Pełnotekstowe zasoby PLDML oraz innych baz dziedzinowych są już dostępne w nowej Bibliotece Nauki.
Zapraszamy na https://bibliotekanauki.pl

PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2016 | 26 | 4 | 815-826

Tytuł artykułu

Fault isolability with different forms of the faults-symptoms relation

Treść / Zawartość

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
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.

Rocznik

Tom

26

Numer

4

Strony

815-826

Opis fizyczny

Daty

wydano
2016
otrzymano
2016-01-18
poprawiono
2016-06-08
poprawiono
2016-07-19
zaakceptowano
2016-08-14

Twórcy

  • Institute of Automatic Control and Robotics, Warsaw University of Technology, ul. Św. Andrzeja Boboli 8, 02-525 Warsaw, Poland
  • Institute of Automatic Control and Robotics, Warsaw University of Technology, ul. Św. Andrzeja Boboli 8, 02-525 Warsaw, Poland
  • Institute of Automatic Control and Robotics, Warsaw University of Technology, ul. Św. Andrzeja Boboli 8, 02-525 Warsaw, Poland
autor
  • Institute of Automatic Control and Robotics, Warsaw University of Technology, ul. Św. Andrzeja Boboli 8, 02-525 Warsaw, Poland

Bibliografia

  • 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.
  • 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.
  • Basseville, M. (1999). On fault detectability and isolability, 1999 European Control Conference (ECC), Karlsruhe, Germany, pp. 385-390.
  • Chen, J. and Patton, R.J. (1999). Robust Model-based Fault Diagnosis for Dynamic Systems, Springer Science & Business Media, New York, NY.
  • 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.
  • 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.
  • Ding, S.X. (2008). Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools, Springer Science & Business Media, London.
  • 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.
  • 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.
  • 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.
  • Gertler, J. (1998). Fault Detection and Diagnosis in Engineering Systems, CRC Press, New York, NY.
  • 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.
  • 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.
  • Isermann, R. (2006). Fault-Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance, Springer Science & Business Media, Berlin/Heidelberg.
  • 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.
  • Korbicz, J., Kościelny, J.M., Kowalczuk, Z. and Cholewa, W. (Eds.) (2004). Fault Diagnosis: Models, Artificial Intelligence, Applications, Springer Science & Business Media, Berlin/Heidelberg.
  • 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.
  • 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.
  • 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.
  • 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 FaultTolerant Systems (SysTol), Nice, France, pp. 275-280.
  • 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.
  • 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.
  • Patton, R.J., Frank, P.M. and Clark, R.N. (2000). Issues of Fault Diagnosis for Dynamic Systems, Springer Science & Business Media, London.
  • 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.
  • 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.
  • Reiter, R. (1987). A theory of diagnosis from first principles, Artificial Intelligence 32(1): 57-95, DOI: 10.1016/0004-3702(87)90062-2.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.

Typ dokumentu

Bibliografia

Identyfikatory

Identyfikator YADDA

bwmeta1.element.bwnjournal-article-amcv26i4p815bwm
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.