Fault isolability with different forms of the faults–symptoms relation

Kóscielny, Jan Maciej; Syfert, Michał; Rostek, Kornel; Sztyber, Anna

doi:10.1515/amcs-2016-0058

Abstract

The definitions and conditions for fault isolability of single faults for various forms of the diagnostic relation are reviewed. Fault isolability and unisolability on the basis of a binary diagnostic matrix are analyzed. Definitions for conditional and unconditional isolability and unisolability on the basis of a fault information system (FIS), symptom sequences and directional residuals are formulated. General definitions for conditional and unconditional isolability and unisolability in the cases of simultaneous evaluation of diagnostic signal values and a sequence of symptoms are provided. A comprehensive example is discussed.

References

Bartýs, 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-2013-0031.10.2478/amcs-2013-0031
Search in Google Scholar Back to article
Basseville, M. (1997). Information criteria for residual generation and fault detection and isolation, Automatica 33(5): 783-803, DOI: 10.1016/S0005-1098(97)00004-6.10.1016/S0005-1098(97)00004-6
Search in Google Scholar Back to article
Basseville, M. (1999). On fault detectability and isolability, 1999 European Control Conference (ECC), Karlsruhe, Germany, pp. 385-390.
Search in Google Scholar Back to article
Chen, J. and Patton, R.J. (1999). Robust Model-based Fault Diagnosis for Dynamic Systems, Springer Science & Business Media, New York, NY.10.1007/978-1-4615-5149-2
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
Ding, S.X. (2008). Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools, Springer Science & Business Media, London.
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
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.2013.02.045.10.1016/j.automatica.2013.02.045
Search in Google Scholar Back to article
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.2012.2189877.10.1109/TSMCA.2012.2189877
Search in Google Scholar Back to article
Gertler, J. (1998). Fault Detection and Diagnosis in Engineering Systems, CRC Press, New York, NY.
Search in Google Scholar Back to article
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. 273-291.10.1007/978-94-010-0550-0_13
Search in Google Scholar Back to article
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.2013.2251891.10.1109/TII.2013.2251891
Search in Google Scholar Back to article
Isermann, R. (2006). Fault-Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance, Springer Science & Business Media, Berlin/Heidelberg.10.1007/3-540-30368-5
Search in Google Scholar Back to article
Koivo, H. (1994). Artificial neural networks in fault diagnosis and control, Control Engineering Practice 2(1): 89-101. DOI: 10.1016/0967-0661(94)90577-0.10.1016/0967-0661(94)90577-0
Search in Google Scholar Back to article
Korbicz, J., Kóscielny, J.M., Kowalczuk, Z. and Cholewa, W. (Eds.) (2004). Fault Diagnosis: Models, Artificial Intelligence, Applications, Springer Science & Business Media, Berlin/Heidelberg.10.1007/978-3-642-18615-8
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
Kościelny, J.M., Bartýs, 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.
Search in Google Scholar Back to article
Kościelny, J.M. and Łab˛eda-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-2013-0030.10.2478/amcs-2013-0030
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
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.2008.2003968.10.1109/TSMCA.2008.2003968
Search in Google Scholar Back to article
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-2015-0004.10.1515/amcs-2015-0004
Search in Google Scholar Back to article
Patton, R.J., Frank, P.M. and Clark, R.N. (2000). Issues of Fault Diagnosis for Dynamic Systems, Springer Science & Business Media, London.10.1007/978-1-4471-3644-6
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
Reiter, R. (1987). A theory of diagnosis from first principles, Artificial Intelligence 32(1): 57-95, DOI: 10.1016/0004-3702(87)90062-2.10.1016/0004-3702(87)90062-2
Search in Google Scholar Back to article
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-2003.00015.10.3182/20090630-4-ES-2003.00015
Search in Google Scholar Back to article
Sztyber, A., Ostasz, A. and Kóscielny, 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.2014.2384000.10.1109/TSMC.2014.2384000
Search in Google Scholar Back to article
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.2013.09.018.10.1016/j.engappai.2013.09.018
Search in Google Scholar Back to article
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.2006.878984.10.1109/TSMCA.2006.878984
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
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.2015.2481318.10.1109/TCST.2015.2481318
Search in Google Scholar Back to article
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.2015.2498194.10.1109/TCYB.2015.249819426600561
Search in Google Scholar Back to article

Fault isolability with different forms of the faults–symptoms relation

Abstract

Paradigm

My account