Data-driven fault detection and reasoning for industrial monitoring / Jing Wang, Jinglin Zhou, Xiaolu Chen.
Wang, Jing, author.; Zhou, Jinglin, author.; Chen, Xiaolu, author.
2022
T57.5 .W36 2022
Available Online

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Title
Data-driven fault detection and reasoning for industrial monitoring / Jing Wang, Jinglin Zhou, Xiaolu Chen.
Author
Wang, Jing, author.
ISBN
9789811680441 (electronic bk.)
9811680442 (electronic bk.)
9789811680434
9811680434
DOI
https://doi.org/10.1007/978-981-16-8044-1
Published
Singapore : Springer, [2022]
Copyright
©2022
Language
English
Description
1 online resource (277 pages) : illustrations (chiefly color).
Item Number
10.1007/978-981-16-8044-1 doi
Call Number
T57.5 .W36 2022
Dewey Decimal Classification
658.5
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.
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Open access
Digital File Characteristics
text file PDF
Source of Description
Description based upon print version of record.
Added Author
Zhou, Jinglin, author.
Chen, Xiaolu, author.
Series
Intelligent control and learning systems ; volume 3.
Available in Other Form
Data-Driven Fault Detection and Reasoning for Industrial Monitoring
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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.

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