Data-Driven Fault Detection and Reasoning for Industrial Monitoring

1st ed. 2022

by Jing Wang , Jinglin Zhou , Xiaolu Chen

Book Details

Format Hardback or Cased Book

Book Series Intelligent Control and Learning Systems

ISBN-10 9811680434

ISBN-13 9789811680434

Edition 1st ed. 2022

Publisher Springer Verlag, Singapore

Imprint Springer Verlag, Singapore

Country of Manufacture GB

Country of Publication GB

Publication Date Jan 4th, 2022

Print length 264 Pages

Weight 564 grams

Dimensions 16.20 x 24.10 x 2.50 cms

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This open access book assesses the potential of data-driven methods in industrial process monitoring engineering.

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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