Abstract
Inferential sensing is the use of information related to a plant parameter to infer its actual value. The most common method of inferential sensing uses a mathematical model to infer a parameter value from correlated sensor values. Collinearity in the predictor variables leads to an ill posed problem that causes inconsistent results when data based models such as linear regression and neural networks are used. This chapter presents several linear and non-linear inferential sensing methods including linear regression and neural networks. Both of these methods can be modified from their original form to solve ill posed problems and produce more consistent results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Basseville, M. (1988). “Detecting Changes in Signals and Systems–A Survey,” Automatica, 24, 309–326.
Bishop, Christopher M. (1996), Neural Networks for Pattern Recognition,Oxford University Press.
Black, C.L., R.E. Uhrig, and J.W. Hines, (1998), “System Modeling and Instrument Calibration Verification with a Non-linear State Estimation Technique,” published in the proceedings of the Maintenance and Reliability Conference (MARCON 98), Knoxville, TN, May 12–14.
Cherkassky, V., and F. Muller (1998), Learning From Data,John Wiley & Sons.
Desai, M., A. Ray (1981), “A Fault Detection and Isolation Methodology,” Proc. 20th Conf. on Decision and Control, 1363–1369.
EPRI (1992), “Feedwater Flow Measurement in U.S. Nuclear Power Generation Stations,” TR-101388, Electric Power Research Institute.
Efron, B. (1982), The Jacknife, The Bootstrap, and Other Resampling Plans. Philadelphia, Penn.: Society for Industrial and Applied Mathematics.
Erriksson, J., Marten Gulliksson, Per Lindsrom and Per-Ake Wedin, “Regularization Tools for Training Large-Scale Neural Networks,” Technical report UMINF-96.05 Department of Computing Science, Umea University, Sweden.
Gantmacher, F.R. (1959), Applications of the Theory of Matrices, Interscience Publishers, New York.
Geladi, P, and B.R. Kowalski (1986), “Partial Least-Squares Regression: A Tutorial,” Analyta Chimica, Acta, 185, pp 1–17.
Gertler, J. (1988), “Survey of Model Based Failure Detection and Isolation in Complex Plants,” IEEE Control Systems Magazine, pp. 3–11.
Glockler, O. (1991), “Fault Detection in Nuclear Power Plants Applying the Sequential Probability Ratio Test of Mar-Based Residual Time Series,” Proceedings of the AI 91 Frontiers in Innovative Computing for the Nuclear Industry, Jackson, Wyoming, 859–868.
Golub, G. H., M.T. Heath, and G. Wahba (1979), Generalized cross-validation as a method for choosing a good ridge parameter, Technometrics, 21, pp. 215–223.
Golub, G.H., and C.F. Van Loan (1996), Matrix Computations, Third Edition, the Johns Hopkins University Press, Baltimore, MD.
Grini, R.E., O. Naess, O. Berg (1989), “Model-Based Fault Detection and Diagnosis in Process Plant Operation,” Proceedings of the Seventh Power Plant Dynamics, Control & Testing Symposium, Knoxville, Tennessee, 56.01–56.14.
Gross, K.C., R.M. Singer, S.W. Wegerich, J.P. Herzog, R. Van Alstine, and F. K. Bockhorst (1997), “Application of a Model-based Fault Detection System to Nuclear
Plant Signals,“ Proc. 9th Intl. Conf. on IntelligentSystems Applications to Power Systems, Seoul, Korea.
Gull, S.F. “Bayesian Inductive Inference and Maximum Entropy,” Maximum Entropy and Bayesian Methods in Science and Engineering,Vol.1, Erickson and Smith, eds., pp.53–74, Kluwer, Dordrecht.
Hadamard, J. (1923), Lectures on Cauchy’s Problem in Linear Partial Differential Equations, Yale University Press, New Haven.
Hansen, P.C. (1989), Regularization, GSVD and Truncated GSVD,BIT 29, pp. 491504.
Hansen, P.C. (1990), The Discrete Picard Condition for Discrete Ill-Posed Problems, BIT 30, pp. 658–672.
Hansen, P.C. (1994), Regularization Tools: A Matlab package for analysis and solution of discrete ill posed problems, Numer. Algorithms, vol. 6, pp. 1–35.
Hansen, P.C. (1995), Test Matrices for Regularization Methods, SIAM J. SCI. Comput. vol. 16, No. 2, pp. 506–512.
Hansen, P.C. (1997), Rank —Deficient and Discrete Ill-Posed Problems. Numerical Aspects of Linear Inversion, SIAM, Philadelphia.
Hansen, P.C. (1992), Analysis of Discrete Ill-Posed Problems by Means of the L-curve, SIAM Review vol. 34, No. 4, pp. 561–58.
Hardy, C.R., D.W. Miller, B.K. Hajek (1992), “A Model-Based Approach to Malfunction Isolation in Interacting Systems,” Proceedings of the 8th Power Plant Control & Testing Symposium, Knoxville, Tennessee, 37.01–37.13.
Hertz, J., A. Krogh, and R.G. Palmer (1991), Introduction to the Theory of Neural Computation,Addison-Wesley.
Hines, J.W. and D.J. Wrest (1997a), “Signal Validation Using an Adaptive Neural Fuzzy Inference System,” Nuclear Technology, August, pp. 181–193.
Hines, J.W. and R.E. Uhrig (1997b), “Use of Autoassociative Neural Networks for Signal Validation,” Journal of Intelligent and Robotic Systems, Kluwer Academic Press.
Hines, J.W., R.E., Uhrig, C. Black and X. Xu (1997c), “An Evaluation of Instrument Calibration Monitoring Using Artificial Neural Networks,” published in the proceedings of the 1997 American Nuclear Society Winter Meeting, in Albuquerque, NM, November 16–20.
Hoerl, A.E., and R.W. Kennard (1970), Ridge Regression: Biased Estimation for Nonorthogonal Problems, Technometrics, 12, pp. 55–67.
Holbert, K.E. and B.R. Upadhyaya (1990), “An Integrated Signal Validation System For Nuclear Power Plants,” Nuclear Technology, Vol. 92, pp. 411–427.
Holbert, K.E., Heger, S.A. and A.M. Ishaque, (1995) “Fuzzy Logic For Power Plant Signal Validation,” Proceedings of the 9th Power Plant Control & Testing Symposium, Knoxville, Tennessee, May 24–26, 20.01–20.15.
Hornick, K., Stinchcombe, M., and H. White, 1989, Multilayer Feedforward Networks are Universal Approximators, Neural Networks, 2, pp 359–366.
Hoskuldsson, A., (1988), “PLS Regression Methods,” Journal of Chemometrics, Vol. 2, pp. 211–228.
Ikonomopoulos, A., R.E. Uhrig, L. H. Tsoukalas (1992), “A Methodology for Performing Virtual Measurements in a Nuclear Reactor System,” Transactions of the 1992 American Nuclear. Society International Conference on Fifty Years of Controlled Nuclear Chain Reaction: Past, Present, and Future, Chicago, Illinois, 106–107.
Isermann, R. (1984), “Process Fault Detection Based on Modeling and Estimation Methods-A Survey,” Automatica, Vol. 20, No. 4, 381–404.
Jolliffe, I. (1986), Principal Components Analysis,, Springer-Verlag, New York.
Kavaklioglu, K., and Belle R. Upadhyaya (1994), “Monitoring Feedwater Flow Rate and Component Thermal Performance of Pressurized Water Reactors By Means of Artificial Neural Networks,” Nuclear Technology Vol. 107.
Kittamura, M. (1980), “Detection of Sensor Failures in Nuclear Plants Using Analytical Redundancy,” Transactions of the American Nuclear Society, 34, 581–583.
Kramer, R. (1998), Chemometric Techniques for Quantitative Analysis,Marcel-Dekker.
MacKay, David J.C. (1992), “Bayesian Interpolation,” Neural Computation, Vol. 4, No. 3, pp. 415–447.
MacKay, David J.C. (1992), “A Practical Bayesian Framework for Backpropagation Networks,” Neural Computation, Vol. 4 No. 3, pp. 448–472
MacKay, David J.C. (1995), “Bayesian Methods for Neural Networks:Theory and Applications,” Course notes for neural network summer school. University of Cambridge programme for industry.
Morozov, V.A. (1984), Methods for Solving Incorrectly Posed Problems, Springer-Verlag, New York.
Nuclear News, “Flow Rate Mismeasurement Causes Unneeded Derating,” Nucl. News, 36, 2, 39, Feb., 1993.
Patton, R.J., J. Chen (1991), “A Review of Parity Space Approaches to Fault Diagnosis,” IFAC/IMACS Safeprocess Conference, Baden-Baden, Germany, 239–255.
Qin, S.J., and T.J. McAvoy (1992), “Nonlinear PLS Modeling Using Neural Networks,” Computers chem. Engng., Vol. 16, No. 4, pp. 379–391.
Qualls, A.L., R.E. Uhrig, and B.R. Upadhyaya (1988), “Development of an Expert System for Signal Validation,” Topical Report prepared for the U.S. Department of Energy, by The University of Tennessee, Knoxville, DOE/NE/37959–17.
Singer, R.M., K. C. Gross, J.P. Herzog, R. W. King, and S. W. Wegerich (1997), “Model-Based Nuclear Power Plant Monitoring and Fault Detection: Theoretical Foundations,” Proc. 9th Intl. Conf. on Intelligent Systems Applications to Power Systems, Seoul, Korea.
Tikhonov, A.N. (1963), Solution of incorrectly formulated problems and the regularization method, Doklady Akad. Nauk USSR 151 (1963), pp. 501–504.
Tsoukalas, L.H. (1992), “Expert Systems for Power Plant Applications: An Overview,” Proceedings of the 8th Power Plant Control & Testing Symposium, Knoxville, Tennessee, May 27–29, pp47. 01–47. 10.
Uhrig, R.E. (1993), “Artificial Neural Networks and Potential Applications to Nuclear Power Plants,” Proceedings of the Conference on Structural Mechanics in Reactor Technology, Knostanz, Germany.
Uhrig, R.E., J.W. Hines, and W.R. Nelson (1998), “Integration of Artificial Intelligence Systems into a Monitoring and Diagnostic System for Nuclear Power Plants,” presented at the Special Meeting on Instrumentation and Control of the Halden Research Center, Lillihammer, Norway, March 28–21.
Upadhyaya, B.R., E. Eryurek (1992), “Application of Neural Networks for Sensor Validation and Plant Monitoring,” Nuclear Technology, Vol. 97, 170–176.
Willsky, A.S. (1976), “A Survey of Design Methods for Failure Detection in Dynamic Systems,” Automatica, Vol. 12, 601–611.
Flow Rate Mismeasurement Causes Unneeded Derating,“ Nucl. News,36, 2, 39, Feb., 1993.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Hines, J.W., Gribok, A.V., Attieh, I., Uhrig, R.E. (2000). Regularization Methods for Inferential Sensing in Nuclear Power Plants. In: Ruan, D. (eds) Fuzzy Systems and Soft Computing in Nuclear Engineering. Studies in Fuzziness and Soft Computing, vol 38. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1866-6_13
Download citation
DOI: https://doi.org/10.1007/978-3-7908-1866-6_13
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-2466-7
Online ISBN: 978-3-7908-1866-6
eBook Packages: Springer Book Archive