Estimation of Trends in the Scram Rate at Nuclear Power Plants

• Published in: Technometrics Vol. 41; no. 4; pp. 352 - 364
• Main Authors: Martz, Harry F., Parker, Robert L., Rasmuson, Dale M.
• Format: Journal Article
• Language: English
• Published: Alexandria, VI Taylor & Francis Group 01.11.1999; Milwaukee, WI The American Society for Quality and The American Statistical Association; American Society for Quality Control; American Statistical Association
• Subjects: Applied sciences; Approximation; Bayes estimators; Computer science; control theory; systems; Control system analysis; Control theory. Systems; Data smoothing; Estimation methods; Exact sciences and technology; Gamma prior distribution; Government regulation; Hierarchical Bayes; Mathematics; Miscellaneous theoretical ideas and models; Nuclear power plants; Parametric empirical Bayes; Parametric inference; Physics; Poisson sampling; Population trends; Power reactors; Probability and statistics; Reliability; Sampling theory, sample surveys; Sciences and techniques of general use; Scram rate estimation; Shrinkage; Specific theories and interaction models; particle systematics; Standard deviation; Statistical discrepancies; Statistics; The physics of elementary particles and fields; Bayes estimation; Moving average; Bayes hierarchical estimation; bayes empirical estimation; Parametric empirical Bayes approximation; Shrinkage; Data smoothing; Poisson distribution; Exponentially weighted moving average; Poisson process; Fisher information; Gamma distribution; Parametric method; Nuclear power plant; Maximum likelihood; Sampling
• ISSN: 0040-1706; 1537-2723
• DOI: 10.1080/00401706.1999.10485934

An important task of the U.S. Nuclear Regulatory Commission is to examine annual operating data from the nation's population of nuclear power plants for trends over time. We are interested here in trends in the scram rate at 66 commercial nuclear power plants based on annual observed scram data from 1984-1993. For an assumed Poisson distribution on the number of unplanned scrams, a gamma prior, and an appropriate hyperprior, a parametric empirical Bayes (PEB) approximation to a full hierarchical Bayes formulation is used to estimate the scram rate for each plant for each year. The PEB-estimated prior and posterior distributions are then subsequently smoothed over time using an exponentially weighted moving average. The results indicate that such bidirectional shrinkage is quite useful for identifying reliability trends over time.