Moamar Sayed-Mouchaweh received his PhD 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 SpringerBriefs ‘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”.

• Prologue.- Wind Turbine Fault Localization: A Practical Application of Model-Based Diagnosis.- Fault detection and localization using Modelica and abductive reasoning.- Robust Data-Driven Fault Detection in Dynamic Process Environments Using Discrete Event Systems.- Critical States Distance Filter Based Approach for Detection and Blockage of Cyberattacks in Industrial Control Systems.- Active diagnosis for switched systems using Mealy machine modeling.- Secure Diagnosability of Hybrid Dynamical Systems.- Diagnosis in Cyber-physical systems with Fault Protection Assemblies.- Passive Diagnosis of Hidden-Mode Switched Affine Models with Detection Guarantees via Model Invalidation.- Diagnosability of Discrete Faults with Uncertain Observations.- Abstractions Refinement for Hybrid Systems Diagnosability Analysis.

Moamar Sayed-Mouchaweh received his PhD 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 SpringerBriefs ‘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”.

• Prologue.- Wind Turbine Fault Localization: A Practical Application of Model-Based Diagnosis.- Fault detection and localization using Modelica and abductive reasoning.- Robust Data-Driven Fault Detection in Dynamic Process Environments Using Discrete Event Systems.- Critical States Distance Filter Based Approach for Detection and Blockage of Cyberattacks in Industrial Control Systems.- Active diagnosis for switched systems using Mealy machine modeling.- Secure Diagnosability of Hybrid Dynamical Systems.- Diagnosis in Cyber-physical systems with Fault Protection Assemblies.- Passive Diagnosis of Hidden-Mode Switched Affine Models with Detection Guarantees via Model Invalidation.- Diagnosability of Discrete Faults with Uncertain Observations.- Abstractions Refinement for Hybrid Systems Diagnosability Analysis.