Sistemi di Supervisione Adattativi


    Supervision and Adaptive Systems. Modulo di 60 ore per la Laurea Magistrale in Ingegneria dell'Automazione e Informatica, Laurea Magistrale in Meccanica, e Laurea in Informatica. Dipartimento di Ingegneria dell'Università di Ferrara
 
 

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

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  • Application Examples

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    Downloads: Lecture Notes

  • Introduction to Fault Diagnosis, Residual Generation and Evaluation, Robustness Problems and Related Issues. (PDF file, single page) ; (PDF file, 2 slides per page) .
  • Recursive Least Squares for Fault Diagnosis. (PDF file, single page) ; (PDF file, 2 slides per page) .
  • Neural Networks and Fuzzy Systems for Fault Diagnosis. (PDF file, single page) ; (PDF file, 2 slides per page) .
  • Fault Diagnosis Application Examples. See hands-on computer laboratory exercises.

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    Hands-On Computer Laboratory Exercises

  • 4/10/2018: exercise. Design of residual generator and evaluation schemes in noise-free and noisy environment with step fault. Simulation without measurement noise (Simulink file); Simulation with measurement noise (Simulink file) ; State-space model parameters (Matlab file) ; Residual graphs (Matlab file) ; Matlab script file and Simulink schemes in PDF format (PDF file) ; Lecture notes at LIM smartboard (PDF file) ; Matlab and Simulink files from laboratory hands-on (zipped file) .
  • 18/10/2018: 1st exercise. Design Example of Output Observers for FDI. SIMO Model with three Observers. Matlab file with the model and the observer design; Simulink model; Matlab file for esidual evaluation. File in PDF format with Matlab scripts and Simulink scheme.
  • 18/10/2018: 2nd exercise. Design Example of UIO. Model with disturbance. Generation of disturbance decoupled estimation errors and residuals. Matlab file with the model, the observer design and the UIO; Simulink model with OO and UIO. File in PDF format with Matlab scripts and Simulink scheme. Lecture notes at LIM smartboard (PDF file).
  • 25/10/2018: 1st exercise. Design Example of UIO for FDI. MIMO Model with disturbance and two faults. Generation of disturbance decoupled residuals for input sensor fault isolation. Matlab file with the model and the UIO for input fault isolation; Simulink model with the state-space system and the UIO for FDI. File in PDF format with Matlab scripts and Simulink scheme.
  • 25/10/2018: 2nd exercise. Kalman filter for Fault Diagnosis. Model with noise errors and output sensor fault. Example of residual statistical tests. Kalman filter design example (Matlab file); Comparison between output observer and Kalman filter (Simulink file); Example of statistical tests (Matlab function file). File in PDF format with Matlab scripts and Simulink scheme. Lecture notes at LIM smartboard (PDF file).
  • 08/11/2018: 1st exercise. Design Example of Kalman filter for FDI. SISO model with disturbance. Comparison with dynamic observer. Matlab file with the model and filter designs; Simulink model with schemes for FDI. File in PDF format with Matlab scripts and Simulink schemes.
  • 08/11/2018: 2nd exercise. Recursive parameter estimation for Fault Diagnosis. Model with parameter change. Example of parameter change detection. Implementation of RLS with forgetting factor. Application to FDI. Matlab function implementing the RLS algorithm with forgetting factor; Matlab script for showing the achieved results; Simulink file for data generation with parameter change example; Simulink file for data generation with input/output faults. PDF file with Matlab scripts and Simulink schemes. Lecture notes at LIM smartboard (PDF file).
  • 29/11/2018: 1st exercise. Design Example of a Radial Basis Function Neural Network. Application to the example from: "Neural Networks for Pattern Recognition", C. M. Bishop, Oxford University Press, 1995. Matlab file for RBF design and simulations; File in PDF format with Matlab script.
  • 29/11/2018: 2nd exercise. Design of MLP neural networks for dynamic model estimation and residual generation. Example of nonlinear process model derivation. Implementation of the MLP design and NN training. Script file for the initialization of the model parameters; Simulink model of the process; Script file for neural network training. Simulink file for NN data generation assuming that y(t) = F(u(t)); Simulink file for NN data generation assuming that y(t) = F(u(t), y(t-1)); Simulink file for NN data generation assuming that y(t) = F(u(t), u(t-1), y(t-1)); Simulink file for NN data generation assuming that y(t) = F(u(t), u(t-1), y(t-1), y(t-2)); Simulink file for NN data generation assuming that y(t) = F(u(t), u(t-1), u(t-2), y(t-1), y(t-2)); Simulink file for NN validation with y(t) = F(u(t)); Simulink file for NN validation with y(t) = F(u(t), y(t-1)); Simulink file for NN validation with y(t) = F(u(t), u(t-1), y(t-1)); Simulink file for NN validation with y(t) = F(u(t), u(t-1), y(t-1), y(t-2)); Simulink file for NN validation with y(t) = F(u(t), u(t-1), u(t-2), y(t-1), y(t-2)); Simulink file with NN for FDI with y(t) = F(u(t), u(t-1), u(t-2), y(t-1), y(t-2)). PDF file with Matlab scripts and Simulink schemes.
  • 04/12/2018: Example of Design of a Fuzzy Inference System for FDI. Design of the fuzzy system; fuzzy model estimation; fault diagnosis using a fuzzy model. Matlab file with process model parameters; Simulink file with process model for simulations; Matlab file for data clustering; Simulink file with data generation for fuzzy clustering: 1st step; Simulink file for fuzzy model validation: 1st step; Simulink file with data generation for fuzzy clustering: last step; Simulink file for fuzzy model validation: last step; Simulink file with fuzzy process model for fault diagnosis: final step; Matlab file with estimated fuzzy (FIS) models : .mat format; Zipped folder with Matlab and Simulink files; File in PDF format with Matlab scripts and Simulink models. Lecture notes at LIM smartboard (PDF file).
  • 06/12/2018 - 11/12/2018: Exam Exercise Example 1. Example of exam exercises. Matlab file with process model; Simulink file with process model for simulations; Zipped folder with Matlab and Simulink files; File in PDF format with the design requirements for the exam (in Italian). File in PDF format with Matlab scripts and Simulink models. Lecture notes at LIM smartboard (PDF file).
  • 13/12/2018: Exam Exercise Example 2. Example of exam exercises. Matlab file with process model; Matlab file with UIO and KF designs; Simulink file with UIO simulations; Simulink file with KF simulations; Matlab file for NN training; Simulink file with data generation for NN and fuzzy model training; Simulink file for NN validation; Matlab file for ANFIS design; Simulink file for fuzzy model validation; Zipped folder with complete Matlab and Simulink files; File in PDF format with the design requirements for the exam (in Italian). File in PDF format with Matlab scripts and Simulink models.
  • 13/12/2018: Exam Exercise Example 3. Example of exam exercises. Matlab file with process model; Simulink file with process model, measurement noise and faults; Matlab script file with basic code for UIO and KF designs; File in PDF format with the design requirements for the exam (in Italian).
  • Exam Example: Open and Multiple Choice Questions (in Italian). Example of open and multiple choice questions proposed for exam (in Italian). PDF text example.

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    References: 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).
  • Stamatis, D. H., "Failure Mode and Effect Analysis: FMEA from Theory to Execution",ASQ Quality Press, 2003, 2nd Edition, June, 2003. ISBN: 0873895983.
  • 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.

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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).
  • "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).
  • 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).
  • Design Example of UIO. Model with disturbance. Generation of disturbance decoupled estimation errors and residuals. (Matlab and Simulink files for Matlab 6.1): (zipped Matlab and Simulink files, 4 KB).
  • Design Example of UIO for FDI. MIMO Model with disturbance and two faults. Generation of disturbance decoupled residuals for input sensor fault isolation. (Matlab and Simulink files 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).
  • List of demos for the PERCEPTRON neural network example: "demop1", classification for a 2-input perceptron; "demop6", linearly non-separable input vectors; and selected from "nndtoc": "nnd3pc", perceptron classification - fruit example; "nnd4db", perceptron decision boundary; "nnd4pr" perceptron rule.
  • 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 of radial basis function (RBF) neural network taken from the Neural Network Design Table of Contents ("nndtoc", Chapter 11): "demorb1", "demorb3", and "demorb4", with different types and number of radial basis functions.
  • 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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