Fault Diagnosis Inverse Problems: Solution with Metaheuristics

Fault Diagnosis Inverse Problems: Solution with Metaheuristics

EnglishHardbackPrint on demand
Camps Echevarría, Lídice
Springer, Berlin
EAN: 9783319899770
Print on demand
Delivery on Monday, 27. of January 2025
CZK 2,370
Common price CZK 2,633
Discount 10%
pc
Do you want this product today?
Oxford Bookshop Praha Korunní
not available
Librairie Francophone Praha Štěpánská
not available
Oxford Bookshop Ostrava
not available
Oxford Bookshop Olomouc
not available
Oxford Bookshop Plzeň
not available
Oxford Bookshop Brno
not available
Oxford Bookshop Hradec Králové
not available
Oxford Bookshop České Budějovice
not available
Oxford Bookshop Liberec
not available

Detailed information

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.

EAN 9783319899770
ISBN 3319899775
Binding Hardback
Publisher Springer, Berlin
Publication date June 5, 2018
Pages 167
Language English
Dimensions 235 x 155
Country Switzerland
Readership Professional & Scholarly
Authors Campos Velho, Haroldo Fraga de; Camps Echevarria, Lidice; Llanes Santiago Orestes; Silva Neto, Antonio Jose da
Illustrations XVIII, 167 p. 68 illus., 52 illus. in color.
Edition 1st ed. 2019
Series Studies in Computational Intelligence