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Estimation of system reliability using a semiparametric model

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6 Author(s)
Leon Wu ; Department of Computer Science and the Center for Computational Learning Systems, Columbia University, New York, 10027 USA ; Timothy Teräväinen ; Gail Kaiser ; Roger Anderson
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An important problem in reliability engineering is to predict the failure rate, that is, the frequency with which an engineered system or component fails. This paper presents a new method of estimating failure rate using a semiparametric model with Gaussian process smoothing. The method is able to provide accurate estimation based on historical data and it does not make strong a priori assumptions of failure rate pattern (e.g., constant or monotonic). Our experiments of applying this method in power system failure data compared with other models show its efficacy and accuracy. This method can be used in estimating reliability for many other systems, such as software systems or components.

Published in:

Energytech, 2011 IEEE

Date of Conference:

25-26 May 2011