Principal Component Analysis of Process Datasets with Missing Values
Name
processes-05-00038.pdf
Description
Published version
Size
924.29 KB
Format
Adobe PDF
Checksum (MD5)
4540d69987511396fc93846729eea73c
Author(s)
Severson, Kristen
Molaro, Mark
Braatz, Richard D
Date Issued
July 2017
Journal
Processes
Publisher
MDPI AG
Citation
Severson, Kristen et al. “Principal Component Analysis of Process Datasets with Missing Values.” Processes 5, 4 (July 2017): 38. © 2017 The Authors
Version
Final published version
Abstract
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. This article considers missing data within the context of principal component analysis (PCA), which is a method originally developed for complete data that has widespread industrial application in multivariate statistical process control. Due to the prevalence of missing data and the success of PCA for handling complete data, several PCA algorithms that can act on incomplete data have been proposed. Here, algorithms for applying PCA to datasets with missing values are reviewed. A case study is presented to demonstrate the performance of the algorithms and suggestions are made with respect to choosing which algorithm is most appropriate for particular settings. An alternating algorithm based on the singular value decomposition achieved the best results in the majority of test cases involving process datasets. Keywords: principal component analysis; missing data; process data analytics; chemometrics; machine learning; multivariable statistical process control; process monitoring; Tennessee Eastman problem
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3390/pr5030038
