Data-Driven Fault Detection and Reasoning for Industrial Monitoring

By:

Wang, Jing [auth]

Contributor(s):

Material type: Article

ArticleLanguage: English Series: Intelligent Control and Learning SystemsPublication details: Springer Nature 2022Description: 1 electronic resource (264 p.)ISBN:

9789811680441

Subject(s):

Online resources:

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. This is an open access book.

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Holdings
Item type	Current library	Collection	Shelving location	Call number	Status	Notes	Date due	Barcode
Electronic edition	Bucheon University Library	Computers	OAPEN	004.8 I-69	Not for loan	Download (pdf)		1010987

Open Access star Unrestricted online access

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.

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English

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