Fault Diagnosis Inverse Problems: Solution with Metaheuristics

• Κύριοι συγγραφείς: Camps Echevarría, Lídice (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut), Llanes Santiago, Orestes (http://id.loc.gov/vocabulary/relators/aut), Campos Velho, Haroldo Fraga de (http://id.loc.gov/vocabulary/relators/aut), Silva Neto, Antônio José da (http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Έκδοση: 1st ed. 2019.
• Σειρά: Studies in Computational Intelligence, 763
• Θέματα: Mathematical models.; Computer mathematics.; Calculus of variations.; Mathematical Modeling and Industrial Mathematics.; Computational Science and Engineering.; Calculus of Variations and Optimal Control; Optimization.

 This book presents a methodology based on inverse problems for use in solutions for fault diagnosis in control systems, combining tools from mathematics, physics, computational and mathematical modeling, optimization and computational intelligence. This methodology, known as fault diagnosis - inverse problem methodology or FD-IPM, unifies the results of several years of work of the authors in the fields of fault detection and isolation (FDI), inverse problems and optimization. The book clearly and systematically presents the main ideas, concepts and results obtained in recent years. By formulating fault diagnosis as an inverse problem, and by solving it using metaheuristics, the authors offer researchers and students a fresh, interdisciplinary perspective for problem solving in these fields. Graduate courses in engineering, applied mathematics and computing also benefit from this work. 

 Chapter 01- Model-based Fault Diagnosis and Inverse Problems -- Chapter 02- Fault Diagnosis Inverse Problems -- Chapter 03- Metaheuristics for Optimization Problems -- Chapter 04- Applications of the Fault Diagnosis: Inverse Problem Methodology to Benchmark Problems -- Chapter 05- Final Remarks -- Appendix A- Implementation in Matlab of Differential Evolution with Particle Collision (DEwPC) -- Appendix B- Implementation in Mathlab of Particle Swarm Optimization with Memory (PSO-M) -- References.