Several U.S. Nuclear Power Plants are attempting to move from a periodic sensor calibration schedule to a condition-based schedule using on-line calibration monitoring systems. This move requires a license amendment that must address the requirements set forth in a recently released Nuclear Regulatory Commission Safety Evaluation Report (SER). The major issue addressed in the SER is that of a complete uncertainty analysis of the empirical models. It has been shown that empirical modeling techniques are inherently unstable and inconsistent when the inputs are highly correlated. Regularization methods such as ridge regression or truncated singular value decomposition produce consistent results but may be overly simplified and not produce optimal results. This paper describes a new local regularization method, generalized ridge regression (GRR), and its potential value for sensor calibration monitoring at nuclear power plants. A case study is used to quantitatively compare different modeling methods.
[1]
Robert E. Uhrig,et al.
Regularization Methods for Inferential Sensing in Nuclear Power Plants
,
2000
.
[2]
Mark J. L. Orr.
Local Smoothing of Radial Basis Function Networks
,
1995
.
[3]
Arthur E. Hoerl,et al.
Ridge Regression: Biased Estimation for Nonorthogonal Problems
,
2000,
Technometrics.
[4]
R. Storn,et al.
Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces
,
2004
.
[5]
Kenneth V. Price,et al.
An introduction to differential evolution
,
1999
.
[6]
Colin L. Mallows,et al.
Some Comments on Cp
,
2000,
Technometrics.
[7]
A. Gribok,et al.
Selection of Multiple Regularization Parameters in Local Ridge Regression Using Evolutionary Algorithms and Prediction Risk Optimization
,
2003
.