Application Examples of Fault Detection and Isolation Strategies using System Identification Approaches

Application Examples of Fault Detection and Isolation Strategies using System Identification Approaches. Seminar of two hours for the Nanjing University of Aeronautics and Astronautics - NUAA
 

Abstract

• The ability to promptly detect, isolate, and diagnose faults is a crucial requirement for ensuring safety, reliability, and efficiency in modern industrial processes. This presentation explores how system identification approaches can be effectively exploited to design robust fault detection and isolation schemes, capable of coping with the complexity of real-world dynamic systems. Both linear and nonlinear models are considered, ranging from autoregressive structures and state-space representations to Takagi-Sugeno fuzzy formulations. By combining these models with observer-based and data-driven strategies, residual signals are generated and analysed to identify fault signatures with enhanced sensitivity and robustness. Case studies drawn from chemical reactors and gas turbine prototypes illustrate the practical implementation and performance of these methods, highlighting their ability to deal with parameter variations, noise, and operating uncertainties. The results demonstrate that integrating system identification techniques with artificial intelligence tools significantly improves diagnostic accuracy, reduces false alarms, and provides a reliable framework for advanced fault management in industrial applications.

Keywords

• Fault detection and isolation; System identification; Nonlinear modelling; Fuzzy logic; Observers; Residual generation; Fault diagnosis; Industrial applications; Chemical reactors; Gas turbines; Artificial intelligence; Robustness.

Talk's Slides and General Topics

• Application Examples of Fault Detection and Isolation Strategies using System Identification Approaches. Lecture Slides PDF Format (15.5MB).
• Fundamental Textbook. Silvio Simani, Cesare Fantuzzi and Ron J. Patton, "Model-based fault diagnosis in dynamic systems using identification techniques". Springer-Verlag Ed., ISBN: 1852336854. Advances in Industrial Control Series. London, UK. First Ed. November 2002. (298 pages). Draft manuscript: file in PDF format (35 MB).
• General Readings. Ljung, L. System Identification: Theory For User. Book in PDF format. 1st Ed. 1987.
• The System Identification Toolbox by MathWorks. Tutorial from MathWorks Website. Webinar by Lennart Ljung (YouTube Channel). Lecture recorded movie (YouTube Channel).
• Open Problems and Future Works. Some System Identification Challenges and Approaches. Slides in PDF format.

Downloads

• Identification Dataset Example for the System Identification Toolbox of Matlab. Example process data from a SISO model: (Dynamic process data file in ascii format, Matlab Dataset).
• 22. K. Sigmon, "Matlab Primer Third Edition". University of Florida, Florida, Second Edition ed., 1992. (file in formato pdf ).
• Get Started with System Identification Toolbox System Identification Graphical User Interface for Matlab. (Matlab website, English).
• System Identification Toolbox System Identification Graphical User Interface for Matlab. (pdf format file).

General References and Textbooks

• SID, "System Identification Toolbox". System Identification Graphical User Interface for Matlab (pdf format file).
• System Identification: Theory for the User, Lennart Ljung - Springer, 1999.
• Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques, S. Simani, C. Fantuzzi, R. J. Patton - Springer, 2003.
• System Identification, T. Soderstrom, P. Stoica, Prentice Hall International, Cambridge, 1989

Fundamental Publications on System Identification and Data Analysis

Books

1. Ljung, L. (1999). System Identification: Theory for the User (2nd ed.). Prentice Hall, Upper Saddle River, NJ, USA.
2. Ljung, L., Glad, T. (2021). Modeling and Identification of Dynamic Systems. Studentlitteratur AB, Lund, Sweden.
3. Soderstrom, T., Stoica, P. (1989). System Identification. Prentice Hall International, Hemel Hempstead, UK.
4. Sintelon, R., Schoukens, J. (2012). System Identification: A Frequency Domain Approach (2nd ed.). Wiley-IEEE Press, Hoboken, NJ, USA.
5. Nelles, O. (2001). Nonlinear System Identification: From Classical Approaches to Neural Networks. Springer, Berlin, Germany.
6. Goodwin, G.C., Payne, R.L. (1977). Dynamic System Identification: Experiment Design and Data Analysis. Academic Press, New York, NY, USA.

Journal Articles

7. Schoukens, J., Ljung, L. (2019). Nonlinear system identification: A user-oriented roadmap. IEEE Control Systems Magazine, 39(6), 28 - 99.
8. Pillonetto, G., Ljung, L., Chen, T. (2023). Deep networks for system identification: A survey. Annual Reviews in Control, 56, 242 - 264.
9. Wahlberg, B., Ljung, L. (2018). Algorithms and performance analysis for stochastic Wiener system identification. Automatica, 95, 277 - 289.
10. Brunton, S.L., Proctor, J.L., Kutz, J.N. (2016). Sparse Identification of Nonlinear Dynamical Systems with Control (SINDYc). IFAC-PapersOnLine, 49(18), 710 - 715.
11. Pillonetto, G., Dinuzzo, F., Chen, T., De Nicolao, G., Ljung, L. (2014). Kernel methods in system identification, machine learning and function estimation: A survey. Automatica, 50(3), 657 - 682.
12. Ljung, L. (2010). Perspectives on system identification. Annual Reviews in Control, 34(1), 1 - 12.
13. Verdult, V., Ljung, L. (2005). Nonlinear state-space system identification with application to neural networks. Automatica, 41(11), 1771 - 1784.
14. Ljung, L. (2008). Perspectives on the relation between artificial intelligence and system identification. IFAC Proceedings Volumes, 41(2), 1 - 12.
15. Gevers, M., Ljung, L. (1986). Optimal experiment designs with respect to the intended model application. Automatica, 22(5), 543 - 554.

Software Resources

16. Ljung, L. (2024). System Identification Toolbox (version 10.1). MathWorks, Natick, MA, USA. (PDF format file).

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