a posterior probability of each class. The result of case studies with the data acquired from Pyung-tak LNG pump and experimental setup show that the system developed in this research is very effective and useful.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.12
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• pp.8-14
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• 2015

An effective way to ensure that LEDs produce wanted light output is to use a current driving topology, because the brightness of LEDs is directly related to their current. However, this topology may lead to the lifetime shortening of a illumination system because over-currents may flow through non-damaged LEDs in case some LEDs are damaged. This paper presents an adaptive current control circuits for LEDs, which protect LEDs in a good state by limiting the driving currents according to the number of damaged ones. The proposed control circuits consist of a simple constant-current driver and a micro-controller which monitors the voltage of LED array without any auxiliary current sensors for fault diagnosis. And the driving current is automatically controlled into 6-levels according to the number of failures.

• Industrial Engineering and Management Systems
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• v.14 no.4
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• pp.392-400
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• 2015

Monitoring of plasma etch processes for fault detection is one of the hallmark procedures in semiconductor manufacturing. Optical emission spectroscopy (OES) has been considered as a gold standard for modeling plasma etching processes for on-line diagnosis and monitoring. However, statistical quantitative methods for processing the OES data are still lacking. There is an urgent need for a statistical quantitative method to deal with high-dimensional OES data for improving the quality of etched wafers. Therefore, we propose a robust relevance vector machine (RRVM) for regression with statistical quantitative features for modeling etch rate and uniformity in plasma etch processes by using OES data. For effectively dealing with the OES data complexity, we identify seven statistical features for extraction from raw OES data by reducing the data dimensionality. The experimental results demonstrate that the proposed approach is more suitable for high-accuracy monitoring of plasma etch responses obtained from OES.