Supervision and Condition Monitoring Strategies for Technical Processes: Model-Based and Data-Driven Methods

Supervision and Condition Monitoring Strategies for Technical Processes: Model-Based and Data-Driven Methods. Course of 4 hours for the Engineering Sciences PhD Program at the Engineering Department of Ferrara University - UNIFE Phd page

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

Goals

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 to present the control and supervision methodologies currently required and expected by modern industries and practical applications. The main goal of the course is to provide advanced topics and tools for the study, supervision, fault diagnosis, and control of complex dynamic systems, as well as their interconnections, subject to proper design constraints imposed by cost, speed, computational cost, robustness, reliability, sustainability, and power consumption.

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

Seminar Slides: (PDF file, single page).

Application Examples in Matlab and Simulink

Recursive Estimation for the Diagnosis of Parametric Faults of a Dynamic Process. Implementation of the RLS Algorithm with Forgetting Factor; Matlab script for the visualisation of the results; Simulink file for the data generation with parametric changes; Simulink file for the generation of input-output faults.
Recursive Estimation with Forgetting Factor and Parametric Faults of a Dynamic Process. Simulink file with RLS Simulink block (Matlab R2016).
Comparisons of the Recursive Estimations in Simulink for Parametric Fault Diagnosis. Simulink file with the estimate generation via the RLS Simulink block.
Design Example of the Observer Bank for a SIMO Model (3 Dynamic Observers) for FDI. Matlab script file with the process model and the observer design; Simulink model with output sensor faults; Simulink model with input sensor faults; Matlab script file for residual evaluation.
The Fuzzy c-means Algorithm for Fuzzy Clustering Partition. Fuzzy c-means algorithm implementation; fuzzy clustering of 2D data; fuzzy clustering of 3D data.
Modelling and Simulation of the Magnetic Levitation System for Fault Diagnosis. File with problem definition files (.zip file, 65kB); file with problem solutions (file .zip, 7MB).

Examination Method

If the rules of the PhD course of the candidate require an official exam, a project will be assigned for the implementation and analysis of the most interesting fault diagnosis strategy of interest for the candidate. It will aim to determine whether the student has acquired the course skills.
If the rules of the PhD course of the candidate do not require an official exam, an attendance certificate will be issued to the student, upon request.

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 Edition. 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 from 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).

Simani Home Page

Dipartimento di Ingegneria