Data-Driven Fault Detection and Reasoning for Industrial Monitoring

This book evaluates the potential of data-driven methods in industrial process monitoring engineering, focusing on process modeling, fault detection, classification, isolation, and reasoning. It provides a framework for fault diagnosis and implements statistical analysis methods for process monitoring, catering to senior undergraduate and graduate students, researchers, practitioners, and engineers.

Format: Hardback
Length: 264 pages
Publication date: 04 January 2022
Publisher: Springer Verlag, Singapore

It evaluates the potential of data-driven approaches in industrial process monitoring engineering, delving into detailed process modeling, fault detection, classification, isolation, and reasoning. These methods hold immense potential for enhancing the safety and reliability of industrial processes. Fault diagnosis, encompassing fault detection and reasoning, has garnered the attention of engineers and scientists from diverse fields such as control, machinery, mathematics, and automation engineering. By integrating diagnosis algorithms and real-world application cases, this book establishes a foundational framework for this domain and employs various statistical analysis methods for process monitoring. This book is specifically designed for senior undergraduate and graduate students with a keen interest in fault diagnosis technology, researchers exploring automation and industrial security, professional practitioners, and engineers engaged in engineering modeling and data processing applications.

Weight: 564g
Dimension: 162 x 241 x 25 (mm)
ISBN-13: 9789811680434
Edition number: 1st ed. 2022