The comparison of fault distinguishability approaches – case study

• Autorzy: Kościelny J. M. , Bartyś M. , Rostek K.

Identyfikatory

DOI

10.24425/bpasts.2019.131566

• Języki publikacji: EN

Abstrakty

EN

A comprehensive characterization of four selected fault distinguishability methods is presented herein. All considered methods are derived from structural residual approaches referring to mdel-based diagnostics. In particular, these methods, are based on binary diagnostic matric, foult isolation system, sequences of symptoms, and their combination. fault distinguishability issues are discussed based on an example of four pressure vessels system. Substantian benefit are shown in fault distinguishability figures obtained by utilising extended knowledge regarding foult-symptom relation. Finally, the values of three fault distinguishability metrix are calculated for each method. For the case study, the highest score is achived using the multivalued foult isolation method combined with a diagnosis utilising information regarding the antecedence of symptoms.

Słowa kluczowe

EN

fault detection and isolation; fault distinguishability; diagnostics of industrial processes; metrics of distinguishability; antecedence of symptoms

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2019
• Tom: Vol. 67, nr 6
• Strony: 1059--1068
• Opis fizyczny: Bibliogr. 35 poz. rys., tab.

Twórcy

autor

Kościelny J. M.

• Warsaw University of Technology, św. A. Boboli 8, 02-525 Warsaw, Poland

autor

Bartyś M.

• Warsaw University of Technology, św. A. Boboli 8, 02-525 Warsaw, Poland

autor

Rostek K.

• Warsaw University of Technology, św. A. Boboli 8, 02-525 Warsaw, Poland

Bibliografia

• [1] J.Korbicz, “Robust fault detection using analytical and soft computing methods”, Bull.Pol.Ac.: Tech. 54(1), 75–88 (2006).
• [2] J.M. Kościelny, M. Syfert, K. Rostekand A. Sztyber, “Fault isolability with different forms of faults-symptoms relation”, Int. J.of Appl. Math.a nd Comp. Sc. 26 (4), 815–826 (2016).
• [3] J. Gertler, “Fault detection and isolation using parity relations”, Contr. Eng. Pract. 5(5), 653–661 (1997).
• [4] J. Gertler. Fault Detection and Diagnosis in Engineering Systems, Marcel Dekker Inc., 1998.
• [5] M. Blanke and M. Staroswiecki, “Structural design of systems with safe behavior under single and multiple faults”, IFAC Symposium Safe process, 511–515 (2006).
• [6] R. Isermann, Fault Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance, Springer, 2006.
• [7] L. Travè-Massuyès, T. Escobetand R. Milne, “Diagnosability analysis based on component-supported analytical redundancy relations”, IEEE Trans. On Syst., Manand Cyber., Part A: Systems and Humans 36 (6), 1146–1160 (2006).
• [8] M. Basseville, “On fault detectability and isolability” ,Europ. J. of Contr. 7(6), 625–637 (2001).
• [9] J.M. Kościelny, M. Bartyś, P. Rzepiejewski and J.M. da Costa, “Actuator fault distinguishability study of the damadics benchmark problem”, Contr. Eng. Pract. 14 (6), 645–652 (2006).
• [10] A.Sztyber, A. Ostaszand J.M. Kościelny, “Graph of a process–a new tool for finding model’s structures in model based diagnosis”, IEEE Tr. On Sys., Man, and Cyb.: Sys. 45 (7), 1004–1017 (2015).
• [11] J.M. Kościelny, Diagnostyka zautomatyzowanych procesów prze-mysłowych, Akad. Oficyna Wyd. Exit, Warszawa, 2001.
• [12] J.M. Kościelny, D. Sędziak and K. Zakroczymski, “Fuzzy logic fault isolation in large scale systems”, Int. Journal of Applied Math. And Comp. Sc. 9(3), 637–652 (1999).
• [13] J. Korbicz, J.M. Kościelny, Z. Kowalczuk and W. Cholewa (Eds.), Fault Diagnosis. Models, Artificial Intelligence, Applications, Springer, 2004.
• [14] M. Syfertand J.M. Kościelny, “Diagnostic reasoning based on symptom forming sequence”, IFAC Symp. On Fault Detection, Supervision and Safety of Technical Processes, 89–94( 2009).
• [15] R.A. Reiter, “Theory of diagnosis from first principles”, Artificial Intelligence 32 (1), 57–95 (1987)
• [16] J. de Kleer, A.K. Mackworth and R. Reiter, “Characterizing diagnoses and systems”, Artificial Intelligence 56(2), 197–222 (1992).
• [17] B. Górny and A. Ligęza, “Model-based diagnosis of dynamic systems: Systematic conflict generation”, Logical and Computational aspects Model-Based Reasoning, vol. 25, 273-291, Springer, 2002.
• [18] B. Pulidoand C.A. González, “Possible conflicts: A compilation technique for consistency-based diagnosis”, IEEE Trans. On Syst., Man, and Cyb. B: Cybernetics 34 (5), 2192–2206 (2004).
• [19] M.O. Cordier, P. Dague, F. Lévy, J. Montmain, M.Staroswiecki and L. Travé-Massuyés, “Conflicts versus analytical redundancy relations: A comparative analysis of the model based diagnosis approach from the artificial intelligence and automatic control perspectives”, IEEE Trans. On Syst., Man, and Cyber. B: Cybernetics 34 (5), 2163–2177 (2004).
• [20] S. Vazquez, S.M. Lukic, E. Galvan, L.G. Franquelo and J.M. Carrasco, “Energy storage systems for transport and grid applications”, IEEE Trans. On Ind. Electr. 57, 3881–3895 (2010).
• [21] A. Setiawan, A. Priyadi, M. Pujiantara and M.H. Purnom, “Sizing Compressed-Air Energy Storage Tanks for Solar Home Systems”, IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), Shenzhen, China, DOI:10.1109/CIVEMSA.2015.7158620 (2015).
• [22] B. Shibata, S. Tateno, Y. Tsuge and H. Matsuyama, “Fault diagnosis of the chemicall process utilizing the signed directed graph–improvement and evaluation of the diagnosis accuracy”, IFAC Symposium Safe process (2), 381–386 (1991).
• [23] K. Takeda, B. Shibata, Y. Tsuge and H. Matsuyama, “The improvement of fault diagnosis algorithm using signed directed graph”, IFAC Symposium Safe process (1), 368–373 (1994).
• [24] J.M. Kościelny and M. Syfert, “Fuzzy diagnostic reasoning that take sin to account the uncertainty of the faults-symptoms relation”, Int. J. of Appl. Math. And Comp. Sc. 16(1), 27–35 (2006).
• [25] M. Bartyś, “Single faul tisolability metrics of the binary isolating structures”, Advances and Intelligent Computation sin Diagnosis and Control (386), 61–75, Springer 2016.
• [26] M. Bartyś, “Generalised reasoning about faults based on diagnostic matrix”, Int. J. of Appl. Math. And Comp. Sc. 23 (2), 407–417 (2013).
• [27] K. Rostek, “Influence of multi-valued diagnostic signals on optimal sensor placement”, IOP Conference Series, J. of Physics: Conference Series (783) (2017).
• [28] C. B. Low, D. Wang, S. Arogeti and M. Luo, “Quantitative hybrid bond graph based fault detection and isolation”, IEEE Trans. On Autom. Sc. And Eng. 7 (3), 558–569, (2010).
• [29] S. Benmoussa, B.O. Bouamama and R. Merzouki, “Bond graph approach for plant fault detection and isolation: Application to intelligent autonomous vehicle”, IEEE Trans. On Autom. Sc. And Eng. 11 (2), 585–593 (2014).
• [30] D. Jung, “A generalized fault isolability matrix for improved fault diagnosability analysis”, Conference on Control and Fault-Tolerant Systems (SysTol), 519–524 (2016).
• [31] M. Bartyś, R. Patton, M. Syfert, S.delas Heras and J.Quevedo, “Introduction to the Damadics actuator FDI benchmark study”, Contr. Eng. Pract. 14 (6), 577–596 (2006).
• [32] K. Rostek, “Optimal sensor placement for fault information system”, Adv. Mechatr. Sol., Springer Int. Publ., 67–72 (2016).
• [33] G. Chi, D. Wang, T. Le, M. Yu and M. Luo, “Sensor placement for fault isolability using low complexity dynamic programming”, IEEE Trans .on Aut. Sc. And Eng. 12(3), 1080–1091 (2015).
• [34] G. Chiand D. Wang, “Sensor placement for fault isolability based on bond graphs”, IEEE Tr. Aut. Con. 60(11), 3041–3046 (2015).
• [35] J.M. Kościelny and M. Syfert, “Application properties of methods for fault detection and isolation in the diagnosis of complex large-scale processes”, Bull. Pol. Ac.: Tech. 62 (3), 571–582 (2014).

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).