Cite

Adam-Medina, M., Theilliol, D. and Sauter, D. (2003). Simultaneous fault diagnosis and robust model selection in multiple linear models framework, Proceedings of the 5thIFAC Symposium on Fault Detection, Supervision and Safetyof Technical Processes, SAFEPROCESS, Washington,DC, USA, pp. 513-518.Search in Google Scholar

Chen, J. and Patton, R.J. (1999). Robust Model Based Fault Diagnosisfor Dynamic Systems, Kluwer Academic Publishers, Boston, MA.10.1007/978-1-4615-5149-2_9Search in Google Scholar

Chen, R.H. and Speyer, J.L. (1999). Optimal stochastic multiple faults detection filter, Proceedings of the 38th IEEE Conferenceon Decision and Control, Phoenix, AL, USA, Vol. 5, pp. 4965-4970.Search in Google Scholar

Clark, R.N. (1989). State estimation schemes for instrument fault detection, in R.J. Patton, P.M. Frank and R.N. Clark (Eds.), Fault Diagnosis in Dynamic Systems: Theory andApplication, Prentice Hall, London.Search in Google Scholar

Daigle, M., Koutsoukos, X. and Biswas, G. (2006). Multiple fault diagnosis in complex physical systems, 17th InternationalWorkshop on Principles of Diagnosis, Penaranda deDuero, Spain, pp. 69-76.Search in Google Scholar

de Kleer, J. and Kurien, J. (2003). Fundamentals of model-based diagnosis, Proceedings of the 5th IFAC Symposium onFault Detection, Supervision and Safety of Technical Processes,SAFEPROCESS 2003, Washington, DC, USA, pp. 25-36.Search in Google Scholar

de Kleer, J. and Williams, B.C. (1987). Diagnosing multiple faults, Artificial Intelligence 32(1): 97-130.10.1016/0004-3702(87)90063-4Search in Google Scholar

De-Persis, C. and Isidori, A. (2001). A geometric approach to nonlinear fault detection and isolation, IEEE Transactionson Automatic Control 46(6): 853-866.10.1109/9.928586Search in Google Scholar

Ding, S.X. (2008). Model-based Fault Diagnosis Techniques, Springer, Berlin/Heidelberg.Search in Google Scholar

Frank, P.M. (1987). Fault diagnosis in dynamic systems via state estimations methods: A survey, in S.G. Tzafestas, M. Singh and G. Schmidt (Eds.), System Fault Diagnostics,Reliability and Related Knowledge-based Approaches, Vol. 2, D. Reidel Publishing Company, Dordrecht/Boston, MA/Lancaster/Tokyo.Search in Google Scholar

Frank, P.M. (1991). Enhancement of robustness in observer-based fault detection, Proceedings of theIFAC/IMACS Symposium on Fault Detection, Supervisionand Safety of Technical Processes, SAFEPROCESS,Baden-Baden, Germany, pp. 275-288.Search in Google Scholar

Geltler, J. and Singer, D. (1990). A new structural framework for parity equation based failure detection and isolation, Automatica 26(2): 381-388.10.1016/0005-1098(90)90133-3Search in Google Scholar

Gertler, J. (1998). Fault Detection and Diagnosis in EngineeringSystems, Marcel Dekker, Inc., New York, NY/Basel/Hong Kong.Search in Google Scholar

Górny, B. (2001). Consistency-Based Reasoning in Model-Based Diagnosis, Ph.D. thesis, AGH University of Science and Technology, Cracow.Search in Google Scholar

Hamscher, W., Console, L. and de Kleer, J. (1992). Readingsin Model-Based Diagnosis, Morgan Kaufmann Publishers, San Mateo, CA.Search in Google Scholar

Hashtrudi, S. and Massoumnia, M. (1999). Generic solvability of the failure detection and identification problem, Automatica35(5): 887-893.10.1016/S0005-1098(98)00221-0Search in Google Scholar

Hwee, T.N. (1991). Model-based, multiple-fault diagnosis of dynamic, continuous physical devices, IEEE Expert6(6): 38-43.10.1109/64.108950Search in Google Scholar

Isermann, R. (2006). Fault Diagnosis Systems. An Introductionfrom Fault Detection to Fault Tolerance, Springer-Verlag, New York, NY.10.1007/3-540-30368-5Search in Google Scholar

Khémiri, K., Ben Hmida, F., Ragot, J. and Gossa, M. (2011). Novel optimal recursive filter for state and fault estimation of linear stochastic systems with unknown disturbances, International Journal of Applied Mathematicsand Computer Science 21(4): 629-637, DOI: 10.2478/v10006-011-0049-3.10.2478/v10006-011-0049-3Search in Google Scholar

Korbicz, J., Ko´scielny, J.M., Kowalczuk, Z. and Cholewa, W. (Eds.) (2004). Fault Diagnosis. Models, Artificial Intelligence,Applications, Springer, Berlin.10.1007/978-3-642-18615-8Search in Google Scholar

Kościelny, J.M. (1995). Fault isolation in industrial processes by dynamic table of states method, Automatica31(5): 747-753.10.1016/0005-1098(94)00147-BSearch in Google Scholar

Kościelny, J.M. (2001). Diagnostics of Automated IndustrialProcesses, Akademicka Oficyna Wydawnicza Exit, Warsaw, (in Polish).Search in Google Scholar

Kościelny, J.M. and Łab˛eda, Z.M. (2007). Double fault distinguishability in linear systems, 8th Conference onDiagnostics of Processes and Systems, Słubice, Poland, pp. 45-52, (in Polish).Search in Google Scholar

Kościelny, J.M., Barty´s, M. and Syfert, M. (2012). Method of multiple fault isolation in large scale systems, IEEE Transactionson Control Systems Technology 20(5): 1302-1310.10.1109/TCST.2011.2162587Search in Google Scholar

Ligęza, A. and Ko´scielny, J.M. (2008). A new approach to multiple fault diagnosis: A combination of diagnostic matrices, graphs, algebraic and rule-based models. The case of two-layer models, International Journal of AppliedMathematics and Computer Science 18(4): 465-476, DOI: 10.2478/v10006-008-0041-8.10.2478/v10006-008-0041-8Search in Google Scholar

Manders, E.J., Narasimhan, S., Biswas, G. and Mosterman, P. (2000). A combined qualitative/quantitative approach for fault isolation in continuous dynamic systems, 4th Symposiumon Fault Detection, Supervision and Safety for TechnicalProcesses, Budapest, Hungary, pp. 1074-1079.Search in Google Scholar

Mattone, R. and de Luca, A. (2006). Relaxed fault detection and isolation: An application to a nonlinear case study, Automatica42(1): 109-116.10.1016/j.automatica.2005.08.018Search in Google Scholar

Mosterman, P.J. and Biswas, G. (1999). Diagnosis of continuous valued systems in transient operating regions, IEEETransactions on Systems, Man and Cybernetics, Part A29(6): 554-565.10.1109/3468.798059Search in Google Scholar

Patton, R.J., Frank, P.M. and Clark, R.N. (2000). Issues of FaultDiagnosis for Dynamic Systems, Springer, Berlin.10.1007/978-1-4471-3644-6Search in Google Scholar

Sorsa, T. and Koivo, H.N. (1993). Application of artificial neural networks in process fault diagnosis, Automatica29(4): 843-849.10.1016/0005-1098(93)90090-GSearch in Google Scholar

Staroswiecki, M., Cassar, J.P. and Declerck, P. (2000). A structural framework for the design of FDI system in large scale industrial plants, in R.J. Patton, P.M. Frank and R.N. Clark (Eds.), Issues of Fault Diagnosis for Dynamic Systems, Springer-Verlag, Berlin.10.1007/978-1-4471-3644-6_9Search in Google Scholar

Verde, C., Gentil, S. and Rosas, O. (2001). Fuzzy directional residuals evaluation for multileaks in pipelines, EuropeanControl Conference, Porto, Portugal, pp. 504-509.Search in Google Scholar

Watanabe, K. and Hirota, S. (1991). Incipient diagnosis of multiple faults in chemical process via hierarchical artificial neural networks: Industrial electronics, control and instrumentation, IECON International Conference,Kobe, Japan, Vol. 2, pp. 1500-1505.Search in Google Scholar

Watanabe, K. and Hou, L. (1992). An optimal neural network for diagnosing multiple faults in chemical processes. industrial electronics, control and instrumentation, Proceedingsof the International Conference on Power Electronicsand Motion Control, San Diego, CA, USA, Vol. 2, pp. 1068-1073.10.1109/IECON.1992.254464Search in Google Scholar

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