• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.127.2-127
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• 2001

A transformer is one of the most important elements in the power network. Transformer faults could cause costly repairs and be dangerous to personnel. To avoid this, its reliable operation has great significance and, therefore, the diagnosis system of the transformer is necessitated. The dissolved gas-in-oil analysis (DGA) is the worldwide popular method of detecting faults such as a hot spot or partial discharges inside the transformer. DGA, however, is not a reliable technique to identify aging phenomena and mechanical faults including insulation failure, inter-turn short, etc. To overcome the drawbacks of DGA, the transfer function method is used to identify effectively these kinds of the mechanical faults. The transformer has a unique transfer function independent of the shape of the input waveform, which can be evaluated through sweep test. This transfer function changes by winding ...

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.110-115
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• 2004

The hybrid fault diagnosis method based on a combination of the signed digraph (SDG) and the partial least-squares (PLS) has the advantage of improving the diagnosis resolution, accuracy and reliability, compared to those of previous qualitative methods, and of enhancing the ability to diagnose multiple fault. In this study, the method is applied for the multiple fault diagnosis of the Tennessee Eastman challenge process, which is a realistic industrial process for evaluating process contol and monitoring methods. The process is decomposed using the local qualitative relationships of each measured variable. Dynamic PLS (DPLS) model is built to estimate each measured variable, which is then compared with the estimated value in order to diagnose the fault. Through case studies of 15 single faults and 44 double faults, the proposed method demonstrated a good diagnosis capability compared with previous statistical methods.

• 설비공학논문집
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• 제16권9호
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• pp.872-880
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• 2004

The fault detection and diagnosis technology may be applied in order to decrease the energy consumption and the maintenance cost of the air-conditioning system. In this study, an air handling unit fault test apparatus was built and fault diagnosis algorithms were applied to diagnose various faults of an air handling unit. Test results showed the good diagnosis for applied faults. Therefore, these algorithms may be effectively used to develope the real time fault detection and diagnosis system for the air handling unit.

• 제어로봇시스템학회논문지
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• 제14권10호
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• pp.983-990
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• 2008

This paper presents the fault detection and diagnosis(FDD) algorithm to enhance reliability of a longitudinal controller for an autonomous All-Terrain Vehicle(ATV). The FDD is designed to monitor and identify faults which may occur in distributed hardware used for longitudinal control, e.g., DSPs, CAN, sensors, and actuators. The proposed FDD is an integrated approach of decentralized and centralized FDD. While the former is processed in a DSP and suitable to detect faults in a single hardware, it is sensitive to noise and disturbance. On the other hand, the latter is performed via communication and it detects and diagnoses faults through analyzing concurrent performances of multiple hardware modules, but it is limited to isolate faults specifically in terms of components in the single hardware. To compensate for disadvantages of each FDD approach, two layered structure including both decentralized and centralized FDD is proposed and it allows us to make more robust fault detection and more specific fault isolation. The effectiveness of the proposed method will be validated experimentally.

• 한국생산제조학회지
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• 제22권3호
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• pp.343-348
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• 2013

In line with the advances in factory automation, various pieces of equipment are now operated in batch processes controlled by computers. However, many kinds of faults can occur in complicated and large systems, which can result in low productivity and economic loss. The reliability and safety of systems have been studied because of the difficulty of determining the severity and location of faults. Therefore, it is necessary to detect and diagnose such faults in order to guarantee the reliability and safety of the equipment. In this paper, a diagnosis method for the ball bearings of a hydraulic pump is applied using a vibration signal for the maintenance of injection molding equipment. The bearings' defects are selected as a main failure mode through a failure mode and effect analysis (FMEA). Usually, there are nonlinear and impulse components of vibration in a ball bearing with faults. For the effective fault diagnosis of a ball bearing, nonlinear diagnostic methods and time-frequency analysis are applied, in addition to the methods currently used, such as power spectrum, time series analysis, and statistical methods. As a result of this study, a failure diagnosis system is provided that is useful even for non-experts. This is a condition-based method that makes it possible to resolve problems in a timely and economical way, in contrast to the prior method, which required regular but wasteful maintenance based on the experience of expensive external experts.

• 한국통신학회논문지
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• 제31권11C호
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• pp.1044-1051
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• 2006

시스템-레벨 진단 알고리즘은 결함의 개수가 t개를 초과하지 않는다는 t-진단가능 시스템의 특성을 이용한다. 기존의 진단 알고리즘으로 대형 멀티프로세서 시스템에서의 보다 많은 수의 결함을 처리하기에는 한계가 있다. Somani와 Peleg은 진단의 정확 여부를 판단할 수 없는 충분히 작은 개수의 노드가 존재한다는 것을 허용으로써 결함의 갯수가 t개를 초과할 경우에도 시스템을 진단하는 t/k-diagnosable 시스템을 제안하였다. 본 논문에서는 t/k-diagnosable 시스템을 이용한 적응적 방법에 의한 하이퍼큐브 진단 알고리즘을 제안한다. 결함의 개수가 t개를 초과하는 경우에 대하여, k개의 부정확한 진단을 허용한다. 성능 실험 결과 제안 알고리즘은 HADA알고리즘보다 우수함을 보여 주었다. 제안한 알고리즘은 RGC-Ring들의 신드롬을 분석하여 기존의 HADA/IHADA의 기법보다 테스트 라운드를 줄이는 보다 개선된 방법을 제안하였다. 또한 제안 알고리즘은 HYP-DIAG알고리즘과의 성능 비교에서도 유사한 결과를 보여 준다.

• 융합신호처리학회논문지
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• 제10권1호
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• pp.86-92
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• 2009

유도 전동기의 사용이 증가함에 따라 유도전동기의 고장은 산업 사회에 커다란 피해를 끼치게 되었다. 그렇기 때문에 유도 전동기의 고장을 찾아내는 것은 매우 중요한 문제로 부각되었다. 하지만 그 중에서도 문제점은 유도전동기의 고장은 종종 오랜 시간에 걸쳐 진행된다는 것이다. 그것은 빠른 진단이 매우 중요하다는 것을 뜻한다. 이에 대해 많은 연구가 진행되어 왔으며 가장 일반적으로 쓰이는 고장 진단 방법은 진동 센서를 이용한 전동기의 기계적 고장을 찾는 방법이다. 하지만 이 방법은 신뢰도가 높은 검증 방법임에도 불구하고 높은 시스템 가격과 활용의 어려움으로 인해 새로운 방법들이 시도가 되었다. 이 논문은 시스템을 기반으로 웨이블릿 변환을 이용한 유도전동기의 고장 진단 기술을 구현하는 것을 보여주며 윈도우즈 기반 C++을 이용하여 고장인지 아닌지를 결정하는 알고리즘으로 구성되어 있다. 전체 시스템은 전류 데이터 수집 보드와 PC를 이용한 신경망 알고리즘으로 실시간으로 수행 될 것이다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1727-1731
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• 2003

In this paper we proposed backward fuzzy Petri-net to diagnoses faults in power systems by using the time sequence information of protection system. As the complexity of power systems increases, especially in the case of multiple faults or incorrect operation of protective devices, fault diagnosis requires new and systematic methods to the reasoning process, which improves both its accuracy and its efficiency. The fuzzy Petri-net models of protection system are composed of the operating process of protective devices and the fault diagnosis process. Fault diagnosis model, which makes use of the nature of fuzzy Petri-net, is developed to overcome the drawbacks of methods that depend on operator knowledge. The proposed method can reduce processing time and increase accuracy when compared with the traditional methods. And also this method covers online processing of real-time data from SCADA (Supervisory Control and Data Acquisition)

• 한국해양공학회지
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• 제15권4호
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• pp.101-107
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• 2001

This paper describes a fault diagnosis method by a T-invariance of Petri Net (PN). First, a complicated fault system with some failure is modeled into a PN graphic expressions. Next, the PN model is analyzed by using the backward chaining of T-invariance to find out causes of the faults. In this step, an inter-node search technique which is suggested in this paper is applied for reducing searching area in the fault system. Also, a novel idea to compose incidence matrices which have different dimension each other in PN model is proposed. As the new knowledges which is discovered newly about faults can be added easily to conventional systems, the diagnosis system will be very flexible. Finally, the proposed method is applied to the automobile fault diagnosis system to confirm the validity of the method.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.1109-1114
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• 1992

Modern chemical processes are becoming more complicated. The sophisticated chemical processes have needed the fault diagnosis pxpert systems that can detect and diagnose the fault diagnosis expert systems that can detect and diagnose the faults of some processes and give and advice to the operator in the event of process faults. We present the Time-Delay Neural Network(TDNN) approach for on-line fautl diagnosis. The on-line fault diagnosis system finds the exact origin of the fault of which the symptom is propagated continuously with time. The proposed method has been applied to a pilot distillation column to show the merits and applicability of the TDNN.