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|Written by YUAN FUQING, UDAY KUMAR and K. B. MISRA|
Complex System Reliability Evaluation using Support Vector Machine for Incomplete Data-set
Volume 7, Number 1, January 2011 - Paper 3 - pp. 32-42
YUAN FUQING1, UDAY KUMAR1 and K. B. MISRA21 Division of Operation and Maintenance Engineering,
Luleå University of Technology, SE-971 87 Luleå, Sweden
2 RAMS Consultants, 71 Vrindaban Vihar, Ajmer Road, Jaipur-302019, Rajasthan, India
(Received on March 16, 2010, revised on July 6, 2010)
Support Vector Machine (SVM) is an artificial intelligence technique that has been successfully used in data classification problems, taking advantage of its learning capacity. In systems modelled as networks, SVM has been used to classify the state of a network as failed or operating to approximate the network reliability. Due to the lack of information, or high computational complexity, the complete analytical expression of system states may be impossible to obtain, that is to say, only incomplete data-set can be obtained. Using these incomplete data-sets, depending on amount of missed data-set, this paper proposes two different approaches named rough approximation method and simulation based method to evaluate system reliability. SVM is used to make the incomplete data-set complete. Simulation technique is also employed in the so called simulation based approximation method. Several examples are presented to illustrate the approaches.
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