Condition: New.
Seller: Ria Christie Collections, Uxbridge, United Kingdom
£ 40.27
Quantity: Over 20 available
Add to basketCondition: New. In.
Condition: As New. Unread book in perfect condition.
Condition: New.
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Condition: New.
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Condition: As New. Unread book in perfect condition.
Condition: New.
Language: English
Published by Springer Nature Singapore, 2022
ISBN 10: 9811680434 ISBN 13: 9789811680434
Seller: Buchpark, Trebbin, Germany
Condition: Hervorragend. Zustand: Hervorragend | Seiten: 284 | Sprache: Englisch | Produktart: Bücher | 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.
Seller: Majestic Books, Hounslow, United Kingdom
Condition: New. Print on Demand.
Seller: Biblios, Frankfurt am main, HESSE, Germany
Condition: New. PRINT ON DEMAND.
Language: English
Published by Springer Nature Singapore, 2022
ISBN 10: 9811680434 ISBN 13: 9789811680434
Seller: moluna, Greven, Germany
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Evaluates the practicality of data-driven methods in industrial process monitoringEmbeds manifold learning technology into multivariate statistical methodsIntroduces partial least absolute technology to provide valuable guidanceThis .