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• W89161661 abstract "Two types of maintenance interventions are usually administered at nuclear power plants: planned and corrective. The cost incurred includes the labor (manpower) cost, cost for new parts, or emergency order of expensive items. At the plant management level there is a budgeted amount of money to be spent every year for such operations. It is very important to have a good forecast for this cost since unexpected events can trigger it to a very high level. In this research we present a statistical factor model to forecast the maintenance cost for the incoming month. One of the factors is the expected number of unplanned (due to failure) maintenance interventions. We introduce a Bayesian model for the failure rate of the equipment which result is then fed into the cost forecasting model. A detailed statistical analysis is performed on a set of maintenance cost data gathered at the South Texas Project Nuclear Operating Company (STP) in Bay City, Texas, USA. 1. DATA DESCRIPTION There are three relevant to this analysis data bases that get populated and updated every month at STP: failure times, repair costs, and items’ characteristics. Each individual item can have several failure modes. The total number of the item-failure mode combinations in the first database is more than 46,000. As part of this study, a procedure that extracts the time of failure for each item-failure mode combination and computes the time between failures was constructed. The repair cost is recorded for each item-failure mode combination and it includes the cost for labor. The third set contains specific features (risk level, quality, etc.) for each item. A detailed description of all the procedures necessary to construct these data sets is given by Yu et. al. (2004). 2. BAYESIAN MODELING OF THE FAILURE RATE The main difference between classical and Bayesian estimation is the assumption about the parameters of the proposed distribution. The classical approach assumes that the parameters are unknown but constant, whereas the Bayesian regards them as random variables with specified prior distributions. The assumption of constant failure rates with the associated exponential failure distribution and homogeneous Poisson process pervades analysis in today's nuclear power industry. Typically, the data gathered in industry are the number of failures in a given time period (which is sufficient for exponential failure times). In general, we cannot expect to have a “complete data set” but rather a collection of failure times and “success” times (also known as censored times). The Risk Management Department at STP has implemented the procedure now recommended by the U.S. Department of Energy, see Blanchard (1993), where the constant failure rate assumption is" @default.
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• W89161661 date "2005-01-01" @default.
• W89161661 modified "2023-09-24" @default.
• W89161661 title "BASIC FACTORS TO FORECAST MAINTENANCE COST FOR NUCLEAR POWER PLANTS" @default.
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