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On-line Diagnostics of Large-Scale Industrial Processes

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Automatic Control, Robotics, and Information Processing

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 296))

Abstract

Basic problems of on-line diagnostics of large-scale industrial processes have been characterized, as well as the methods aimed at solving them, focusing on the authors results obtained in the area of fault isolation. Particular attention was given to fault distinguishability, multiple faults isolation, inference methods of uncertain signals, decomposition of the system and diagnostics in decentralized structures, as well as the application of graph models in diagnostic systems design for industrial processes. Effective and robust diagnostic algorithms of complex dynamic large-scale systems and their implementation in the realized diagnostics systems are also described. The summary emphasizes the significance of on-line diagnostics in ensuring process safety.

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References

  1. Bartyś, M.: Chosen Issues of Fault Isolation, Theory, Practice and Applications. PWN, Warszawa (2014)

    Google Scholar 

  2. Basseville, M., Nikiforov, I.V.: Detection of Abrupt Changes—Theory and Application. Prentice-Hall, Englewood Cliffs, NJ (1993)

    MATH  Google Scholar 

  3. Blanke, M., Kinnaert, M., Lunze, J., Staroswiecki, M.: Diagnosis and Fault-Tolerant Control. Springer, Berlin (2004)

    MATH  Google Scholar 

  4. Chen, J., Patton, R.: Robust Model-Based Fault Diagnosis for Dynamic Systems. Kluwer Akademic Publishers, Boston (1999)

    Book  MATH  Google Scholar 

  5. Gertler, J.: Fault Detection and Diagnosis in Engineering Systems. Marcel Dekker, Inc., New York-Basel-Hong Kong (1998)

    Google Scholar 

  6. Himmelblau, D.: Fault Detection and Diagnosis in Chemical and Petrochemical Processes. Elsevier, Amsterdam (1978)

    Google Scholar 

  7. Isermann, R.: Fault Diagnosis Systems. An Introduction from Fault Detection to Fault Tolerance. Springer, New York (2006)

    Google Scholar 

  8. Korbicz, J., Kościelny, J.M. (eds.): Modeling, Diagnostics and Process Control. Implementation in the DiaSter System. Springer, Berlin, Heidelberg (2010)

    Google Scholar 

  9. Korbicz, J., Kościelny, J.M., Kowalczuk, Z., Cholewa, W. (eds.): Fault Diagnosis: Models, Artificial Intelligence Methods, Applications. Springer, Berlin, Heidelberg (2004)

    MATH  Google Scholar 

  10. Kościelny, J.M.: Diagnostics of Automated Industrial Processes, (in Polish. Akademicka Oficyna Wydawnicza Exit, Warsaw (2001)

    Google Scholar 

  11. Patton, R., Frank, P., Clark, R. (eds.): Issues of Fault Diagnosis for Dynamic Systems. Springer, London (2000)

    Google Scholar 

  12. Witczak, M.: Modelling and Estimation Strategies for Fault Diagnosis of Non-Linear Systems. From Analytical to Soft Computing Approaches. Springer, Berlin (2007)

    MATH  Google Scholar 

  13. Calado, J.M.F., Korbicz, J., Patan, K., Patton, R.J., Sá da Costa, J.M.G.: Soft computing approaches to fault diagnosis for dynamic systems. Eur. J. Control 7(2–3), 248–286 (2001)

    Article  MATH  Google Scholar 

  14. Frank, P.M.: Fault diagnosis in dynamic systems via state estimations methods. A survey. In: Tzafestas S.G., et al. (eds.) System Fault Diagnostics, Reliability and Related Knowledge Based Approaches, vol. 2, D. Reidl Publishing Company, Dordrecht (1987)

    Google Scholar 

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

    Article  MATH  Google Scholar 

  16. Isermann, R.: Model-based fault-detection and diagnosis—status and applications. Annu. Rev. Control 29(2005), 71–85 (2005)

    Article  Google Scholar 

  17. Isermann, R., Ballé, P.: Trends in the application of model-based fault detection and diagnosis of technical process. Control Eng. Pract. 5(5), 709–719 (1997)

    Article  Google Scholar 

  18. Korbicz, J.: Robust fault detection using analytical and soft computing methods. Bull. Polish Acad. Sci. Tech. Sci. 54(1), 75–88 (2006)

    Google Scholar 

  19. Leonhardt, S., Ayoubi, M.: Methods of fault diagnosis. Control Eng. Pract. 5(5), 683–692 (1997)

    Article  Google Scholar 

  20. Venkatasubramanian, V., Rengaswamy, R., Yin, K., Kavuri, S.N.: A review of process fault detection and diagnosis, Part I: quantitative model-based methods. Comput. Chem. Eng. 27, 293–311 (2003)

    Article  Google Scholar 

  21. Venkatasubramanian, V., Rengaswamy, R., Kavuri, S.N.: A review of process fault detection and diagnosis, Part II: qualitative model and search strategies. Comput. Chem. Eng. 27, 313–326 (2003)

    Article  Google Scholar 

  22. Venkatasubramanian, V., Rengaswamy, R., Kavuri, S.N., Yin, K.: A review of process fault detection and diagnosis, Part III: process history based methods. Comput. Chem. Eng. 27, 327–346 (2003)

    Article  Google Scholar 

  23. Kościelny, J.M., Syfert, M.: Application properties of methods for fault detection and isolation in the diagnosis of complex large-scale processes. Bull. Polish Acad. Sci. Tech. Sci. 62(3), 571–582 (2014)

    Google Scholar 

  24. Kościelny, J.M.: Diagnostics of Continuous Automatized Industrial Processes by Dynamic Table of States Method (in Polish). In: Scientific Papers of Warsaw Technical University, Series: Electronics, vol. 95. Warszawa (1991)

    Google Scholar 

  25. Kościelny, J.M., Syfert, M.: Robust diagnostics of complex chemical processes: main problems and possible solutions. Chem. Process Eng. 39(2), 1–19 (2018)

    Google Scholar 

  26. Łabęda-Grudziak, Z.: Zastosowanie addytywnego modelu regresji do generacji residuów dla potrzeb detekcji uszkodzeń. Ph.D. Dissertation, PW, Wydział Mechatroniki (2011)

    Google Scholar 

  27. Kościelny, J.M.: Recognition of fault in the diagnosing process. Appl. Math. Comput. Sci. 3(3), 559–572 (1993)

    MathSciNet  MATH  Google Scholar 

  28. Pawlak, Z.: Systemy Informacyjne. Podstawy teoretyczne, WNT, Warszawa (1983)

    Google Scholar 

  29. Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning About Data. Springer, Netherlands (1991)

    MATH  Google Scholar 

  30. Kościelny, J.M., Bartyś, M., Rzepiejewski, P., Sá da Costa, J.: Actuator fault distinguishability study. Control Eng. Pract. 14, 645–652 (2006)

    Article  Google Scholar 

  31. Kościelny, J., Syfert, M., Wnuk, P.: Advanced monitoring and diagnostic system ‘AMandD’. IFAC Proc. Volumes 39(13), 635–640 (2006)

    Article  Google Scholar 

  32. Kościelny, J.M., Zakroczymski, K.: Fault isolation algorithm that takes dynamics of symptoms appearances into account. Bull. Polish Acad. Sci. Tech. Sci. 49(2), 323–336 (2001)

    Google Scholar 

  33. Syfert, M., Kościelny, J.M.: Diagnostic reasoning based on symptom forming sequence. IFAC Proc. Volumes 42(8), 89–94 (2009)

    Article  Google Scholar 

  34. Sztyber, A., Ostasz, A., Kościelny, J.M.: Graph of a process—a new tool for finding model’s structures in model based diagnosis. IEEE Trans. Syst., Man, Cybern.: Syst. 45(7), 1004–1017 (2015)

    Article  Google Scholar 

  35. Kościelny, J.M., Syfert, M., Sztyber, A.: Fault distinguishability in diagnostic of complex dynamical systems (in Polish). Akademicka Oficyna Wydawnicza EXIT (2018)

    Google Scholar 

  36. Kościelny, J.M., Rostek, K., Syfert, M., Sztyber, A.: Fault isolability with different forms of faults-symptoms relation. Int. J. Appl. Math. Comput. Sci. 26(4), 815–826 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  37. Kościelny, J.M., Syfert, M., Tabor, Ł.: Application of knowledge about residuals dynamics for fault isolation and identification. In: Conference on Control and Fault-Tolerant Systems. SysTol′13, Nice, IEEE Xplore Digital Library, pp. 275–280 (2013)

    Google Scholar 

  38. Kościelny, J.M., Łabęda-Grudziak, Z.: Double fault distinguishability in linear systems. Int. J. Appl. Math. Comput. Sci. 23(2), 395–406 (2013)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  40. Kościelny, J.M., Bartyś, M., Syfert, M.: Method of multiple fault isolation in large scale systems. IEEE Trans. Control Syst. Technol. 20(5), 1302–1310 (2012)

    Article  Google Scholar 

  41. Ligęza, A., Kościelny, J.M.: A new approach to multiple fault diagnosis. Combination of diagnostic matrices, graphs, algebraic and rule-based models. The case of two-layer models. Int. J. Appl. Math. Comput. Sci. 18(4), 465–476 (2008)

    Google Scholar 

  42. Kościelny, J.M., Syfert, M.: Fuzzy diagnostic reasoning that takes into account the uncertainty of the faults-symptoms relation. Int. J. Appl. Math. Comput. Sci. 16(1), 27–35 (2006)

    MathSciNet  MATH  Google Scholar 

  43. Pearl, J.: Probabilistic Reasoning in Intelligent Systems. Networks of Plausible Inference. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1988)

    MATH  Google Scholar 

  44. Zadeh, L.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  MATH  Google Scholar 

  45. Dempster, A.P.: Upper and lower probabilities induced by a multivalued mapping. Ann. Math. Stat. 38(2), 325–339 (1967)

    Google Scholar 

  46. Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press (1976)

    Google Scholar 

  47. Buchanan, B.G., Shortliffe, E.H.: Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA (1984)

    Google Scholar 

  48. Shortliffe, E.H., Buchanan, B.G.: A Model of Inexact Reasoning in Medicine. Readings in Uncertain Reasoning, pp. 259–275. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1990)

    Google Scholar 

  49. Cooper, G.F.: The computational complexity of probabilistic inference using Bayesian belief networks (research note). Artif. Intell. 42(2–3), 393–405 (1990)

    Article  MATH  Google Scholar 

  50. Piegat, A.: Fuzzy Modelling and Control. Springer, Berlin (2001)

    Google Scholar 

  51. Yager, R.R., Filev, D.P.: Essentials of Fuzzy Modelling and Control. Wiley (1994)

    Google Scholar 

  52. Kościelny, J.: Application of fuzzy logic fault isolation in a three-tank system. IFAC Proc. Volumes (14th World Congress IFAC), 32(2), 7754–7759 (1999)

    Google Scholar 

  53. Kościelny, J., Sędziak, D., Zakroczymski, K.: Fuzzy logic fault isolation in large scale systems. Int. J. Appl. Math. Comput. Sci. 9(3), 637–652 (1999)

    Google Scholar 

  54. Kościelny, J., Syfert, M.: Current diagnostics of power boiler system with use of fuzzy logic. IFAC Proc. Volumes 33(11), 669–674 (2000)

    Article  Google Scholar 

  55. Kościelny, J.M., Syfert, M.: Fuzzy logic application to diagnostic of industrial processes. IFAC Proc. Volumes 36(5), 711–716 (2003)

    Article  Google Scholar 

  56. Sędziak, D.: Methods of fault isolation in industrial processes (in Polish). Ph. D. Thesis, Warsaw University of Technology, Department of Mechatronics (2001)

    Google Scholar 

  57. Syfert, M.: Diagnostics of industrial processes with the use of partial models and fuzzy logic. Ph. D. Thesis, Warsaw University of Technology, Department of Mechatronics (2003)

    Google Scholar 

  58. Sztyber, A., Kościelny, J.M.: Diagnostic reasoning framework combining fuzzy logic and dempster-shafer theory. In: IEEE Xplore Digital Library. IEEE International Conference on Prognostics and Health Management (ICPHM), Ottawa, Ontario (2016)

    Google Scholar 

  59. Kościelny, J.M., Sztyber, A.: Metoda wnioskowania diagnostycznego uwzględniająca niepewności symptomów na drodze fuzji teorii Bayes’a i logiki rozmytej. In: Kowalczuk, W.Z., Domżalski, M. (eds.) Advanced Systems for Automation and Diagnostics, Pomorskie Wydawnictwo Naukowo-Techniczne, pp. 117-128 (2015)

    Google Scholar 

  60. Siljak, D.: Large-Scale Dynamic Systems: Stability and Structure. North Holland, New York (1978)

    MATH  Google Scholar 

  61. Boem, F., Ferrari, R.M.G., Parisini, T., Polycarpou, M.M.: Distributed fault diagnosis for nonlinear systems. In: Preprints of the 8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, pp. 1089–1094 (2012)

    Google Scholar 

  62. Indra, S., Travé-Massuyés, L., Chanthery, E.: Decentralized diagnosis with isolation on request for spacecraft. In: Preprints of the 8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, vol. 8, no. PART 1, pp. 283–288 (2012)

    Google Scholar 

  63. Kościelny, J.M.: Diagnostics of processes in decentralized structures. Arch. Control Sci. 7(3/4), 181–202 (1998)

    Google Scholar 

  64. Kościelny, J.M., Sikora, A.I.: Dekompozycja złożonych obiektów diagnozowania. Archiwum Automatyki i Telemechaniki 1(1991), 115–131 (1991)

    Google Scholar 

  65. Pulido, B., Zamarre, J.M.: Using structural decomposition methods to design gray-box models for fault diagnosis of complex industrial systems: a beet sugar factory case study. In: First European Conference of the Prognostics and Health Management Society (2012)

    Google Scholar 

  66. Wnuk, P., Kościelny, J.M.: Diagnostic system decomposition with genetic optimization. Pomiary Automatyka Kontrola, Nr 6(2011), 641–647 (2011)

    Google Scholar 

  67. Kościelny, J.M., Sztyber, A.: Decomposition of complex diagnostic systems. In: Preprints of the 10th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, pp. 755–762 (2018)

    Google Scholar 

  68. Kernighan, B.W., Lin, S.: An efficient heuristic procedure for partitioning graphs. Bell Syst. Tech. J. 49 (1970)

    Google Scholar 

  69. Kościelny, J.M., Bartyś, M., Syfert, M.: Diagnostics of industrial processes in decentralised structures with application of fuzzy logic. IFAC Proc. Volumes (17th World Congress of IFAC), 41(2), 6944–6949 (2008)

    Google Scholar 

  70. Syfert, M., Kościelny, J.M., Bartyś, M.: A fuzzy inference approach to fault diagnosis refinement in decentralized diagnostics. In: Advanced Solutions in Diagnostics and Fault Tolerant Control, pp. 143–154. Springer (2017)

    Google Scholar 

  71. Syfert, M., Bartyś, M., Kościelny, J.M.: Refinement of fuzzy diagnosis in decentralized two-level diagnostic structure. In: Preprints of the 10th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, pp. 160–167 (2018)

    Google Scholar 

  72. Kościelny, J.M., Ostasz, A.: Application of causal graph GP for description of diagnosed process. IFAC Proc. Volumes 36(5), 801–806 (2003)

    Article  Google Scholar 

  73. Sztyber, A.: Sensor placement for industrial process diagnosis using graph of a process. Ph. D. Thesis, Warsaw University of Technology, Department of Mechatronics (2015)

    Google Scholar 

  74. Kościelny, J.M., Syfert, M., Wnuk, P.: The idea of on-line diagnostics as a method of cyberattack. In: Advanced Solutions in Diagnostics and Fault Tolerant Control, Springer, pp. 449–457 (2017)

    Google Scholar 

  75. Kościelny, J.M., Sztyber, A., Syfert, M.: Graph description of the process and its applications. In: Trends in Advanced Intelligent Control, Optimization and Automation, pp. 550–559. Springer (2017)

    Google Scholar 

  76. Chang, C.C., Yu, C.C.: On-line fault diagnosis using the signed directed graph. Ind. Eng. Chem. Res. 29(7), 1290–1299 (1990)

    Article  Google Scholar 

  77. Kramer, B.L., Palowitch, M.A.: A rule-based approach to fault diagnosis using the signed directed graph. AIChE J. 33(7), 1067–1078 (1987)

    Article  MathSciNet  Google Scholar 

  78. Shiozaki, J., Matsuyama, H., Tano, K., O’Shima, E.: Fault diagnosis of chemical processes by the use of signed directed graphs: extension to five-range patterns of abnormality. Int. Chem. Eng. 37(4), 651–659 (1985)

    Google Scholar 

  79. Bouamama, B.O., Harabia, R.E., Abdelkrimb, M., Gayedb, M.B.: Bond graphs for the diagnosis of chemical processes. Comput. Chem. Eng. 36, 301–324 (2012)

    Article  Google Scholar 

  80. Samantaray, A., Medjaherb, K., Bouamama, B.O., Staroswiecki, M., Dauphin-Tanguy, G.: Diagnostic bond graphs for online fault detection and isolation. Simul. Model. Pract. Theory 14(3), 237–262 (2006)

    Article  Google Scholar 

  81. de Flaugergues, V., Cocquempot, V., Bayart, M., Pengov, M.: Structural analysis for FDI: a modified, invertibility-based canonical decomposition. In: 20th International Workshop on Principles of Diagnosis, pp. 59–66 (2009)

    Google Scholar 

  82. Düstegör, D., Frisk, E., Cocquempot, V., Krysander, M., Staroswiecki, M.: Structural analysis of fault isolability in the damadics benchmark. Control Eng. Pract. 14, 597–608 (2006)

    Article  Google Scholar 

  83. Kościelny, J.M.: Diagnostyka on-line aparatury technologicznej i układów automatyki metodą DTS—dynamicznych tablic stanu. Archiwum Automatyki i Telemechaniki, 4/1988, pp. 593–606 (1988)

    Google Scholar 

  84. Kościelny, J.M., Pieniążek, A.: Algorithm of fault detection and isolation applied for evaporation unit in sugar factory. Control Eng. Pract. 2(4), 649–657 (1994)

    Article  Google Scholar 

  85. Syfert, M., Rzepiejewski, P., Wnuk, P., Kościelny, J.M.: Current diagnostics of the evaporation station. IFAC Proc. Volumes 38(1), 365–370 (2005)

    Article  Google Scholar 

  86. Kościelny, J.M., Leszczyński, M., Gąsecki, A., Syfert, M.: Monitoring of the degree of coking in H-Oil plant. IFAC Proc. Volumes 42(8), 1258–1263 (2009)

    Article  Google Scholar 

  87. Kościelny, J.M., Syfert, M., Leszczyński, M., Gąsecki, A.: Pilot tests of the advanced system of process diagnostics in PKN ORLEN. Pomiary Automatyka Kontrola 55(3/2009), 136–140 (2009)

    Google Scholar 

  88. Syfert, M., Wnuk, P., Kościelny, J.M.: DiaSter—intelligent system for diagnostics and automatic control support of industrial processes. JAMRIS—J. Autom., Mob. Rob. Intell. Syst. 5(4), 41–46 (2011)

    Google Scholar 

  89. Pawlak, M., Kościelny, J.M., Wasiewicz, P.: Method of increasing the reliability and safety of the processes through the use of fault tolerant control systems. Eksploatacja i Niezawodność—Maintenance and Reliability 17(3), 398–407 (2015)

    Google Scholar 

  90. Kościelny, J.M., Bartyś, M.: The requirements for a new layer in the industrial safety systems. In: 9th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, Safeprocess 2015, pp. 1333–1338. Arts de Métiers ParisTech, Paris, France (2015)

    Google Scholar 

  91. Kościelny, J., Syfert, M., Fajdek, B., Kozak, A.: The application of a graph of a process in HAZOP analysis in accident prevention system. J. Loss Prev. Process Ind. 50(2017), 55–66 (2017)

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Mechatronics, Warsaw University of Technology, ul. Św. A. Boboli 8, Warsaw, 02-525, Poland

    Jan Maciej Kościelny

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  1. Jan Maciej Kościelny
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Correspondence to Jan Maciej Kościelny .

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  1. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Piotr Kulczycki

  2. Institute of Control and Computation Engineering, University of Zielona Góra, Zielona Góra, Poland

    Józef Korbicz

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Kościelny, J.M. (2021). On-line Diagnostics of Large-Scale Industrial Processes. In: Kulczycki, P., Korbicz, J., Kacprzyk, J. (eds) Automatic Control, Robotics, and Information Processing. Studies in Systems, Decision and Control, vol 296. Springer, Cham. https://doi.org/10.1007/978-3-030-48587-0_21

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