Bayesian semiparametric analysis for a single item maintenance optimization |
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Authors: | Paul Damien Alexander Galenko Elmira Popova Timothy Hanson |
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Institution: | 1. Information, Risk and Operations Management, McCombs School of Business, The University of Texas at Austin, Austin, TX 78712, USA;2. Operations Research and Industrial Engineering, The University of Texas at Austin, Austin, TX 78712, USA;3. Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA |
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Abstract: | We address the problem of a finite horizon single item maintenance optimization structured as a combination of preventive and corrective maintenance in a nuclear power plant environment. We present Bayesian semiparametric models to estimate the failure time distribution and costs involved. The objective function for the optimization is the expected total cost of maintenance over the pre-defined finite time horizon. Typically, the mathematical modeling of failure times are based on parametric models. These models fail to capture the true underlying relationships in the data; indeed, under a parametric assumption, the hazard rates are treated as unimodal, which, as shown in this paper, is incorrect. Importantly, assuming an increasing failure rate, as is typically done, we show, is way off the mark in the present context. Since hazard and cost estimates feed into the optimization phase, from a risk management perspective, potentially gross errors, resulting from purely parametric models, can be obviated. We show the effectiveness of our approach using real data from the South Texas Project Nuclear Operating Company (STPNOC) located in Bay City, Texas. |
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Keywords: | Maintenance Reliability Replacement Uncertainty modeling |
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