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Bien, Zeungnam
Intelligent Robot Control System Lab
Research Interests
  • Intelligent Control
  • Learning System Methodologies
  • Assistive Robotics
  • Smart Home System

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INCIPIENT MULTIPLE-FAULT DIAGNOSIS IN REAL-TIME WITH APPLICATION TO LARGE-SCALE SYSTEMS

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Title
INCIPIENT MULTIPLE-FAULT DIAGNOSIS IN REAL-TIME WITH APPLICATION TO LARGE-SCALE SYSTEMS
Author
CHUNG, HYBien, ZeungnamPARK, JHSEONG, PH
Keywords
Artificial neural network; Incipient multiple fault diagnosis; Kori nuclear power plant; Signed directed graph
Issue Date
1994-08
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation
IEEE TRANSACTIONS ON NUCLEAR SCIENCE, v.41, no.4, pp.1692 - 1703
Abstract
By using a modified signed directed graph (SDG) together with the distributed artificial neural networks and a knowledge-based system, a method of incipient multi-fault diagnosis is presented for large-scale physical systems with complex pipes and instrumentations such as valves, actuators, sensors, and controllers. The proposed method is designed so as to (1) make a real-time incipient fault diagnosis possible for large-scale systems, (2) perform the fault diagnosis not only in the steady-state case but also in the transient case as well by using a concept of fault propagation time, which is newly adopted in the SDG model, (3) provide with highly reliable diagnosis results and explanation capability of faults diagnosed as in an expert system, and (4) diagnose the pipe damage such as leaking, break, or throttling. This method is applied for diagnosis of a pressurizer in the Kori Nuclear Power Plant (NPP) unit 2 in Korea under a transient condition, and its result is reported to show satisfactory performance of the method for the incipient multi-fault diagnosis of such a large-scale system in a real-time manner
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ISSN
0018-9499
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EE_Journal Papers
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