Data-Driven Fault Detection and Reasoning for Industrial Monitoring.

Format:

• Book

Author/Creator:

• Wang, Jing, 1974 April 21-

Contributor:

• Zhou, Jinglin.
• Chen, Xiaolu.

Series:

• Intelligent Control and Learning Systems
• Intelligent Control and Learning Systems ; v.3

Language:

English

Physical Description:

1 online resource (277 pages)

Place of Publication:

• Springer Nature 2022
• Singapore : Springer Singapore Pte. Limited, 2022.

Language Note:

English

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.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

981-16-8044-2

OCLC:

1292353116

Access Restriction:

Open access Unrestricted online access