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@article{IJAMCS_2016_26_4_a7, author = {Ko\'scielny, J. M. and Syfert, M. and Rostek, K. and Sztyber, A.}, title = {Fault isolability with different forms of the faults{\textendash}symptoms relation}, journal = {International Journal of Applied Mathematics and Computer Science}, pages = {815--826}, publisher = {mathdoc}, volume = {26}, number = {4}, year = {2016}, language = {en}, url = {http://geodesic.mathdoc.fr/item/IJAMCS_2016_26_4_a7/} }
TY - JOUR AU - Kościelny, J. M. AU - Syfert, M. AU - Rostek, K. AU - Sztyber, A. TI - Fault isolability with different forms of the faults–symptoms relation JO - International Journal of Applied Mathematics and Computer Science PY - 2016 SP - 815 EP - 826 VL - 26 IS - 4 PB - mathdoc UR - http://geodesic.mathdoc.fr/item/IJAMCS_2016_26_4_a7/ LA - en ID - IJAMCS_2016_26_4_a7 ER -
%0 Journal Article %A Kościelny, J. M. %A Syfert, M. %A Rostek, K. %A Sztyber, A. %T Fault isolability with different forms of the faults–symptoms relation %J International Journal of Applied Mathematics and Computer Science %D 2016 %P 815-826 %V 26 %N 4 %I mathdoc %U http://geodesic.mathdoc.fr/item/IJAMCS_2016_26_4_a7/ %G en %F IJAMCS_2016_26_4_a7
Kościelny, J. M.; Syfert, M.; Rostek, K.; Sztyber, A. Fault isolability with different forms of the faults–symptoms relation. International Journal of Applied Mathematics and Computer Science, Tome 26 (2016) no. 4, pp. 815-826. http://geodesic.mathdoc.fr/item/IJAMCS_2016_26_4_a7/
[1] Bartyś, M. (2013). Generalized reasoning about faults based on the diagnostic matrix, International Journal of Applied Mathematics and Computer Science 23(2): 407–417, DOI: 10.2478/amcs-2013-0031.
[2] Basseville, M. (1997). Information criteria for residual generation and fault detection and isolation, Automatica 33(5): 783–803, DOI: 10.1016/S0005-1098(97)00004-6.
[3] Basseville, M. (1999). On fault detectability and isolability, 1999 European Control Conference (ECC), Karlsruhe, Germany, pp. 385–390.
[4] Chen, J. and Patton, R.J. (1999). Robust Model-based Fault Diagnosis for Dynamic Systems, Springer Science Business Media, New York, NY.
[5] Cordier, M.-O., Dague, P., Lévy, F., Montmain, J., Staroswiecki, M. and Travé-Massuyès, L. (2004). Conflicts versus analytical redundancy relations: A comparative analysis of the model based diagnosis approach from the artificial intelligence and automatic control perspectives, IEEE Transactions on Systems, Man, and Cybernetics B: Cybernetics 34(5): 2163–2177, DOI: 10.1109/TSMCB.2004.835010.
[6] De Kleer, J., Mackworth, A.K. and Reiter, R. (1992). Characterizing diagnoses and systems, Artificial Intelligence 56(2): 197–222, DOI: 10.1016/0004-3702(92)90027-U.
[7] Ding, S.X. (2008). Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools, Springer Science Business Media, London.
[8] Düştegör, D., Frisk, E., Cocquempot, V., Krysander, M. and Staroswiecki, M. (2006). Structural analysis of fault isolability in the damadics benchmark, Control Engineering Practice 14(6): 597–608, DOI: 10.1016/j.conengprac.2005.04.008.
[9] Eriksson, D., Frisk, E. and Krysander, M. (2013). A method for quantitative fault diagnosability analysis of stochastic linear descriptor models, Automatica 49(6): 1591–1600, DOI: 10.1016/j.automatica.2013.02.045.
[10] Frisk, E., Bregon, A., Åslund, J., Krysander, M., Pulido, B. and Biswas, G. (2012). Diagnosability analysis considering causal interpretations for differential constraints, IEEE Transactions on Systems, Man and Cybernetics A: Systems and Humans 42(5): 1216–1229, DOI: 10.1109/TSMCA.2012.2189877.
[11] Gertler, J. (1998). Fault Detection and Diagnosis in Engineering Systems, CRC Press, New York, NY.
[12] Górny, B. and Ligęza, A. (2002). Model-based diagnosis of dynamic systems: Systematic conflict generation, in L. Magnani et al. (Eds.), Logical and Computational Aspects of Model-Based Reasoning, Springer, Dordrecht, pp. 273–291.
[13] He, X.,Wang, Z., Liu, Y. and Zhou, D. H. (2013). Least-squares fault detection and diagnosis for networked sensing systems using a direct state estimation approach, IEEE Transactions on Industrial Informatics 9(3): 1670–1679, DOI: 10.1109/TII.2013.2251891.
[14] Isermann, R. (2006). Fault-Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance, Springer Science Business Media, Berlin/Heidelberg.
[15] Koivo, H. (1994). Artificial neural networks in fault diagnosis and control, Control Engineering Practice 2(1): 89–101. DOI: 10.1016/0967-0661(94)90577-0.
[16] Korbicz, J., Kościelny, J.M., Kowalczuk, Z. and Cholewa, W. (Eds.) (2004). Fault Diagnosis: Models, Artificial Intelligence, Applications, Springer Science Business Media, Berlin/Heidelberg.
[17] Kościelny, J.M. (1999). Application of fuzzy logic for fault isolation in a three-tank system, 14th IFAC World Congress, Beijing, China, pp. 73–78.
[18] Kościelny, J.M., Bartyś, M., Rzepiejewski, P. and Sa Da Costa, J. (2006). Actuator fault distinguishability study for the damadics benchmark problem, Control Engineering Practice 14(6): 645–652, DOI: 10.1016/j.conengprac.2005.06.014.
[19] Kościelny, J.M. and Łabęda-Grudziak, Z.M. (2013). Double fault distinguishability in linear systems, International Journal of Applied Mathematics and Computer Science 23(2): 395–406, DOI: 10.2478/amcs-2013-0030.
[20] Kościelny, J.M., Syfert, M. and Tabor, Ł. (2013). Application of knowledge about residual dynamics for fault isolation and identification, 2013 Conference on Control and Fault-Tolerant Systems (SysTol), Nice, France, pp. 275–280.
[21] Krysander, M. and Frisk, E. (2008). Sensor placement for fault diagnosis, IEEE Transactions on Systems, Man and Cybernetics A: Systems and Humans 38(6): 1398–1410, DOI: 10.1109/TSMCA.2008.2003968.
[22] Ossmann, D. and Varga, A. (2015). Detection and identification of loss of efficiency faults of flight actuators, International Journal of Applied Mathematics and Computer Science 25(1): 53–63, DOI: 10.1515/amcs-2015-0004.
[23] Patton, R.J., Frank, P.M. and Clark, R.N. (2000). Issues of Fault Diagnosis for Dynamic Systems, Springer Science Business Media, London.
[24] Patton, R.J., Lopez-Toribio, C.J. and Uppal, F.J. (1999). Artificial intelligence approaches to fault diagnosis for dynamic systems, International Journal of Applied Mathematics and Computer Science 9(3): 471–518.
[25] Pulido, B. and González, C.A. (2004). Possible conflicts: A compilation technique for consistency-based diagnosis, IEEE Transactions on Systems, Man, and Cybernetics B: Cybernetics 34(5): 2192–2206, DOI: 10.1109/TSMCB.2004.835007.
[26] Reiter, R. (1987). A theory of diagnosis from first principles, Artificial Intelligence 32(1): 57–95, DOI: 10.1016/0004-3702(87)90062-2.
[27] Syfert, M. and Koscielny, J.M. (2009). Diagnostic reasoning based on symptom forming sequence, IFAC Proceedings Volumes 42(8): 89–94, DOI: 10.3182/20090630-4-ES-2003.00015.
[28] Sztyber, A., Ostasz, A. and Kościelny, J.M. (2015). Graph of a process—a new tool for finding model structures in a model-based diagnosis, IEEE Transactions on Systems, Man, and Cybernetics: Systems 45(7): 1004–1017, DOI: 10.1109/TSMC.2014.2384000.
[29] Travé-Massuyès, L. (2014). Bridging control and artificial intelligence theories for diagnosis: A survey, Engineering Applications of Artificial Intelligence 27: 1–16, DOI: 10.1016/j.engappai.2013.09.018.
[30] Travé-Massuyes, L., Escobet, T. and Olive, X. (2006). Diagnosability analysis based on component-supported analytical redundancy relations, IEEE Transactions on Systems, Man and Cybernetics A: Systems and Humans 36(6): 1146–1160, DOI: 10.1109/TSMCA.2006.878984.
[31] Yin, S. and Huang, Z. (2015). Performance monitoring for vehicle suspension system via fuzzy positivistic c-means clustering based on accelerometer measurements, IEEE/ASME Transactions on Mechatronics 20(5): 2613–2620, DOI: 10.1109/ TMECH.2014.2358674.
[32] Yin, S., Wang, G. and Gao, H. (2016). Data-driven process monitoring based on modified orthogonal projections to latent structures, IEEE Transactions on Control Systems Technology 24(4): 1480–1487, DOI: 10.1109/TCST.2015.2481318.
[33] Yin, S., Xie, X., Lam, J., Cheung, K.C. and Gao, H. (2015). An improved incremental learning approach for KPI prognosis of dynamic fuel cell system, IEEE Transactions on Cybernetics PP(99): 1–10, DOI: 10.1109/TCYB.2015.2498194.