Use of Kernel Based Techniques for Sensor Validation in Nuclear Power Plants

Several techniques have been proposed recently for sensor validation in nuclear as well as fossil power plants. They are all based on the same idea of using redundant information contained in collinear data sets to provide an estimation of monitored sensor value. Being data driven statistical techniques they are all prone to the instabilities and inconsistencies caused by collinear finite data sets. This paper examines these techniques from a unifying regularization point of view and presents some experimental comparison of their performance on real plant data. The results show that without proper regularization all the statistical techniques are sensitive to minor variations in the data. Regularization may effectively stabilize the inference making results repeatable and consistent.

[1]  David A. Belsley,et al.  Regression Analysis and its Application: A Data-Oriented Approach.@@@Applied Linear Regression.@@@Regression Diagnostics: Identifying Influential Data and Sources of Collinearity , 1981 .

[2]  Robert E. Uhrig,et al.  Use of Autoassociative Neural Networks for Signal Validation , 1998, J. Intell. Robotic Syst..

[3]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[4]  A.M.C. Chan,et al.  Feedwater flow measurement in US nuclear power generation stations , 1992 .

[5]  Jorma Rissanen,et al.  Stochastic Complexity in Statistical Inquiry , 1989, World Scientific Series in Computer Science.

[6]  Belle R. Upadhyaya,et al.  Monitoring feedwater flow rate and component thermal performance of pressurized water reactors by means of artificial neural networks , 1994 .

[7]  A. E. Hoerl,et al.  Ridge regression: biased estimation for nonorthogonal problems , 2000 .

[8]  Per Christian Hansen,et al.  Analysis of Discrete Ill-Posed Problems by Means of the L-Curve , 1992, SIAM Rev..

[9]  Frederick R. Forst,et al.  On robust estimation of the location parameter , 1980 .

[10]  Xiao Xu,et al.  Evaluation of instrument calibration monitoring using artificial neural networks , 1997 .

[11]  Robert E. Uhrig,et al.  The Use of Regularization in Inferential Measurements , 2000 .

[12]  D. Mackay,et al.  A Practical Bayesian Framework for Backprop Networks , 1991 .

[13]  Andrei V. Gribok,et al.  Regularization of Feedwater Flow Rate Evaluation for Venturi Meter Fouling Problem in Nuclear Power Plants , 2001 .

[14]  Vladimir Naumovich Vapni.  The Nature of Statistical Learning Theory , 1995 .

[15]  J. P. Herzog,et al.  Application of a model-based fault detection system to nuclear plant signals , 1997 .

[16]  Robert E. Uhrig,et al.  Regularization Methods for Inferential Sensing in Nuclear Power Plants , 2000 .

[17]  A Tikhonov,et al.  Solution of Incorrectly Formulated Problems and the Regularization Method , 1963 .