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Data-driven fault detection and reas...

Wang, Jing.

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Data-driven fault detection and reasoning for industrial monitoring

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data-driven fault detection and reasoning for industrial monitoring/ by Jing Wang, Jinglin Zhou, Xiaolu Chen.
Author:	Wang, Jing.
other author:	Zhou, Jinglin.
Published:	Singapore :Springer Singapore : : 2022.,
Description:	xvii, 264 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Industrial engineering - Data processing. -
Online resource:	https://doi.org/10.1007/978-981-16-8044-1
ISBN:	9789811680441

Data-driven fault detection and reasoning for industrial monitoring
Wang, Jing.

Data-driven fault detection and reasoning for industrial monitoring[electronic resource] /by Jing Wang, Jinglin Zhou, Xiaolu Chen. - Singapore :Springer Singapore :2022. - xvii, 264 p. :ill., digital ;24 cm. - Intelligent control and learning systems,v. 32662-5466 ;. - Intelligent control and learning systems ;v. 3..

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.

Open 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.

ISBN: 9789811680441

Standard No.: 10.1007/978-981-16-8044-1doiSubjects--Topical Terms:

1621221
Industrial engineering
--Data processing.

LC Class. No.: T57.5 / .W36 2022

Dewey Class. No.: 658.5

Data-driven fault detection and reasoning for industrial monitoring
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245 1 0 $a Data-driven fault detection and reasoning for industrial monitoring $h [electronic resource] / $c by Jing Wang, Jinglin Zhou, Xiaolu Chen.
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300 $a xvii, 264 p. : $b ill., digital ; $c 24 cm.
490 1 $a Intelligent control and learning systems, $x 2662-5466 ; $v v. 3
505 0 $a 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.
506 $a Open access.
520 $a 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.
650 0 $a Industrial engineering $x Data processing. $3 1621221
650 0 $a Fault location (Engineering) $x Data processing. $3 3237797
650 1 4 $a Robotics and Automation. $3 1066695
650 2 4 $a Computational Intelligence. $3 1001631
700 1 $a Zhou, Jinglin. $3 3592688
700 1 $a Chen, Xiaolu. $3 3592689
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Intelligent control and learning systems ; $v v. 3. $3 3592690
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