Online fault diagnosis is crucial to ensure safe operation of complex dynamic systems in spite of faults affecting the system behaviors. Consequences of the occurrence of faults can be severe and result in human casualties, environmentally harmful emissions, high repair costs, and economical losses caused by unexpected stops in production lines. The majority of real systems are hybrid dynamic systems (HDS). In HDS, the dynamical behaviors evolve continuously with time according to the discrete mode (configuration) in which the system is. Consequently, fault diagnosis approaches must take into account both discrete and continuous dynamics as well as the interactions between them in order to perform correct fault diagnosis. This book presents recent and advanced approaches and techniques that address the complex problem of fault diagnosis of hybrid dynamic and complex systems using different model-based and data-driven approaches in different application domains (inductor motors, chemical process formed by tanks, reactors and valves, ignition engine, sewer networks, mobile robots, planetary rover prototype etc.). These approaches cover the different aspects of performing single/multiple online/offline parametric/discrete abrupt/tear and wear fault diagnosis in incremental/non-incremental manner, using different modeling tools (hybrid automata, hybrid Petri nets, hybrid bond graphs, extended Kalman filter etc.) for different classes of hybrid dynamic and complex systems.
Tabla de materias
Prologue.- Motor Fault Detection and Diagnosis Based on Meta-cognitive Random Vector Functional Link Network.- Optimal Adaptive Threshold and Mode Fault Detection for Model-based Fault Diagnosis of Hybrid Dynamical Systems.- Diagnosing Hybrid Dynamical Systems using Max-Plus Algebraic Methods.- Monitoring of Hybrid Dynamic Systems: Application to Chemical Process.- Hybrid Bond-Graph Possible Conflicts for Hybrid Systems Fault Diagnosis.- Hybrid System Model based Fault Diagnosis of Automotive Engines.- Diagnosis of Hybrid Systems Using Structural Model Decomposition.- Diagnosis of Hybrid Systems Using Hybrid Particle Petri Nets: Theory and Application on a Planetary Rover.- Diagnosis of Hybrid Dynamic Systems based on the Behavior Automaton Abstraction.- Index.
Sobre el autor
Moamar Sayed-Mouchaweh received his Ph D from the University of Reims-France. He was working as Associated Professor in Computer Science, Control and Signal processing at the University of Reims-France in the Research Center in Sciences and Technology of the Information and the Communication. In December 2008, he obtained the Habilitation to Direct Researches (HDR) in Computer science, Control and Signal processing. Since September 2011, he is working as a Full Professor in the High National Engineering School of Mines Department of Computer Science and Automatic Control. He edited the Springer book ‘Learning in Non-Stationary Environments: Methods and Applications‘, in April 2012 and wrote two Springer Briefs ‘Discrete Event Systems: Diagnosis and Diagnosability’, and ‘Learning from Data Streams in Dynamic Environments’. He was a guest editor of several special issues of international journals. He was IPC Chair of conference Chair of several international workshops and conferences (IEEE International Conference on Machine Learning and Applications IEEE International Conference on Evolving and Adaptive Intelligent Systems). He is working as a member of the Editorial Board of Elsevier Journal “Applied Soft Computing” and Springer Journals “Evolving systems” and “Intelligent Industrial Systems”.
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