Data-driven fault detection and reasoning for industrial monitoring / Jing Wang, Jinglin Zhou, Xiaolu Chen.
By: Wang, Jing [author.]
Contributor(s): Zhou, Jinglin [author.] | Chen, Xiaolu [author.]
Language: English Series: Intelligent control and learning systems: volume 3.Publisher: Singapore : Springer, [2022]Copyright date: ©2022Description: 1 online resource (277 pages) : illustrations (chiefly color)Content type: text Media type: computer Carrier type: online resourceISBN: 9788981168049; 9789811680441; 9811680442; 8981168040Subject(s): Industrial engineering -- Data processing | Fault location (Engineering) -- Data processing | Industrial engineering | Automation | Industrial engineering -- Data processingGenre/Form: Electronic book.LOC classification: T57.5 | .W36 2022Online resources: Full text is available at the Directory of Open Access Books. Click here to view. Summary: 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.| Item type | Current location | Home library | Call number | Status | Date due | Barcode | Item holds |
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EBOOK/OPEN ACCESS
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COLLEGE LIBRARY | COLLEGE LIBRARY | Not for loan |
Includes bibliographical references.
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.
Description based upon print version of record.
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