• 대한전기학회논문지
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• 제41권4호
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• pp.331-338
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• 1992

The application of Connectionist expert system using neural network to fault diagnosis of power system is presented and compared with rule-based expert system. Also, the merits of Connectionist model using neural network is presented. In this paper, the neural network for fault diagnosis is hierarchically composed by 3 neural network classes. The whole power system is divided into subsystems, the neural networks (Class II) which take charge of each subsystem and the neural network (Class III) which connects subsystems are composed. Every section of power system is classified into one of the typical sections which can be applied with same diagnosis rules, as line-section, bus-section, transformer-section. For each typical section, only one neural network (Class I) is composed. As the proposed model has hierarchical structure, the great reduction of learning structure is achieved. With parallel distributed processing, we show the possibility of on-line fault diagnosis.

• Journal of Mechanical Science and Technology
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• 제20권7호
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• pp.929-940
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• 2006

Early detection and diagnosis of incipient induction machine faults increases machinery availability, reduces consequential damage, and improves operational efficiency. However, fault detection using analytical methods is not always possible because it requires perfect knowledge of a process model. This paper proposes a neural network based expert system for diagnosing problems with induction motors using vibration analysis. The short-time Fourier transform (STFT) is used to process the quasi-steady vibration signals, and the neural network is trained and tested using the vibration spectra. The efficiency of the developed neural network expert system is evaluated. The results show that a neural network expert system can be developed based on vibration measurements acquired on-line from the machine.

• 한국지능시스템학회논문지
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• 제11권3호
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• pp.231-240
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• 2001

Conventional expert systems has been criticized due to its lack of capability to adapt to the changing decision-making environments. In literature, many methods have been proposed to make expert systems more environment-adaptive by incorporating fuzzy logic and neural networks. The objective of this paper is to propose a new approach to building a self-evolving expert system inference mechanism by integrating fuzzy neural network and fuzzy rule extraction technique. The main recipe of our proposed approach is to fuzzify the training data, train them by a fuzzy neural network, extract a set of fuzzy rules from the trained network, organize a knowledge base, and refine the fuzzy rules by applying a pruning algorithm when the decision-making environments are detected to be changed significantly. To prove the validity, we tested our proposed self-evolving expert systems inference mechanism by using the bankruptcy data, and compared its results with the conventional neural network. Non-parametric statistical analysis of the experimental results showed that our proposed approach is valid significantly.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 추계학술대회 논문집
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• pp.852-855
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• 1995

In modern plant lndustry, dignosis system is an essential implement because a human operator cannot check the state of system all the time. The recent facility needs a computer system which is able to replace and extense the function of the human expert. Checking the state of the plant system, the computer system uses signals form sensors attached to the plant systems. But, It is difficult to predict the cause of the failure from the sensing signals. Because the relationship among the signals cannot be easily represented by mathematical models. So expert system based on a fuzzy rule and Neural network method is sugguested. Expert system decide whether aa state of the system is ordinary of failure by the evaluation of the signals. If the state of the system is unstable, expert system preprocess the signals. When fault is occurred in the machine, the expert system dignoses the state of the system and find the cause as a primary tool. If the expert system dose not find the adequate cause successfully, neural network system uses the preprocessed signals as an input and propose a cause of the failure.

• 한국윤활학회:학술대회논문집
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• 한국윤활학회 2002년도 proceedings of the second asia international conference on tribology
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• pp.455-456
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• 2002

Wear is one of the main factors causing breakdown and fault of machine, so ferrography technique analyzing wear particles can be an effective way for condition monitoring and fault diagnosis. On the base of the forward multilayer neural network, a nodes self-deleting neural network model is provided in this paper. This network can itself deletes the nodes to optimize its construction. On the basis of the nodes self-deleting neural network, an intelligent ferrography diagnosis expert system (IFDES) for wear particles recognition and wear diagnosis is described. This intelligent expert system can automatically slim lip knowledge by learning from samples and realize basically the entirely automatic processing from wear particles recognition to wear diagnosis.

• 제어로봇시스템학회논문지
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• 제4권5호
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• pp.651-657
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• 1998

In this study, an intelligent scheduling with hybrid architecture, which integrates expert system and neural network, is proposed. Neural network is trained with the data acquired from simulation model of FMC to obtain the knowledge about the relationship between the state of the FMC and its best dispatching rule. Expert system controls the scheduling of FMC by integrating the output of neural network, the states of FMS, and user input. By applying the hybrid system to a scheduling problem, the human knowledge on scheduling and the generation of non-logical knowledge by machine teaming, can be processed in one scheduler. The computer simulation shows that comparing with MST(Minimum Slack Time), there is a little increment in tardness, 5% growth in flow time. And at breakdown, tardness is not increased by expert system comparing with EDD(Earliest Due Date).

• 한국지반공학회지:지반
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• 제10권3호
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• pp.79-96
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• 1994

인공신경회로망을 이용하여 터널굴착설계를 위한 전문가 시스템 NESTED를 개발하였다. 이를 위하여 지하 암반의 안정성을 평가할 수 있는 신경회로망 모델과 대표적인 암반분류법인 RMR과 Q 시스템 사이의 상관관계를 결정할 수 있는 신경회로망 모델을 사용하였다. 또한 사용된 모델과 전산화된 암반분류법 프로그램이 동일한 사용자 환경을 통해 운용될 수 있도록 통합 시스템을 구성하였다. NESTED에 사용된 신경회로망의 구조는 역전파 학습 알고리즘을 채용한 다층 역전파 신경 회로망이다. 전문가 시스템에 필요한 지식기반을 구축하기 위해 이전의 현장 시공사례로 학습과정을 수행함으로써 불완전하거나 오류가 포함된 정보를 처리할 수 있는 공학 데이터베이스를 개발하였다. 일련의 실험을 통해 전문가 시스템을 현장사례에 적용해보고 여기서 출력된 결과를 문헌에 보고된 자료와 비교하였다. 이 결과 암반의 파괴거동을 추정하고 이에 따른 보강시기의 변화를 정확히 예측하는 신경회로망의 추론능력을 확인할 수 있었다. 이처럼 본 연구를 통해 개발된 신경회로망 전문가 시스템을 암반터널에 적용할 경우 부족한 지질자료에 대해 합리적인 기준을 제공하고 터널의 예비설계에 필요한 보강설계를 제시할 수 있었다.

• 한국농공학회지
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• 제36권1호
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• pp.95-102
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• 1994

Since parameters influencing the indoor environment of livestock building interrelate so complicatedly, it is of great difficulty to identify the exact cause of environmental problems in a livestock building. Therefore, the approaches for the problem solving based on experience not numerical calculation will be helpful to the management of livestock building This study was attempt to develop the decision supporting system to diagnose environmen- tal problems in a livestock building based on an expert system and a neural network. HClips$^3$), attaching the Hangeul user interface to Clips which is known as a powerful shell for develop- ing expert system, was used. The multilayer perceptron consisting of 4 layers including back propagation learning algorithm was adpoted, which was rapidly converged within the allowable range at 50,000 learning sweeps. The expert system and neural network seemed to work well for this specific application, providing proper suggestions for some environmental problems: particularly, the neural net- work trained by an environmental problem and its corresponding answer with certainty factor, produced the same results as those by expert system.

• 대한기계학회논문집
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• 제18권7호
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• pp.1881-1897
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• 1994

This paper presents a neural network-based computational scheme to generate the optimized robotic assembly sequence for an assembly product consisting of a number of parts. An assembly sequence is considered to be optimal when it meets a number of conditions : it must satisfy assembly constraints, keep the stability of in-process subassemblies, and minimize assembly cost. To derive such an optimal sequence, we propose a scheme using both the Hopfield neural network and the expert system. Based upon the inferred precedence constraints and the assembly costs from the expert system, we derive the evolution equation of the network. To illustrate the suitability of the proposed scheme, a case study is presented for industrial product of an electrical relay. The result is compared with that obtained from the expert system.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 추계학술대회 논문집
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• pp.1110-1113
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• 1995

This study presents the new method which can transfer the expert's skill to deburring robot through neural network. The expert's skill is expressed as associationmapping between the characteristics of the burr and human expert's action. Under the fundamental idea that the state of the deburring processcan be extracted via the visual sense of the human,we employ vision system for the perception and identification of the changing burr. Form the demonstration of human experts, force data are measured. Finally the characteristics of the burr and coressponding force are associated by the neural network which is trained through many demonstrations. The proposed method is verified in the deburring process of welding burr.