Genetic algorithms for signal grouping in sensor validation: A comparison of the filter and wrapper approaches

Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability

Volumn 222, Issue 2, 2008, Pages 189-206

  Baraldi, P ^a   Zio, E ^a   Gola, G ^a   Hoffmann, M ^a   Roverso, D ^b  

^a POLITECNICO DI MILANO   (Italy)

^b INSTITUTE FOR ENERGY TECHNOLOGY   (Norway)

Author keywords

fitter wrapper; multi objective genetic algorithms; principal component analysis; sensor monitoring; signal validation

Indexed keywords

EID: 84993804243     PISSN: 1748006X     EISSN: 17480078     Source Type: Journal    
DOI: 10.1243/1748006XJRR137     Document Type: Article

References (38)

1. Hoffmann M. On-line monitoring for calibration reduction. HWR-784, OECD Halden Reactor Project, October 2005
2. Hoffmann M. Signal grouping algorithm for an improved on-line calibration monitoring system. Proceedings of FLINS, Genova, Italy, 2006
3. Roverso D. Solutions for plant-wide on-line calibration monitoring. Proceedings of ESREL 2007. Stavanger, Norway, 2007, Vol. 1, pp. 827–832
4. Perrone M.P. Cooper L.N. When networks disagree: ensemble methods for hybrid neural networks. 1992 (National Science Foundation, USA)
5. Krogh A. Vedelsby J. Neural network ensembles, cross-validation and active learning. Advances in newel information processing systems, (Eds Ed. Tesauro G. Touretzky D.S. Loen T.K.) 1995, Vol. 7, pp. 231–238 (MIT press, Cambridge, MA, USA)
6. Sharkey A.J.C. On combining artificial neural nets. Connection Sci., 1996, 8(3), 299–314
7. Holland J.H. Adaptation in natural and artificial systems: An introductory analysis with application to biology. Control and artificial intelligence, 4th edition, 1975 (MIT Press, Cambridge, MA, USA)
8. Goldberg D.E. Genetic algorithms in search, optimization, and machine learning, 1989 (Addison-Wesley, New York)
9. Chambers L. Practical handbook of genetic algorithms: applications Vol. 1; new frontiers Vol. II, 1995 (CRC Press, London)
10. Sawaragy Y. Nakayama H. Tanino T. Theory of multiobjective optimization, 1985 (Academic Press, Orlando, Florida)
11. Raymer M.L. Punch W.F. Goodman E.D. Khun L.A. Jain A.K. Dimensionality reduction using genetic algorithms. IEEE Trans. Evolutionary Computation, 2000, 4(2), 164–171
12. Bozdogan H. Statistical data mining with informational complexity and genetic algorithm. Statistical data mining and knowledge discovery, (Ed. Ed. Bozdogan H.), 2003 (CRC Press, London)
13. Jolliffe I.T. Principal component analysis, 2002 (Springer)
14. Diamantaras K.I. Kung S.Y. Principal component neural networks: theory and applications, 1996 (John Wiley, New York)
15. Scholkopf B. Smola A. Muller K.R. Kernel principal component analysis. Advances in Kernel Methods—Support Vector Learning, 1999, pp. 327–352 (MIT Press, Cambridge, USA)
16. Moore B. Principal component analysis in linear systems: Controllability, observability, and model reduction. IEEE Trans. Automatic Control, 1981, 26(1)
17. Baldi P. Hornik K. Neural networks and principal component analysis: Learning from examples without local minima. Neural Networks, 1989, 2(1), 53–58
18. Hoffmann M. Kirschner A. PEANO—findings from using the NNPLS algorithm and HAMMLAB applications. HWR-690, OECD Halden Reactor Project, August 2004
19. Kirschner A. Hoffmann M. PEANO NNPLS: advancements in 2002–03. HWR-741, OECD Halden Reactor Project. April 2004
20. Fantoni P.F. Mazzola A. Multiple-failure signal validation in nuclear power plants using artificial neural networks. Nuclear Technol., 1996, 113(3), 368–374
21. Fantoni P.F. Experiences and applications of Peano for online monitoring in power plants. Progress in nuclear energy, 2005, Vol. 46, pp. 206–225, (Elsevier)
22. Marseguerra M. Zio E. Marcucci F. A soft-computing based classification procedure for the identification of transients in the steam generator of a pressurized water reactor. Annls Nuclear Energy, 2004, 31, 1429–1446
23. Kramer M.A. Autoassociative neural networks. Computers Chem. Engng, 1992, 16(4), 313–328
24. Hines J.W. Uhrig R.E. Wrest D.J. Use of auto-associative neural networks for signal validation. J. Intelligent and Robotic Systems, 1998, 21(2), 143–154
25. Fantoni P.F. A neuro-fuzzy model applied to full range signal validation of PWR nuclear power plant data. Int. J. General Systems, 2000, 29(2), 305–320
26. Duran B. Odell P. Cluster analysis: a survey, 1974 (Springer Verlag, New York)
27. Zio E. Baraldi P. Roverso D. An extended classifiability index for feature selection in nuclear transients. Annls Nuclear Energy, 2005, 32, 1632–1649
28. Kohavi R. John G. Wrappers for feature subset selection. Artif. Intelligence, 1997, 97, 273–324
29. Zio E. Baraldi P. Pedroni N. Selecting features for nuclear transients classification by means of genetic algorithms. IEEE Trans. Nuclear Sci., 2006, 53(3), 1479–1493
30. Gola G. Zio E. Baraldi P. Roverso D. Hoffmann M. Signal grouping for sensor validation: a multi-objective genetic algorithm approach. HWR-852, OECD Halden Reactor Project, Feb. 2007
31. Cotter S.F. Adler R. Rao R.D. Kreutz-Delgado K. Forward sequential algorithms for best basis selection. IEEE Proc. Vision, Image Signal Processing, 1999, 146(5), 235–244
32. Mao K.Z. Orthogonal forward selection and backward elimination algorithms for feature subset selection. IEEE Trans. Systems, Man, Cybernetics, Part B, 2004, 34(1), 629–634
33. Zhang H. Sun G. Feature selection using tabu search method. Pattern Recognition, 2002, 35, 701–711, 2002
34. Girard S. Lovleff S. Auto-associative models and generalized principal component analysis. J. Multivariate Analysis, 2005, 93(1), 21–39
35. Nowicki D. Dekhtyarenko O. Kernel-based associative memory. Proceedings of International Joint Conference on Neural Networks, Vol. 1, 2004, pp. 741–744
36. Lawrence I. Kuei L. A concordance correlation coefficient to evaluate reproducibility. Biometrics, 1989, 45(1), 255–268
37. Hunt R.J. Percent agreement, Pearson's correlation, and kappa as measures of inter-examiner reliability. J. Dental Res., 1986, 65, 128–130
38. Marseguerra M. Lecture notes on principal components analysis (PCA), Polytechnic of Milan