• Nuclear Engineering and Technology
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• 제36권3호
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• pp.210-218
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• 2004

In the interest of nuclear power plant safety, a self-diagnostic monitoring system (SDMS) is needed to monitor defects in safety-related components. An air-operated valve (AOV) is one of the components to be monitored since the failure of its operation could potentially have catastrophic consequences. In this paper, a model of the AOV is developed with the parameters that affect the operational characteristics. The model is useful for both understanding the operation and correlating parameters and defects. Various defects are introduced in the experiments to construct a fault library, which will be used in a pattern recognition approach. Finally, the validity of the fault library is examined.

• 한국가스학회지
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• 제8권2호
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• pp.28-34
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• 2004

Enhanced methodologies for process diagnosis and abnormal situation management have been developed for the last two decades. However, there is no single method that always shows better performance over all kinds of diagnostic problems. In this paper, a framework of message-passing, cooperative, intelligent diagnostic agents is presented for improved on-line fault diagnosis through cooperative problem solving of different expertise. A group of diagnostic agents in charge of different process functional perform local diagnoses in parallel; exchange related information with other diagnostic agents; and cooperatively solve the global diagnostic problem of the whole process plant or business units just like human experts would do. For their better understanding, sharing and exchanging of process knowledge and information, we also suggest a way of remodeling processes and protocols, taking into account semantic abstracts of process information and data. The benefits of the suggested multi-agents-based approach are demonstrated by the implementations for solving the diagnostic problems of various chemical processes.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 1995년도 춘계공동학술대회논문집; 전남대학교; 28-29 Apr. 1995
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• pp.387-394
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• 1995

With respect to the effectiveness of types of knowledge on human diagnostic performance, the results of several experiments claimed that training with diagnostic rules (procedural knowledge) is more effective than training that provides theoretical knowledge (principle knowledge). However, we usually have the idea that understanding the principles of system dynamics is necessary for diagnosis in some situations. In this study, we pointed out some problems in the previous experiments that force to reinterpret their experimental conclusions. Accordingly, we conducted an experiment to reinvestigate the value of theoretical knowledge in two problem situations. A simulator system, which is named DLD, that is to diagnose an electronic device was created for this purpose. It is a context-free digital logic circuit which includes forty-one gates of three basic types. Our experiment investigated the marginal effects of theoretical knowledge over common diagnostic rules. The experimental results showed that the effectiveness of the instruction in theoretical knowledge is dependent on the complexity of diagnostic situations. This adds up an experimental evidence against the presumed ineffectiveness of theoretical knowledge and forward reasoning in fault diagnosis. Furthermore, the result suggests the source of the use of theoretical knowledge.

• International Journal of Precision Engineering and Manufacturing
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• 제8권3호
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• pp.54-59
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• 2007

It is widely recognized that increasing the accuracy and diversity of rotating machinery necessitates an appropriate diagnostic technique and maintenance system. Until now, operators have monitored machinery using their senses or by analyzing simple changes to root mean square output values. We developed an expert diagnostic system that uses fuzzy inference to expertly assess the condition of a machine and allow operators to make accurate judgments. This paper describes the hardware and software of the expert diagnostic system. An assessment of the diagnostic performance for five fault phenomena typically found in pumps is also described.

• 한국지능시스템학회논문지
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• 제14권3호
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• pp.267-272
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• 2004

본 논문에서는 농산물 저온저장시설의 냉동기용 지능형 진단 시스템을 제안한다. 제안하는 방법은 냉동기에 고장이 발생하면 냉동기 주요배관의 온도가 변화하는 성질을 이용하였다. 주요배관의 온도와 고장부위의 관계를 신경회로망을 이용하여 학습함으로써 고장상태를 추론할 수 있다. 제안된 방법을 구현하기 위하여 온도계측시스템 및 진단프로그램을 개발하였다. 개발된 시스템을 현장에 적용 실험하여 제안된 방법의 유용성을 검증하였다.

• 한국통신학회:학술대회논문집
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• 한국통신학회 1983년도 추계학술발표회논문집
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• pp.112-117
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• 1983

In this paper, a functional m-redundant system, which is me-fault tolerant, is defined based on the graph-theory. This system is designed to be t fault-diagnosable by comparing its unit's outcomes without additive test functions, and so, the system down for diagnosis is not needed. the diagnostic model for this system is presented and this effectively uses system's redundancy. It is shown that this model can be converted into Preparata's model. Thus, the diagnostic characteristics of a functional m-redundant system is analyzed by the methods originated by Preparata et al..

• Journal of Electrical Engineering and Technology
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• 제11권1호
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• pp.29-37
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• 2016

Fast and accurate fault diagnosis of the position sensor is of great significance to ensure the reliability as well as sensor fault tolerant operation of the Switched Reluctance Wind Generator (SRWG). This paper presents a fault diagnostic scheme for a SRWG based on the residual between the estimated rotor position and the actual output of the position sensor. Extreme Learning Machine (ELM), which could build a nonlinear mapping among flux linkage, current and rotor position, is utilized to design an assembled estimator for the rotor position detection. The data for building the ELM based assembled position estimator is derived from the magnetization curves which are obtained from Finite Element Analysis (FEA) of an SRWG with the structure of 8 stator poles and 6 rotor poles. The effectiveness and accuracy of the proposed fault diagnosis method are verified by simulation at various operating conditions. The results provide a feasible theoretical and technical basis for the effective condition monitoring and predictive maintenance of SRWG.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권3호
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• pp.178-184
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• 2009

An efficient fault diagnosis system is needed for industry because it can optimize the resources management and improve the performance of the system. In this study, a fault diagnostic system is proposed for rotating machine using wavelet packet transform (WPT) and elman neural network (ENN) techniques. In most fault diagnosis for mechanical systems, WPT is a well-known signal processing technique for fault detection and identification. In previous work, WPT can improve the continuous wavelet transform (CWT) used over a longer computing time and huge operand. It can also solve the frequency-band disagreement by discrete wavelet transform (DWT) only breaking up the approximation version. In the experimental work, the extracted features from the WPT are used as inputs in an Elman neural network. The results show that the scheme can reliably diagnose four different conditions and can be considered as an improvement of previous works in this field.

• Nuclear Engineering and Technology
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• 제43권5호
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• pp.437-446
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• 2011

Siting criteria for nuclear power plants require that faults be characterized as to their potential for generating earthquakes, or that the absence of the potential for these occurrences be demonstrated. Because the definition of active faults in Korea has been applied by the deterministic method, which depends on the numerical age of fault movement, the possibility of inherent uncertainties exists in determining the maximum earthquake from the fault sources for seismic design. In an attempt to overcome these problems this study suggests new criteria and a probabilistic quantitative diagnostic procedure that could estimate whether a fault is capable of generating earthquakes in the near future.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2008년 영문 학술대회
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• pp.16-21
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• 2008

Aero-engine, as one kind of rotating machinery with complex structure and high rotating speed, has complicated vibration faults. Therefore, condition monitoring and fault diagnosis system is very important for airplane security. In this paper, a vibration data acquisition and intelligent fault diagnosis system is introduced. First, the vibration data acquisition part is described in detail. This part consists of hardware acquisition modules and software analysis modules which can realize real-time data acquisition and analysis, off-line data analysis, trend analysis, fault simulation and graphical result display. The acquisition vibration data are prepared for the following intelligent fault diagnosis. Secondly, two advanced artificial intelligent(AI) methods, mapping-based and rule-based, are discussed. One is artificial neural network(ANN) which is an ideal tool for aero-engine fault diagnosis and has strong ability to learn complex nonlinear functions. The other is data mining, another AI method, has advantages of discovering knowledge from massive data and automatically extracting diagnostic rules. Thirdly, lots of historical data are used for training the ANN and extracting rules by data mining. Then, real-time data are input into the trained ANN for mapping-based fault diagnosis. At the same time, extracted rules are revised by expert experience and used for rule-based fault diagnosis. From the results of the experiments, the conclusion is obvious that both the two AI methods are effective on aero-engine vibration fault diagnosis, while each of them has its individual quality. The whole system can be developed in local vibration monitoring and real-time fault diagnosis for aero-engine.