Otwarty dostęp

Investigating The Problem of Misdiagnosis in Model–Based Fault Diagnosis

 oraz   
24 cze 2025

Zacytuj
Pobierz okładkę

Armengol, J., Bregón, A., Escobet, T., Gelso, E., Krysander, M., Nyberg, M., Olive, X., Pulido, B. and Travè-Massuyès, L. (2009). Minimal structurally overdetermined sets for residual generation: A comparison of alternative approaches, IFAC Proceedings Volumes 42(8): 1480–1485. Search in Google Scholar

Bartyś, M. (2013). Generalised reasoning about faults based on diagnostic matrix, International Journal of Applied Mathematics and Computer Science 23(2): 407–417. Search in Google Scholar

Bartyś, M. (2014). Selected Issues of Fault Isolation, Polish Scientific Publishers, Warsaw. Search in Google Scholar

Bartyś, M. (2021). Fault compensation effect in fault detection and isolation, Acta IMEKO 10(3): 45–53. Search in Google Scholar

Biswas, G., Kapadia, R. and Yu, X. (1997). Combined qualitative-quantitative steady-state diagnosis of continuous-valued systems, IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 27(2): 167–185. Search in Google Scholar

Blanke, M., Kinnaert, M., Lunze, J. and Staroswiecki, M. (2015). Diagnosis and Fault-Tolerant Control, Springer, New York. Search in Google Scholar

Bregón, A., Alonso-González, C.J. and Pulido, B. (2014). Integration of simulation and state observers for online fault detection of nonlinear continuous systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems 44(12): 1553–1568. Search in Google Scholar

Bregón, A., Biswas, G., Pulido, B., Alonso-Gonzalez, C. and Khorasgani, H. (2013). A common framework for compilation techniques applied to diagnosis of linear dynamic systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems 44(7): 863–876. Search in Google Scholar

Chen, J. and Patton, R. (1999). Robust model Based Fault Diagnosis for Dynamic Systems, Kluwer Akademic Publishers, Boston. Search in Google Scholar

Cordier, M., Dague, P., Lévy, F., Montmain, J., Staroswiecki, M. and Travé-Massuyés, 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. Search in Google Scholar

Daigle, M., Koutsoukos, X. and Biswas, G. (2009). A qualitative event-based approach to continuous systems diagnosis, IEEE Transactions on Control Systems Technology 17(4): 780–793. Search in Google Scholar

de Kleer, J. and Kurien, J. (2003). Fundamentals of model-based diagnosis, IFAC Proceedings Volumes 36(5): 25–36. Search in Google Scholar

de Kleer, J., Mackworth, A.K. and Reiter, R. (1992). Characterizing diagnoses and systems, Artificial Intelligence 56(2): 197–222. Search in Google Scholar

de Kleer, J. andWilliams, B. (1987). Diagnosing multiple faults, Artificial Intelligence 32(1): 97–130. Search in Google Scholar

Düstegör, 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. Search in Google Scholar

Eskandari, A., Nedaei, A., Milimonfared, J. and Aghaei, M. (2024). A multilayer integrative approach for diagnosis, classification and severity detection of electrical faults in photovoltaic arrays, Expert Systems with Applications 252(Part A): 124111. Search in Google Scholar

Frank, P. M. (1990). Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy, Automatica 26(3): 459–474. Search in Google Scholar

Gertler, J. (1991). Analitical redunduncy methods in fault detection and isolation, IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS’91, Baden-Baden, pp. 9–21. Search in Google Scholar

Gertler, J. (1998). Fault Detection and Diagnosis in Engineering Systems, Marcel Dekker, New York. Search in Google Scholar

Jia, F., Cao, F., Lyu, G. and He, X. (2023). A novel framework of cooperative design: Bringing active fault diagnosis into fault-tolerant control, IEEE Transactions on Cybernetics 53(5): 3301–3310. Search in Google Scholar

Korbicz, J., Kóscielny, J.M., Kowalczuk, Z. and Cholewa, W. (Eds) (2004). Fault Diagnosis. Models, Artificial Intelligence, Applications, Springer, Berlin. Search in Google Scholar

Kóscielny, J.M. (1995). Fault isolation in industrial processes by dynamic table of states method, Automatica 31(5): 747–753. Search in Google Scholar

Kóscielny, J.M. (1999). Application of fuzzy logic fault isolation in a three-tank system, IFAC Proceedings Volumens 32(2): 7754–7759. Search in Google Scholar

Kóscielny, J.M. and Bartyś, M. (2023). A new method of diagnostic row reasoning based on trivalent residuals, Expert Systems with Applications 214: 119116. Search in Google Scholar

Kóscielny, J.M., Bartyś, M. and Grudziak, Z. (2021). Tri–valued evaluation of residuals as a method of addressing the problem of fault compensation effect, in J. Korbicz, K. Patan and M. Luzar (Eds), Advances in Diagnostics of Processes and Systems, Springer, Cham, pp. 31–44. Search in Google Scholar

Kóscielny, J.M., Bartyś, M. and Rostek, K. (2019). The comparison of fault distinguishability approaches – Case study, Bulletin of the Polish Academy of Sciences Technical Sciences 67(6): 1059–1068. Search in Google Scholar

Kóscielny, J. M., Bartyś, M., Rzepiejewski, P. and da Costa, J. S. (2006). Actuator fault distinguishability study of the damadics benchmark problem, Control Engineering Practice 14(6): 645–652. Search in Google Scholar

Kóscielny, J.M., Bartyś, M. and Syfert, M. (2012). Methods of multiple fault isolation in large scale systems, IEEE Transactions On Control Systems Technology 20(5): 1302–1310. Search in Google Scholar

Kóscielny, J.M., Syfert, M., Rostek, K. and Sztyber, A. (2016). Fault isolability with different forms of faults-symptoms relation, International Journal of AppliedMathematics and Computer Science 26(4): 815–826. Search in Google Scholar

Kóscielny, J.M., Syfert, M. and Wnuk, P. (2021). Diagnostic row reasoning method based on multiple-valued evaluation of residuals and elementary symptoms sequence, Energies 14(2476). Search in Google Scholar

Krysander, M., Aslund, J. and Nyberg, M. (2007). An efficient algorithm for finding minimal overconstrained subsystems for model-based diagnosis, IEEE Transactions on Systems, Man, and Cybernetics–Part A: Systems and Humans 38(1): 197–206. Search in Google Scholar

Kunpeng, Z., Bin, J., Fuyang, C. and Hui, Y. (2023). Directed-graph-learning-based diagnosis of multiple faults for high speed train with switched dynamics, IEEE Transactions on Cybernetics 53(3): 1712–1724. Search in Google Scholar

Liu, J., Wang, X., Wu, S., Wan, L. and Xie, F. (2023). Wind turbine fault detection based on deep residual networks, Expert Systems with Applications 213: 119102. Search in Google Scholar

Pawlak, Z. (1991). Rough Sets. Theoretical Aspects of Reasoning About Data, Kluwer Academic Publishers, Boston. Search in Google Scholar

Puig, V., Schmid, F., Quevedo, J. and Pulido, B. (2005). A new fault diagnosis algorithm that improves the integration of fault detection and isolation, 44th IEEE Conference on Decision and Control, Seville, Spain, pp. 3809–3814. Search in Google Scholar

Pulido, B. and González, C. (2004). Possible conflicts: a compilation technique for consistency-based diagnosis, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 34(5): 2192–2206. Search in Google Scholar

Reiter, R.A. (1987). Theory of diagnosis from first principles, Artificial Intelligence 32(1): 57–95. Search in Google Scholar

Song, Q. and Jiang, P. (2022). A multi-scale convolutional neural network based fault diagnosis model for complex chemical processes, Process Safety and Environmental Protection 159: 575–584. Search in Google Scholar

Struss, P. and Dressier, O. (1992). “Physical negation”: Integrating fault models into the general diagnostic system, Proceedings of the 11th International Joint Conference on Artificial Intelligence, Vol.2, pp. 1318–1323. Search in Google Scholar

Su, J. and Chen, W. (2019). Model-Based Fault Diagnosis System Verification Using Reachability Analysis, IEEE Transactions on Systems, Man, and Cybernetics: Systems 49(4): 742–751. Search in Google Scholar

Tatara, M.S. and Kowalczuk, Z. (2024). Approximate and analytic flow models for leak detection and identification, International Journal of Applied Mathematics and Computer Science 34(3): 391–407. Search in Google Scholar

Travè-Massuyès, L. (2014). Bridges between diagnosis theories from control and AI perspectives, in J. Korbicz and M. Kowal (Eds), Intelligent Systems in Technical and Medical Diagnostics, Berlin/Heidelberg, pp. 3–28. Search in Google Scholar

Xia, D. and Fu, X. (2024). Observer-based sliding-mode fault-tolerant consistent control for hybrid event-triggered multi-agent systems, International Journal of Applied Mathematics and Computer Science 34(3): 361–373. Search in Google Scholar

Zheng, S. and Zhao, J. (2022). High-fidelity positive-unlabeled deep learning for semi-supervised fault detection of chemical processes, Process Safety and Environmental Protection 165: 191–204. Search in Google Scholar

Język:
Angielski
Częstotliwość wydawania:
4 razy w roku
Dziedziny czasopisma:
Matematyka, Matematyka stosowana