Fault Detection Techniques in Condition Monitoring: Model-Based and Data-Driven Methods

Fault Detection Techniques in Condition Monitoring: Model-Based and Data-Driven Methods. Course of 10 hours for the National Ph.D. Program in Autonomous Systems - DAUSY

  • Google Meet Link (remote connection for online lectures): meet.google.com/cid-xmdz-bez



  • This represents an advanced course of Automatic Nonlinear Control and Supervision techniques for complex systems, and it studies advanced elements of a control and fault diagnosis system from the dynamic point of view, by considering nonlinear dynamic processes from their input-state-output and input-output points of view. This course thus aims at presenting those control and supervision methodologies currently required and expected by modern industries and practical application activities. The main goal of the course consists of providing advanced topics and tools for the study, supervision, fault diagnosis and control of complex dynamic systems, as well as their interconnections under proper design constrains imposed by cost, speed, computational cost, robustness, reliability, sustainability, and power consumption.


    Course Enrollment

  • Please use the following link for sending your contact details to the instructor and enroll in the course (Prof. Silvio Simani, Department of Engineering, University of Ferrara. Email: silvio.simani@unife.it). In case of problems with the link, please contact the instructor directly by email.


    Course Programme

  • Introduction: Course Introduction
  • Issues in Model-Based Fault Diagnosis
  • Fault Detection and Isolation (FDI) Methods based on Analytical Redundancy
  • Model-based Fault Detection Methods

  • Issues in Model-Based Fault Diagnosis
  • Model Uncertainty and Fault Detection
  • The Robustness Problem in Fault Detection
  • System Identification for Robust FDI
  • Fault Identification Methods
  • Modelling of Faulty Systems

  • Residual Generation Techniques
  • The Residual Generation Problem

  • Fault Diagnosis Technique Integration
  • Fuzzy Logic for Residual Generation
  • Neural Networks in Fault Diagnosis

  • Output Observers for Robust Residual Generation
  • Unknown Input Observer (UIO): Fundamentals
  • FDI Schemes Based on UIO and Output Observers
  • Kalman Filtering and FDI from Noisy Measurements: Fundamentals
  • Residual Robustness to Disturbances

  • Application Examples


    Lecture Notes

  • Fault Diagnosis, Residual Generation and Evaluation, Robustness Problems and Related Issues: (PDF file, single page).


    Examination Method

    The examination is divided into 2 phases that will take place in the same day:
  • A project realised in Matlab and Simulink regarding the design and the simulation of a simple supervision scheme for a nonlinear system by using the Matlab and Simulink environments, which aims at understanding if the student has acquired the skills of the course. The time allowed for this test is about 1 hours.
  • One test (open answers and multiple choice questions) based on all the topics tackled in the class or on the basic concepts of the course, with the aim of evaluating how deeply the student has learnt the subject and how he is able to understand the topics analysed. The time allowed for this test is about 0.5 hour.


    References: Fundamentals Monographs and Textbooks on FDI

  • Rolf Isermann. "Fault-Diagnosis Applications: Model-Based Condition Monitoring: Actuators, Drives, Machinery, Plants, Sensors, and Fault-tolerant Systems". Springer. (April 29, 2011). ISBN: 978-3642127663.
  • Steven X. Ding, "Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools". Springer, (April 10, 2008). ISBN: 978-3540763031.
  • Rolf Isermann, "Fault-Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance". Springer-Verlag, 2005, 1st Editions. November, 28, 2005. ISBN: 3540241124.
  • Blanke, M. and Kinnaert, M. and Lunze, J. and Staroswiecki, M. Schroder, J., "Diagnosis and Fault-Tolerant Control". Springer, 2003. 1st Edition. August, 5, 2005. ISBN: 3540010564.
  • Korbicz, J. and Koscielny, J. M. and Kowalczuk, Z. and Cholewa, W., "Fault Diagnosis: Models, Artificial Intelligence, Applications". Springer-Verlag, 2004. 1st Edition. February, 12, 2004. ISBN: 3540407677.
  • Simani, S. and Fantuzzi, C. and Patton, R. J., "Model-based fault diagnosis in dynamic systems using identification techniques", Springer-Verlag, 2002. ISBN 1852336854. Advances in Industrial Control Series. London, UK. First Eq. November, 2002. (298 pages).
  • Basseville, M. and Nikiforov, I. V., "Detection of Abrupt Changes: Theory and Application", Springer-Verlag (March 1986), ISBN: 0387160434.
  • Chen, J. and Patton, R. J., "Robust Model-Based Fault Diagnosis for Dynamic Systems", Kluwer Academic Publishers, 1999. ISBN: 0792384113.
  • Chiang, L. H. and Russel, E. L. and Braatz, R. D., "Fault Detection and Diagnosis in Industrial Systems", Springer-Verlag London Limited, 2001. Advanced Textbooks in Control and Signal Processing Series. London, Great Britain. ISBN: 1852333278.
  • Gertler, J., "Fault Detection and Diagnosis in Engineering Systems". Marcel Dekker, 1998, New York. ISBN: 0824794273.
  • Hadjicostis, Christoforos N., "Coding Approaches to Fault Tolerance in Combinational and Dynamic Systems", Kluwer Academic Publishers. November 2001. The Kluwer International Series in Engineering and Computer Science. ISBN: 0792376242.
  • Liu, G. P. and Patton, R. J., "Eigenstructure Assignment for Control System Design", John Wiley and Sons. England, 1998. ISBN: 0471975494.
  • Patton, R. J. and Frank, P. M. and Clark, R. N., "Fault Diagnosis in Dynamic Systems, Theory and Application", Prentice Hall. 1989, London. Control Engineering Series. ISBN: 0133082636.
  • Patton, R. J. and Frank, P. M. and Clark, R. N., "Issues of Fault Diagnosis for Dynamic Systems", Springer-Verlag, 2000. London Limited. ISBN: 3540199683.


    Downloads: Application Examples

  • "Model-Based Fault Diagnosis for Industrial Processes" (Silvio Simani's Extended Report, October 2007): (PDF file, 35 MB).
  • "Lecture Notes, Chapters 1 and 2" (Chapters form Silvio Simani's Extended Report, October 2007): (PDF file, 1 MB).
  • "Model-based fault-detection and diagnosis - status and applications" (Journal Paper by Rolf Isermann, 2005): (PDF file, 1 MB).
  • Design Example of Output Observer for FDI. Example with Noise (Matlab and Simulink files and models for Matlab 6.1): (zipped Matlab and Simulink files, 7 KB).
  • Design Example of Output Observers for FDI. SIMO Model with three Observers. (2 Matlab files and 1 Simulink model for Matlab 6.1): (zipped Matlab and Simulink files, 5 KB).
  • Design Example of a Kalman filter. Model with noise errors. Generation of minimal variance estimation error signals. (Matlab and Simulink files for Matlab 6.1): (zipped Matlab and Simulink files, 5 KB).
  • Kalman filter for Fault Diagnosis. Model with noise errors and output sensor fault. Residual statistical tests. (Matlab and Simulink files for Matlab 6.1): (zipped Matlab and Simulink files, 7 KB).
  • Examples of nonlinear models and neural network training. Zipped Matlab and Simulink directories (14 MB).
  • Examples of integration of neural networks and fuzzy models with dynamic observers and filters for fault detection and isolation. Zipped Matlab and Simulink directories (5 MB).


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