Sistemi di Supervisione Adattativi

  • Codice Google Classroom del Corso: rbdhkms
  • Link di Google Meet (per chi segue in remoto la lezione in presenza): meet.google.com/cid-xmdz-bez


    •     Supervision and Adaptive Systems. Modulo di 60 ore al I anno della Laurea Magistrale in Ingegneria Informatica e dell'Automazione, Laurea Magistrale in Meccanica, e Laurea in Informatica. Dipartimento di Ingegneria dell'Università di Ferrara
     
     

    Attenzione: L'accesso ai laboratori didattici sarà consentito SOLO agli studenti in possesso di "green pass" valido.


     
     

    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

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  • 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

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  • Residual Generation Techniques
  • The Residual Generation Problem

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

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  • Residual Robustness to Disturbances

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  • Application Examples

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    Downloads: Lecture Notes


     
     
     

    Filmati delle Lezioni di Teoria (Canale YouTube Ingegneria LM Informatica e dell'Automazione)

    0. Introduzione al Corso

    1. Elementi di Supervisione e di Diagnosi dei Guasti per Processi Dinamici

    2. Il Metodo della Stima Parametrica come Tecnica di Generazione dei Residui per il Rilevamento dei Guasti di Processo o Sistema

    3. Reti Neurali e Sistemi Fuzzy per la Modellistica e la Diagnosi dei Guasi nei Processi Dinamici

    4. Esempi Pratici di Progetto e Applicazione di Sistemi di Supervisione Adattativi a Processi Dinamici. Esercizi per la Preparazione all'Esame

    5. Esercitazioni Pratiche al PC in Laboratorio di Informatica INFO2


     
     
     

    References: Monographs and Textbooks on FDI

  • 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).
  • Chen, J. and Patton, R. J., "Robust Model-Based Fault Diagnosis for Dynamic Systems", Kluwer Academic Publishers, 1999. ISBN: 0792384113.

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    Downloads: Related Readings

  • "Model-Based Fault Diagnosis for Industrial Processes" (Silvio Simani's Extended Report, October 2007): (PDF file, 35 MB).
  • Parameter Estimation Examples for Fault Detection. Matlab and Simulink files and models for Matlab 6.1: (zipped Matlab and Simulink files, 5 KB).
  • Recursive Estimation Examples. 2 Matlab files for Matlab 6.1: (zipped Matlab and Simulink files, 2 KB).
  • 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).
  • Examples taken from Matlab Exchange Files Web Site: (i) implementation of a two-layers two-neurons network, (ii) multi-layer perceptron training with variable learning rate, (iii) character recognition GUI. Zipped Matlab file (495 KB).
  • Three examples taken from the web site of Prof. Robert Babuska, Intelligent Control and Robotics, Delft Center for Systems and Control, Faculty of Mechanical Engineering and Systems, Delft University of Technology. Interactive identification of static and dynamic systems. Zipped Matlab directory (213 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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