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): 1553–1568.10.1109/TSMC.2014.2322581
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): 863–876.10.1109/TSMC.2013.2284577
Chanthery, E., Sztyber, A., Louise Travé-Massuyés, C. and Perez-Zuñiga, C.G. (2020). Process decomposition and test selection for distributed fault diagnosis, in H. Fujita et al. (Eds), Trends in Artificial Intelligence Theory and Applications: Artificial Intelligence Practices, Springer, Cham, pp. 914–925.10.1007/978-3-030-55789-8_78
Cho, S. and Jiang, J. (2019). A fault detection and isolation technique using nonlinear support vectors dichotomizing multi-class parity space residuals, Journal of Process Control 82: 31–43.10.1016/j.jprocont.2019.07.006
Chow, E. and Willsky, A. (1984). Analytical redundancy and the design of robust failure detection systems, IEEE Transactions on Automatic Control 29(3): 603–614.10.1109/TAC.1984.1103593
Cordier, M.O., 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): 2163–2177.10.1109/TSMCB.2004.83501015503514
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): 780–793.10.1109/TCST.2008.2011648
Ding, S. (2014). Data-driven design of monitoring and diagnosis systems for dynamic processes: A review of subspace technique based schemes and some recent results, Journal of Process Control 24(2): 431–449.10.1016/j.jprocont.2013.08.011
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): 597–608.10.1016/j.conengprac.2005.04.008
Frank, P. (1987). Fault diagnosis in dynamic systems via state estimations methods. A survey, in S. Tzafestas et al. (Eds), System Fault Diagnostics, Reliability and Related Knowledge-Based Approaches, Springer, Dordrecht, pp. 35–98.
Frank, P.M. (1990). Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy, Automatica 26(3): 459–474.10.1016/0005-1098(90)90018-D
Frisk, E. and Krysander, M. (2007). Sensor placement for maximum fault isolability, 18th International Workshop on Principles of Diagnosis (DX-07), Nashville, USA, pp. 106–113.
Greiner, R., Smith, B. and Wilkerson, R. (1989). A correction to the algorithm in Reiter’s theory of diagnosis, Artificial Intelligence 41(1): 79–88.10.1016/0004-3702(89)90079-9
Hamdi, H., Rodrigues, M., Rabaoui, B. and Benhadj Braiek, N. (2021). A fault estimation and fault-tolerant control based sliding mode observer for LPV descriptor systems with time delay, International Journal of Applied Mathematics and Computer Science 31(2): 247–258, DOI: 10.34768/amcs-2021-0017.
Iri, M., Aoki, K., O’Shima, E. and Matsuyama, H. (1979). An algorithm for diagnosis of system failures in the chemical process, Computers & Chemical Engineering 3(1–4): 489–493.10.1016/0098-1354(79)80079-4
Jakobsson, E., Petterson, R., Frisk, E. and Krysander, M. (2020). Fatigue damage monitoring for mining vehicles using data driven models, International Journal of Prognostics on and Health Management 11(1): 1–15.
Jung, D. (2020). Data-driven open-set fault classification of residual data using Bayesian filtering, IEEE Transactions on Control Systems Technology 28(5): 2045–2052.10.1109/TCST.2020.2997648
Jung, D., Frisk, E. and Krysander, M. (2015). Quantitative isolability analysis of different fault modes, IFACPapersOnLine 48(21): 1275–1282.10.1016/j.ifacol.2015.09.701
Jung, D., Ng, K., Frisk, E. and Krysander, M. (2018). Combining model-based diagnosis and data-driven anomaly classifiers for fault isolation, Control Engineering Practice 80: 146–156.10.1016/j.conengprac.2018.08.013
de Kleer, J. (2011). Hitting set algorithms for model-based diagnosis, Proceedings of 22nd International Workshop on Principles of Diagnosis, Murnau, Germany, pp. 1–6.
de Kleer, J. and Kurien, J. (2003). Fundamentals of model-based diagnosis, 5th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, Washington DC, USA, pp. 25–36.
Korbicz, J. and Kościelny, J.M. (Eds) (2010). Modeling, Diagnostics and Process Control. Implementation in the Di-aSter System, Springer, Berlin/Heidelberg.
Kościelny, J.M. (1999). Application of fuzzy logic fault isolation in a three-tank system, 14th IFAC World Congress IFAC, Bejing, China, pp. 7754–7759.
Kościelny, J.M. and Bartyś, M. (2021). Comparative study of fault distinguishability based on bi-and three-valued diagnostic signals, 32nd International Workshop on Principles of Diagnosis, DX-2021, Hamburg, Germany, pp. 1–6.
Kościelny, J.M., Bartyś, M. and Grudziak, Z. (2021b). Tri-valued evaluation of residuals as a method of addressing the problem of fault compensation effect, in J. Korbicz and K. Patan (Eds), Advances in Diagnostics of Processes and Systems, Springer, Cham, pp. 31–44.10.1007/978-3-030-58964-6_3
Kościelny, 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.
Kościelny, J., Bartyś, M. and Sztyber, A. (2021a). Diagnosing with a hybrid fuzzy-Bayesian inference approach, Engineering Applications of Artificial Intelligence 104(104345): 1–11.10.1016/j.engappai.2021.104345
Kościelny, J.M., Rostek, K., Syfert, M. and Sztyber, A. (2016). Fault isolability with different forms of the faults–symptoms relation, International Journal of Applied Mathematics and Computer Science 26(4): 815–826, DOI: 10.1515/amcs-2016-0058.
Kościelny, J.M., Syfert, M., Fajdek, B. and Kozak, A. (2017). The application of a graph of a process in HAZOP analysis in accident prevention system, Journal of Loss Prevention in the Process Industries 50: 55–66.10.1016/j.jlp.2017.09.003
Kościelny, J.M., Syfert, M. and Wnuk, P. (2021c). Diagnostic row reasoning method based on multiple-valued evaluation of residuals and elementary symptoms sequence, Energies 14(9), Paper no. 2476.10.3390/en14092476
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 A: Systems and Humans 38(1): 197–206.10.1109/TSMCA.2007.909555
Łabęda-Grudziak, Z. and Lipiński, M. (2021). The identification method of the coal mill motor power model with the use of machine learning techniques, Bulletin of the Polish Academy of Sciences: Technical Sciences 69(1): e135842.
Mejdi, S., Messaoud, A. and Ben Abdennour, R. (2020). Fault tolerant multicontrollers for nonlinear systems: A real validation on a chemical process, International Journal of Applied Mathematics and Computer Science 30(1): 61–74, DOI: 10.34768/amcs-2020-0005.
Mur, A., Travé-Massuyés, L., Chanthery, E., Pons, R. and Ribot, P. (2022). A neural algorithm for the detection and correction of anomalies: Application to the landing of an airplane, Sensors 22(6): 2334.10.3390/s22062334895455535336505
Odendaal, H. and Jones, T. (2014). Actuator fault detection and isolation: An optimised parity space approach, Control Engineering Practice 26: 222–232.10.1016/j.conengprac.2014.01.013
Patton, R. and Chen, J. (1991). A review of parity space approaches to fault diagnosis, IFAC Proceedings Volumes 24(6): 65–81.10.1016/S1474-6670(17)51124-6
Pazera, M., Buciakowski, M., Witczak, M. and Mrugalski, M. (2020). A quadratic boundedness approach to a neural network-based simultaneous estimation of actuator and sensor faults, Neural Computing and Applications 32(2): 379–389.10.1007/s00521-018-3706-8
Pulido, B., Zamarreo, J., Merino, A. and Bregon, A. (2019). State space neural networks and model-decomposition methods for fault diagnosis of complex industrial systems, Engineering Applications of Artificial Intelligence 79: 67–86.10.1016/j.engappai.2018.12.007
Qin, S. (2012). Survey on data-driven industrial process monitoring and diagnosis, Annual Reviews in Control 36(2): 220–234.10.1016/j.arcontrol.2012.09.004
Rodler, P. (2020). Reuse, reduce and recycle: Optimizing Reiter’s HS-tree for sequential diagnosis, 24th European Conference on Artificial Intelligence ECAI 2020, Santiago de Compostella, Spain, pp. 873–880.
Romero, L., Blesa, J., Puig, V. and Cembrano, G. (2022). Clustering-learning approach to the localization of leaks in water distribution networks, Journal of Water Resources Planning and Management 148(4): 04022003.10.1061/(ASCE)WR.1943-5452.0001527
Rotondo, D., Buciakowski, M. and Witczak, M. (2021). Simultaneous state and process fault estimation in linear parameter varying systems using robust quadratic parameter varying observers, International Journal of Robust and Nonlinear Control 31(17): 8390–8407.10.1002/rnc.5395
Simani, S., Farsoni, S. and Castaldi, P. (2018). Data-driven techniques for the fault diagnosis of a wind turbine benchmark, International Journal of Applied Mathematics and Computer Science 28(2): 247–268, DOI: 10.2478/amcs-2018-0018.
Song, Y., Zhong, M., Xue, T., Ding, S. and Li, W. (2020). Parity space-based fault isolation using minimum error minimax probability machine, Control Engineering Practice 95, Paper no. 104242.
Syfert, M., Bartyś, M. and Kościelny, J.M. (2018). Refinement of fuzzy diagnosis in decentralized two-level diagnostic structure, IFAC-PapersOnLine 51(24): 160–167.10.1016/j.ifacol.2018.09.550
Sztyber, A. and Kościelny, J. (2016). Diagnostic reasoning framework combining fuzzy logic and Dempster–Shafer theory, IEEE International Conference Prognostics and Health Management (ICPHM), Ottawa, Canada, pp. 1–6.
Sztyber, A., Ostasz, A. and Kościelny, J. (2015). Graph of a process—A new tool for finding model’s structures in model based diagnosis, IEEE Transactions on Systems, Man, and Cybernetics: Systems 45(7): 1004–1017.10.1109/TSMC.2014.2384000
Taheri, M., Khorasani, K., Shames, I. and Meskin, N. (2020). Cyber attack and machine induced fault detection and isolation methodologies for cyber-physical systems, arXiv 2009.06196v1.
Travé-Massuyés, L. (2014a). Bridges between diagnosis theories from control and AI perspectives, in J. Korbicz and M. Kowal (Eds), Intelligent Systems in Technical and Medical Diagnostics, Springer, Berlin/Heidelberg, pp. 3–28.10.1007/978-3-642-39881-0_1
Travé-Massuyés, L. (2014b). Bridging control and artificial intelligence theories for diagnosis: A survey, Engineering Applications of Artificial Intelligence 27: 1–16.10.1016/j.engappai.2013.09.018
Travé-Massuyés, L., Escobet, T. and Milne, R. (2006). Diagnosability analysis based on component-supported analytical redundancy relations, IEEE Transactions on Systems, Man and Cybernetics, A: Systems and Humans 36(6): 1146–1160.10.1109/TSMCA.2006.878984
Vanden-Daele, R., Peng, Y. and Kinnaert, M. (1997). Fault diagnosis using belief functions, 3rd IFAC Symposium on Fault Detection Supervision and Safety for Technical Processes SAFEPROCESS’97, Hull, UK, pp. 546–551.
Witczak, M. (2007). Modelling and Estimation Strategies for Fault Diagnosis of Non-Linear Systems: From Analytical to Soft Computing Approaches, Springer, Berlin.
Witczak, M. (2014). Fault Diagnosis and Fault-Tolerant Control Strategies for Non-Linear Systems: Analytical and Soft Computing Approaches, Springer, Cham.10.1007/978-3-319-03014-2
Witczak, M., Mrugalski, M., Pazera, M. and Kukurowski, N. (2020). Fault diagnosis of an automated guided vehicle with torque and motion forces estimation: A case study, ISA Transactions 104: 370–381.10.1016/j.isatra.2020.05.01232439131
Xu, F., Puig, V., Ocampo-Martinez, C., Olaru, S. and Niculescu, S.-I. (2017). Robust MPC for actuator-fault tolerance using set-based passive fault detection and active fault isolation, International Journal of Applied Mathematics and Computer Science 27(1): 43–61, DOI: 10.1515/amcs-2017-0004.
Yang, F., Sirish, L. and Xiao, D. (2010). Signed directed graph modeling of industrial processes and their validation by data-based methods, Conference on Control and Fault-Tolerant Systems (SysTol), Nice, France, pp. 387–392.
Zhai, S., Wang, W. and Ye, H. (2015). Fault diagnosis based on parameter estimation in closed-loop systems, IET Control Theory and Application 9(7): 1146–1153.10.1049/iet-cta.2014.0717