Table of Contents
Processes, Volume 5, Issue 3 (September 2017)
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Cover Story (view full-size image) Datasets with missing values arising from causes such as sensor failure, inconsistent sampling [...] Read more. 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 are typically applied during data pre-processing, but can also be applicable during model building. This article considers missing data within the context of principal component analysis (PCA), a method that has widespread industrial application for multivariate statistical process control. Algorithms for applying PCA to datasets with missing values are reviewed, and a case study using the Tennessee Eastman process is presented to demonstrate the performance of the algorithms. View this paper