References
- Apt K. (2003). Principles of Constraint Programming, Cambridge University Press, Amsterdam, The Netherlands.
- Blanke M., Kinnaert M., Lunze J. and Staroswiecki M. (2006). Diagnosis and Fault-tolerant Control, Springer-Verlag, Berlin/Heidelberg.
- Bollobás B. (1998). Modern Graph Theory, Springer, New York, NY.
- Chittaro L. and Ranon R. (2004). Hierarchical model-based diagnosis based on structural abstraction, Artificial Intelligence 155(1-2): 147-182.
- Commault C., Dion J.-M. and Yacoub Agha S. (2006). Structural analysis for the sensor location problem in fault detection and isolation, Proceedings of the IFAC Symposium SAFEPROCESS' 2006 Beijing, China, CD-ROM.
- Console L., Picardi C. and Ribando M. (2000). Diagnosis and diagnosability analysis using process algebra, Proceedings of the 11th International Workshop on Principles of Diagnosis DX'2000, Morelia, Mexico.
- Cordier M.-O., Dague P., Lévy F., Dumas M., Montmain J., Staroswiecki M. and Travé-Massuyès L. (2000). A comparative analysis of AI and control theory approaches to model-based diagnosis, Proceedings of the IFAC Symposium SAFEPROCESS 2000, Budapest, Hungary, pp. 329-334.
- Dalton T., Klotzek P. and Frank P. (1999). Application of sensitivity theory to fuzzy logic based FDI, International Journal of Applied Mathematics and Computer Science 9(3): 619-636.
- de Kleer J. and Williams B. C. (1992). Diagnosis with behavioral modes, in (W. Hamscher, L. Console and J. de Kleer, Eds.), Readings in Model-Based Diagnosis, Morgan Kaufmann Publishers Inc., San Francisco, CA, pp. 124-130.
- Dulmage A. L. and Mendelsohn N. S. (1959). A structure theory of bi-partite graphs of finite exterior extension, Transactions of the Royal Society of Canada 53(III): 1-13.
- Frisk E. and Krysander M. (2007). Sensor placement for maximum fault isolability, Proceedings of the 18th International Workshop on Principles of Diagnosis (DX-07), Nashville, TN, USA.
- Hamscher W., Console L. and De Kleer J. (1992). Readings in Model-Based Diagnosis, Morgan Kaufmann Publishers Inc., San Francisco, CA.
- Korbicz J., Patan K. and Obuchowicz A. (1999). Dynamic neural networks for process modelling in fault detection and isolation, International Journal of Applied Mathematics and Computer Science 9(3): 519-546.
- Koscielny J., Syfert M. and Bartys M. (1999). Fuzzy logic faut diagnosis of industrial process actuators, International Journal of Applied Mathematics and Computer Science 9(3): 653-666.
- Krysander M., Aslund J. and Nyberg M. (2008). An efficient algorithm for finding minimal overconstrained subsystems for model-based diagnosis, IEEE Transactions on Systems, Man and Cybernetics, Part A 38(1): 197-206.
- Lopez-Toribio C., Patton R. and Uppal F. (1999). Artificial intelligence approaches to fault diagnosis for dynamic systems, International Journal of Applied Mathematics and Computer Science 9(3): 471-518.
- Madron F. and Veverka V. (1992). Optimal selection of measuring points in complex plants by linear models, AIChE Journal 38(2): 227-236.
- Maquin D., Luong M. and Ragot J. (1997). Fault detection and isolation and sensor network design, European Journal of Automation 31(2): 393-406.
- Nyberg M. and Krysander M. (2003). Combining AI, FDI, and statistical hypothesis-testing in a framework for diagnosis, Proceedings of the IFAC Symposium SAFEPROCESS'03, Washington, DC, USA, pp. 813-818.
- Ploix S., Désinde M. and Touaf S. (2005). Automatic design of detection tests in complex dynamic systems, Proceedings of the 16th IFAC World Congress, Prague, Czech Republic.
- Ploix S., Touaf S. and Flaus J. M. (2003). A logical framework for isolation in fault diagnosis, Proceedings of the IFAC Symposium SAFEPROCESS'2003, Washington, DC, USA.
- Pothen A. and Chin-Ju F. (1990). Computing the block triangular form of a sparse matrix, ACM Transactions on Mathematical Software 16(4): 303-324.
- Pulido B. and Alonso C. (2002). Possible conflicts, arrs, and conflicts, Proceedings of the 13th International Workshop on Principles of Diagnosis (DX02), Semmering, Austria, pp. 122-128.
- Shumsky A. (2007). Redundancy relations for fault diagnosis in nonlinear uncertain systems, International Journal of Applied Mathematics and Computer Science 17(4): 477-489.
- Struss P. (1992). What's in SD? Towards a theory of modeling for diagnosis, in (W. Hamscher, L. Console and J. de Kleer, Eds.), Readings in model-based diagnosis, Morgan Kaufmann Publishers Inc., San Francisco, CA, pp. 419-449.
- Struss P., Rehfus B., Brignolo R., Cascio F., Console L., Dague P., Dubois P., Dressler O. and Millet D. (2002). Modelbased tools for the integration of design and diagnosis into a common process — A project report, Proceedings of the International Workshop on Principles of Diagnosis DX'02, Semmering, Austria, pp. 25-32.
- Travé-Massuyès L., Escobet T. and Milne R. (2001). Model-based diagnosability and sensor placement application to a frame 6 gas turbine subsystem, Proceedings of the 12th International Workshop on Principles of Diagnosis, Sansicario, Via Lattea, Italy, pp. 205-212.
- Witczak M. (2006). Advances in model-based fault diagnosis with evolutionary algorithms and neural networks, International Journal of Applied Mathematics and Computer Science 16(1): 85-99.