Data-driven models for fault detection using kernel PCA: A water distribution system case study
Open Access
|Dec 2012References
- Aizerman, M., Braverman, E. and Rozonoer, L. (1964). Theoretical foundations of the potential function method in pattern recognition,: 821-837.
- Alpaydin, E. (2010)., The MIT Press, Cambridge, MA.
- Barshan, E., Ghodsi, A., Azimifar, Z. and Jahromi, M.Z. (2011). Supervised principal component analysis: Visualization, classification and regression on subspaces and submanifolds,(7): 1357-1371.
- Brdyś, M., Grochowski, M., Gminski, T., Konarczak, K. and Drewa, M. (2008). Hierarchical predictive control of integrated wastewater treatment systems,(6): 751-767.
- Campbell, W.M., Campbell, J.P., Reynolds, D.A., Singer, E. and Torres-Carrasquillo, P.A. (2006). Support vector machines for speaker and language recognition,(2-3): 210-229.
- Duzinkiewicz, K., Borowa, A., Mazur, K., Grochowski, M., Brdys, M.A. and Jezior, K. (2008). Detection and localisation in drinking water distribution networks by multiregional PCA,(2): 135-152.
- Hoffman, H. (2007). Kernel PCA for novelty detection,(3): 863-874.
- Hott, K. (2008). Robust face recognition under partial occlusion based on support vector machine with local Gaussian summation kernel,(11): 1490-1498.
- Isermann, R. (1984). Process fault detection based on modeling and estimation methods-A survey,(4): 387-404.
- Jackson, J.E. (1991)., Wiley, Newark, NJ.
- Jezior, K., Mazur, K., Borowa, A., Grochowski, M. and Brdys, M. A. (2007). Multiregional PCA for leakage detection and localisation in DWDS-Chojnice case study,J. Korbicz, K. Patan and M. Kowal (Eds.),
- , Academic Publishing House EXIT, Warsaw, pp. 303-310.
- Kulczycki, P. and Charytanowicz, M. (2010). A complete gradient clustering algorithm formed with kernel estimators,(1): 123-134, DOI: 10.2478/v10006-010-0009-3.
- Li, J., Li, X. and Tao, D. (2008). KPCA for semantic object extraction in images,(10): 3244-3250.
- Lima, C.A. and Coelho, A.L. (2011). Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study,(2): 83-95.
- Mashford, J., Silva, D.D., Marney, D. and Burn, S. (2009). An approach to leak detection in pipe networks using analysis of monitored pressure values by support vector machine,pp. 534-539.
- Mercer, J. (1909). Functions of positive and negative type, and their connection with the theory of integral equations,(441-458): 415-446.
- Nogayama, T., Takahashi, H. and Muramatsu, M. (2003). Generalization of kernel PCA and automatic parameter tuning,(389): 43-48.
- Nowicki, A. and Grochowski, M. (2011). Kernel PCA in application to leakage detection in drinking water distribution system,P. Jedrzejowicz, N.T. Nguyen and K. Hoang (Eds.),, Lecture Notes in Computer Science, Vol. 6922, Springer, Berlin, pp. 497-506.
- Patan, K. and Korbicz, J. (2012). Nonlinear model predictive control of a boiler unit: A fault tolerant control study,(1): 225-237, DOI: 10.2478/v10006-012-0017-6.
- Schölkopf, B., Mika, S., Burges, C.J.C., Knirsch, P., Mu¨ller, K.-R., Ra¨tsch, G. and Smola, A.J. (1999). Input space versus feature space in kernel-based methods,(5): 1000-1017.
- Schölkopf, B., Smola, A. and Mu¨ller, K.R. (1998). Nonlinear component analysis as a kernel eigenvalue problem,(5): 1299-1319.
- Shawe-Taylor, J. and Cristianini, N. (2004)., Cambridge University Press, Cambridge.
- Slišković, D., Grbic´, R. and Hocenski, Ž. (2011). Methods for plant data-based process modeling in soft-sensor development,(4): 306-318.
- Thornton, J., Sturm, R. and Kunkel, G. (2008)., McGraw-Hill Companies, New York, NY.
- Venkatasubramanian, V., Rengaswamy, R. and Kavuri, S. (2003a). A review of process fault detection and diagnosis, Part II: Qualitative models and search strategies,(3): 313-326.
- Venkatasubramanian, V., Rengaswamy, R., Kavuri, S. and Yin, K. (2003b). A review of process fault detection and diagnosis, Part III: Process history based methods,(3): 327-346.
- Venkatasubramanian, V., Rengaswamy, R., Yin, K. and Kavuri, S. (2003c). A review of process fault detection and diagnosis, Part I: Quantitative model-based methods,(3): 293-311.
- Xiao-Li, C. and Jiang Chao-Yuan, G.S.-Y. (2008). Leakage monitoring and locating method of water supply pipe network,, pp. 497-506.
- Xiong, H., Swamy, M. and Ahmad, M.O. (2005). Optimizing the kernel in the empirical feature space,(2): 460-474.
Language: English
Page range: 939 - 949
Published on: Dec 28, 2012
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Related subjects:
© 2012 Adam Nowicki, Michał Grochowski, Kazimierz Duzinkiewicz, published by University of Zielona Góra
This work is licensed under the Creative Commons License.