A procedure for the reconstruction of faulty signals by means of an ensemble of regression models based on principal components analysis

6th American Nuclear Society International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies 2009

Volumn 2, Issue , 2009, Pages 652-663

  Baraldi, Piero ^a   Zio, Enrico ^a   Gola, Giulio ^b   Roverso, Davide ^b   Hoffmann, Mario ^b  

^a POLITECNICO DI MILANO   (Italy)

^b INSTITUTE FOR ENERGY TECHNOLOGY   (Norway)

Author keywords

BAGGING; Ensemble; RFSE; Sensor monitoring

Indexed keywords

FEATURE SELECTION; FINNISH; FULL-SCALE IMPLEMENTATION; MODEL OUTPUTS; MULTI-OBJECTIVE GENETIC ALGORITHM; ON-LINE SENSOR; OPTIMAL NUMBER; PRINCIPAL COMPONENTS ANALYSIS; REGRESSION MODEL; SENSOR MONITORING; SIGNAL GROUPS; TECHNIQUES USED; TRAINING DATA;

DATA REDUCTION; FEATURE EXTRACTION; GROUP TECHNOLOGY; INSTRUMENTS; MAN MACHINE SYSTEMS; NUCLEAR POWER PLANTS; OPTIMIZATION; PRESSURIZED WATER REACTORS; REGRESSION ANALYSIS; REPAIR; SENSORS;

PRINCIPAL COMPONENT ANALYSIS;

EID: 77952025283     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

1. M. Hoffmann, "Signal Grouping Algorithm for an Improved On-line Calibration Monitoring System", Proc. of FLINS, Genova, Italy (2006).
2. D. Roverso, M. Hoffmann, E. Zio, P. Baraldi, G. Gola, "Solutions for plant-wide on-line calibration monitoring", Proc. ESREL, Stavanger, Norway, 1, pp. 827-832 (2007).
3. M. P. Perrone and L. N. Cooper, When networks disagree: ensemble methods for hybrid neural networks, National Science Fundation, USA (1992).
4. P. Baraldi, E. Zio, G. Gola, D. Roverso, M. Hoffmann, "Reconstruction of faulty signals by an ensemble of principal component analysis models optimized by a multi-objective genetic algorithm", Proc. of FLINS, Madrid, Spain (2008).
5. G. Gola, E. Zio, P. Baraldi, D. Roverso, M. Hoffmann, "Reconstructing signals for sensor validation by a GA-optimized ensemble of PCA models", HWR-894, OECD Halden Reactor Project (2008).
6. L. Breiman, "Bagging predictors", Machine Learning, 24, pp. 123-140 (1996).
7. R. Polikar, "Ensemble based systems in decision making", IEEE Circuits and Systems Magazine, 6(3), pp. 21-45 (2006).
8. R. Bryll, R. Gutierrez-Osuna, F. Quek, "Attribute bagging: improving accuracy of classifiers ensembles by using random feature subsets", Pattern Recognition, 36, pp. 1291-1302 (2003).
9. I.T. Jolliffe, Principal Component Analysis, Springer Eds. (2002).
10. J. Kittler, M. Hatef, R. P. W. Duin, J. Matas, "On Combining Classifiers", IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(3), pp. 226-239 (1998).
11. D.E. Goldberg, Genetic algorithms in search, optimization, and machine learning, Addison-Wesley Publ. Co. (1989).
12. Y. Sawaragy, H. Nakayama, T. Tanino, Theory of multiobjective optimization, Academic Press, Orlando, Florida (1985).