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Dr. Nasar Aldian Shashoa,
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TITLE : ANALYTICAL REDUNDANCY APPROACH FOR FAULT DETECTION
(Hybrid)
Abstract
Fault detection, isolation and Diagnosis are now being integrated into practical control systems to improve the safety and reliability of these systems. Fault detection and isolation (FDI) using analytical redundancy methods and also called model based techniques are currently the subject of extensive research and numerous surveys can be found. The analytical methods compare real process data to those obtained by mathematical models of the system. The aim of analytical redundancy is to generate information about faults which have occurred in target systems using actual measurements. Analytical redundancy is referred to as a model-based method, which is low-cost compared to hardware redundancy in some safety critical applications, provided that a model can precisely simulate the behavior of a real system. The most common popular analytical redundancy techniques are parameter estimation, parity relation and observer-based approaches. A model based fault detection techniques consists of two main stages: residual generation and residual evaluation. In most practical cases, the process parameters are not known at all, or they are not known exactly enough. Then, they can be determined by means of parameter estimation methods, measuring input and output signals, if the basic structure of the model is known. This approach is based on the assumption that the faults are reflected in the physical system parameters and the basic idea is that the parameters of the actual process are estimated on-line using well-known parameter estimations methods. The results are thus compared with the parameters of the reference model, obtained initially under fault free assumptions. Any discrepancy can indicate that a fault may have occurred. Researchers are invited to contribute their original scholarly work for publication in this special session. The scope of the session includes, but is not limited to, the following
Key-Topics:
Least-Squares-Based Iterative Identification Algorithms ; Parity Equations with Transfer Functions ; Recursive Least Squares (RLS) Parameter Estimation Algorithms ; Data Filtering Based (RLS) Algorithms ; Data Filtering Based Iterative Least Squares Algorithms ; Parity Equations with State Space Models Data Filtering Based maximum likelihood estimation algorithms ; Observer-Based Approaches methods
Biography
Nasar Aldian Ambark Shashoa holds a PhD in control systems from the University of Belgrade, Serbia. He is currently a Professor at Department of Electrical and Computer Engineering, School of Applied Sciences and Engineering, Libyan Academy for Graduate Studies, specializing in Control Engineering. His research interests encompass control engineering, system identification, fault detection and isolation, and pattern recognition. Dr. Shashoa has published more than forty five papers in international journals and conferences, presenting original research findings. He actively participates in various international conferences as a member of the international program committee, steering committee, and scientific committee in different countries. Additionally, Dr. Shashoa holds leadership positions as the Libyan Control Systems and Signal Processing Joint Societies Chapter Chair and Vice President of ISKO-Maghreb (Libya branch). He is an IEEE Senior Member, as well as a Member of the IEEE Control Systems Society and IEEE Signal Processing Society.