Year: 2011
Type: conference-paper
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.
Source: IEEE Energytech
Authors: Leon Wu, Timothy Teravainen, Gail Kaiser, Roger Anderson, Albert Boulanger, Cynthia Rudin
Institutions: Columbia University, Massachusetts Institute of Technology
Language: English
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, National Aeronautics and Space Administration, Raytheon Company, Educational Testing Service, National Geographic Society, Princeton University, New College of Florida, York University, Sun Yat-sen University, National Institutes of Health, Defense Advanced Research Projects Agency, University of Illinois at Urbana-Champaign, Lamont-Doherty Earth Observatory, Columbia University, University at Buffalo, Sloan School of Management, Massachusetts Institute of Technology, Office of Naval Research
Award: G6671297155
OpenAlex ID: W2137335384