Data-Driven Fault Detection and Reasoning for Industrial Monitoring - Couverture souple

Wang, Jing; Zhou, Jinglin; Chen, Xiaolu

Couverture souple

ISBN 10 : 9811680469 ISBN 13 : 9789811680465

Editeur : Springer, 2023

Synopsis

This open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring. This book is intended for senior undergraduate and graduate students who are interested in fault diagnosis technology, researchers investigating automation and industrial security, professional practitioners and engineers working on engineering modeling and data processing applications.

This is an open access book.

Détails de l'édition

• Éditeur: Springer
• Date d'édition: 2023
• Langue: anglais
• ISBN 10: 9811680469
• ISBN 13: 9789811680465
• Reliure: Broché
• Nombre de pages: 284
• Coordonnées du fabricant: non disponible
• Personne responsable: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Allemagne