A Data Driven Fault Isolation Method Based on Reference Faulty Situations with Application to a Nonlinear Chemical Process

Alcala, C. and Qin, S. (2010). Reconstruction-based contribution for process monitoring with kernel principal component analysis, Industrial and Engineering Chemistry Research 49(17): 7849–7857.10.1021/ie9018947 Search in Google Scholar

Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications, Wiley, New York.10.1002/9780470316757 Search in Google Scholar

Benaicha, A., Mourot, G., Benothman, K. and Ragot, J. (2013). Determination of principal component analysis models for sensor fault detection and isolation, International Journal of Control, Automation and Systems 11(2): 296–305.10.1007/s12555-012-0142-x Search in Google Scholar

Blanke, M., Kinnaert, M., Lunze, J. and Staroswiecki, M.D. (2006). Diagnosis and Fault-Tolerant Control, Springer, Berlin. Search in Google Scholar

Botre, C., Mansouri, M., Karim, N., Nounou, H. and Nounou, M. (2016). Kernel PLS-based GLRT method for fault detection of chemical processes, Journal of Loss Prevention in the Process Industries 43: 212–223.10.1016/j.jlp.2016.05.023 Search in Google Scholar

Buchberger, B. and Kauers, M. (2010). Bases de Gröbner, Scholarpedia 4249(5): 7763, DOI: 10.4249/scholarpedia. Ouvrir le DOISearch in Google Scholar

Dagnelie, P. (2012). Principes d’Expérimentation: Planification des Expériences et Analyse de leurs Résultats, Presses Agronomiques, Gembloux. Search in Google Scholar

Diop, S. (1989). A state elimination procedure for nonlinear systems, in J. Descusse et al. (Eds), New Trends in Nonlinear Control Theory, Springer, Berlin, pp. 190–198.10.1007/BFb0043028 Search in Google Scholar

Fazai, R., Taouali, O., Harkat, M.F. and Bouguila, N. (2016). A new fault detection method for nonlinear process monitoring, International Journal of Advanced Manufacturing Technology 87: 3425–3436.10.1007/s00170-016-8745-7 Search in Google Scholar

Frank, P.M. (1990). Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy—A survey and some new results, Automatica 26(3): 459–474.10.1016/0005-1098(90)90018-D Search in Google Scholar

Gao, Q., Liu, W., Zhao, X., Li, J. and Yu, X. (2017). Research and application of the distillation column process fault prediction based on the improved KPCA, IEEE International Conference on Mechatronics and Automation, Takamatsu, Japan, pp. 247–251. Search in Google Scholar

Gu, S., Liu, Y., Zhang, N. and Du, D. (2015). Fault detection approach based on weighted principal component analysis applied to continuous stirred tank reactor, Open Mechanical Engineering Journal 9(1): 966–972.10.2174/1874155X01509010966 Search in Google Scholar

Gunawan, H. (2018). n-Inner products, n-norms and angles between two subspaces, in M. Rozhansky et al. (Eds), Mathematical Analysis and Applications: Selected Topics, Wiley, Hoboken, pp. 493–515.10.1002/9781119414421.ch13 Search in Google Scholar

Huang, D., Zhang, D., Liu, Y., Zhang, S. and Zhu, W. (2014). A KPCA based fault detection approach for feed water treatment process of coal-fired power plant, 11th World Congress on Intelligent Control and Automation, Shenyang, China, pp. 3222–3227. Search in Google Scholar

Jia, Q. and Huang, Y. (2016). Quality-related fault detection approach based on dynamic kernel partial least squares, Chemical Engineering Research and Design 106: 242–252.10.1016/j.cherd.2015.12.015 Search in Google Scholar

Jordan, C. (1875). Essai sur la géométrie n dimensions, Bulletin de la Société Mathématique de France 3: 103–174.10.24033/bsmf.90 Search in Google Scholar

Jun, B.H., Park, J.H., Lee, S.I. and Chun, M.G. (2006). Kernel PCA based faults diagnosis for wastewater treatment system, in Wang J. et al. (Eds), Advances in Neural Networks, Lecture Notes in Computer Science, Vol. 3973, Springer, Berlin, pp. 426–431. Search in Google Scholar

Kallas, M., Mourot, G., Maquin, D. and Ragot, J. (2018). Data driven approach for fault detection and isolation in nonlinear system, International Journal of Adaptive Control and Signal Processing 32(11): 1569–1590.10.1002/acs.2931 Search in Google Scholar

Kallas, M., Mourot, G., Maquin, D. and Ragot, J. (2014). Diagnosis of nonlinear systems using kernel principal component analysis, 11th European Workshop on Advanced Control and Diagnosis, Berlin, Germany.10.1109/ECC.2015.7331026 Search in Google Scholar

Manikandan, P., Geetha, M., Jubi, K. and Jovitha, J. (2013). Fault tolerant fuzzy gain scheduling proportional-integral-derivative controller for continuous stirred tank reactor, Australian Journal of Basic and Applied Sciences 7(13): 84–93. Search in Google Scholar

Navi, M., Meskin, N. and Davoodi, M. (2018). Sensor fault detection and isolation of an industrial gas turbine using partial adaptive KPCA, Journal of Process Control 64: 37–48.10.1016/j.jprocont.2018.02.002 Search in Google Scholar

Nithya, N. and Vijayachitra, S. (2018). Fault detection and analysis using statistical data for continuous stirred tank reactor, International Journal of ChemTech Research 11(2): 266–274. Search in Google Scholar

Nur, M., Gunawan, H. and Neswan, O. (2018). A formula for the g-angle between two subspaces of a normed space, Beitr¨age zur Algebra und Geometrie/Contributions to Algebra and Geometry 59(1): 133–143.10.1007/s13366-017-0355-5 Search in Google Scholar

Ren, Z., Hou, J. and Zhou, H. (2016a). Fault detection and process monitoring of industrial process based on spherical kernel T-PLS, 42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, Italy, pp. 7161–7166. Search in Google Scholar

Ren, Z., Liu, J., She, Z., Yang, C. and Han, Y. (2016b). Data-driven approach of FS-SKPLS monitoring with application to wastewater treatment process, IEEE International Conference on Industrial Technology, Taipei, Taiwan, pp. 550–555.10.1109/ICIT.2016.7474880 Search in Google Scholar

Ritt, J.F. (1950). Differential Algebra, American Mathematical Society, New York.10.1090/coll/033 Search in Google Scholar

Simani, S., Farsoni, S. and Castaldi, S. (2018). Data-driven techniques for the fault diagnosis of a wind turbine benchmark, International Journal of Applied Mathematics and Computer Science 28(2): 247–268, DOI: 10.2478/amcs-2018-0018. Ouvrir le DOISearch in Google Scholar

Smyth, G. (2006). Nonlinear Regression, Wiley, Hoboken.10.1002/9780470057339.van017 Search in Google Scholar

Wang, L. (2018). Enhanced fault detection for nonlinear processes using modified kernel partial least squares and the statistical local approach, Canadian Journal Chemical Engineering 96(5): 1116–1126.10.1002/cjce.23058 Search in Google Scholar

Wang, Z.Y. and Wan, G.X. (2017). Temperature fault-tolerant control system of CSTR with coil and jacket heat exchanger based on dual control and fault diagnosis, Journal of Central South University 24(3): 655–664.10.1007/s11771-017-3466-0 Search in Google Scholar

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