| Vorliegende Sprache |
eng |
| Hinweise auf parallele Ausgaben |
181725068X Erscheint auch als (Druck-Ausgabe): ‡Wang, Jing: Data-driven fault detection and reasoning for industrial monitoring |
| ISBN |
978-981-16-8043-4 |
| 978-981-16-8045-8 |
| 978-981-16-8046-5 |
| Name |
Wang, Jing ¬[VerfasserIn]¬ |
| Zhou, Jinglin ¬[VerfasserIn]¬ |
| Name ANZEIGE DER KETTE |
Zhou, Jinglin ¬[VerfasserIn]¬ |
| Name |
Chen, Xiaolu ¬[VerfasserIn]¬ |
| T I T E L |
Data-Driven Fault Detection and Reasoning for Industrial Monitoring |
| Auflage |
1st ed. 2022. |
| Verlagsort |
Singapore |
| Singapore |
| Verlag |
Springer Singapore |
| Imprint: Springer |
| Erscheinungsjahr |
2022 |
| 2022 |
| 2022 |
| Umfang |
1 Online-Ressource(XVII, 264 p. 134 illus., 115 illus. in color.) |
| Reihe |
Intelligent Control and Learning Systems ; volume 3 |
| Intelligent control and learning systems |
| Band |
volume 3 |
| Notiz / Fußnoten |
Open Access |
| Titelhinweis |
Erscheint auch als (Druck-Ausgabe)ISBN: 978-981-16-8043-4 |
| Erscheint auch als (Druck-Ausgabe)ISBN: 978-981-16-8045-8 |
| Erscheint auch als (Druck-Ausgabe)ISBN: 978-981-16-8046-5 |
| Erscheint auch als (Druck-Ausgabe): ‡Wang, Jing: Data-driven fault detection and reasoning for industrial monitoring |
| ISBN |
ISBN 978-981-16-8044-1 |
| Klassifikation |
TJFM1 |
| TEC037000 |
| TBM |
| 629.892 |
| 629.8 |
| Kurzbeschreibung |
Introduction -- Basic Statistical Fault Detection Problems -- Principal Component Analysis -- Canonical Variate Analysis -- Partial Least Squares Regression -- Fisher Discriminant Analysis -- Canonical Variate Analysis -- Fault Classification based on Local Linear Embedding -- Fault Classification based on Fisher Discriminant Analysis -- Quality-Related Global-Local Partial Least Square Projection Monitoring -- Locality-Preserving Partial Least-Squares Statistical Quality Monitoring -- Locally Linear Embedding Orthogonal Projection to Latent Structure (LLEPLS) -- Bayesian Causal Network for Discrete Systems -- Probability Causal Network for Continuous Systems -- Dual Robustness Projection to Latent Structure Method based on the L_1 Norm. |
| 2. Kurzbeschreibung |
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. |
| SWB-Titel-Idn |
1785428977 |
| Signatur |
Springer E-Book |
| Bemerkungen |
Elektronischer Volltext - Campuslizenz |
| Elektronische Adresse |
$uhttps://doi.org/10.1007/978-981-16-8044-1 |
| Internetseite / Link |
Resolving-System |
