• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.4
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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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• v.20 no.7
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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.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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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.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2002.10b
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.5
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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).

• Geotechnical Engineering
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• v.10 no.3
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• pp.79-96
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• 1994

A tunnel design expert system entitled NESTED is developed using the artificial neural network. The expert system includes three neural network computer models designed for the stability assessment of underground openings and the estimation of correlation between the RMR and Q systems. The expert system consists of the three models and the computerized rock mass classification programs that could be driven under the same user interface. As the structure of the neural network, a multi -layer neural network which adopts an or ror back-propagation learning algorithm is used. To set up its knowledge base from the prior case histories, an engineering database which can control the incomplete and erroneous information by learning process is developed. A series of experiments comparing the results of the neural network with the actual field observations have demonstrated the inferring capabilities of the neural network to identify the possible failure modes and the support timing. The neural network expert system thus complements the incomplete geological data and provides suitable support recommendations for preliminary design of tunnels in rock masses.

• Magazine of the Korean Society of Agricultural Engineers
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• v.36 no.1
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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.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.7
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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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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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.