Principal Component Analysis of Process Datasets with Missing Values

Principal Component Analysis of Process Datasets with Missing Values

Datasets with missing values arising from causes such as sensor failure, inconsistent sampling rates, and merging data from different systems are common in the process industry. Methods for handling missing data typically operate during data pre-processing, but can also occur during model building....

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Bibliographic Details
Main Authors: Kristen A. Severson, Mark C. Molaro, Richard D. Braatz
Format: Article
Language:English
Published: MDPI AG 2017-07-01
Series:Processes
Subjects:
principal component analysis
missing data
process data analytics
chemometrics
machine learning
multivariable statistical process control
process monitoring
Tennessee Eastman problem
Online Access:https://www.mdpi.com/2227-9717/5/3/38

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