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Data-Driven Fault Detection and Reasoning for Industrial Monitoring (e-book) (used book) | bookbook.eu
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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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- Author: Jing Wang
- Publisher: Springer
- Year: 2022
- Pages: 284
- ISBN-10: 9811680469
- ISBN-13: 9789811680465
- Format: 15.6 x 23.4 x 1.5 cm, minkšti viršeliai
- Language: English English
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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