Title :
Estimation of system reliability using a semiparametric model
Author :
Wu, Leon ; Teräväinen, Timothy ; Kaiser, Gail ; Anderson, Roger ; Boulanger, Albert ; Rudin, Cynthia
Author_Institution :
Dept. of Comput. Sci., Columbia Univ., New York, NY, USA
Abstract :
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.
Keywords :
Gaussian processes; failure analysis; power system reliability; Gaussian process smoothing; failure rate estimation; failure rate pattern; failure rate prediction; power system failure data; reliability engineering; semiparametric model; system reliability estimation; Data models; Distribution functions; Estimation; Gaussian processes; Hazards; Reliability; Smoothing methods; Gaussian processes; estimation theory; failure analysis; parametric statistics; power system reliability; prediction methods; reliability engineering; software reliability; statistical analysis; stochastic processes;
Conference_Titel :
Energytech, 2011 IEEE
Conference_Location :
Cleveland, OH
Print_ISBN :
978-1-4577-0777-3
Electronic_ISBN :
978-1-4577-0775-9
DOI :
10.1109/EnergyTech.2011.5948537
