Titlebook: Data-Driven Fault Detection and Reasoning for Industrial Monitoring; Jing Wang,Jinglin Zhou,Xiaolu Chen Book‘‘‘‘‘‘‘‘ 2022 The Editor(s) (i

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CHIP

Multivariate Statistics Between Two-Observation Spaces,easurement spaces. The vast majority of smart manufacturing problems, such as soft measurement, control, monitoring, optimization, etc., inevitably require modeling the data relationships between the two kinds of measurement variables. This chapter’s subject is to discover the correlation between th

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冥界三河

,Soft-Transition Sub-PCA Monitoring of?Batch Processes,s, such as fermentation, polymerization, and pharmacy, is highly sensitive to the abnormal changes in operating condition. Monitoring of such processes is extremely important in order to get higher productivity. However, it is more difficult to develop an exact monitoring model of batch processes th

盡忠

arbovirus

arbovirus

Fault Identification Based on Local Feature Correlation,as PCA, PLS, CCA, and FDA, are only suitable for solving the high-dimensional data processing with linear correlation. The kernel mapping method is the most common technique to deal with the nonlinearity, which projects the original data in the low-dimensional space to the high-dimensional space thr

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沉默

Locality-Preserving Partial Least Squares Regression,e and structure-preserving properties of LPP into the PLS model. The core of LPPLS is to replace the role of PCA in PLS with LPP. When extracting the principal components of . and ., two conditions must satisfy: (1) . and . retain the most information about the local nonlinear structure of their res

鋼筆尖

Locally Linear Embedding Orthogonal Projection to Latent Structure, Monitoring process variables and their associated quality variables is essential undertaking as it can lead to potential hazards that may cause system shutdowns and thus possibly huge economic losses. Maximum correlation was extracted between quality variables and process variables by partial least