Efficient Iterative Dynamic Kernel Principal Component Analysis Monitoring Method for the Batch Process with Super-large-scale Data Sets
Author:
Affiliation:
1. School of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou 121001, China
Funder
Natural Science Foundation of Liaoning Province
National Natural Science Foundation of China
Publisher
American Chemical Society (ACS)
Subject
General Chemical Engineering,General Chemistry
Link
https://pubs.acs.org/doi/pdf/10.1021/acsomega.0c06039
Reference27 articles.
1. A new monitoring method for the between‐mode transition of multimode processes
2. A systematic comparison of PCA-based Statistical Process Monitoring methods for high-dimensional, time-dependent Processes
3. Monitoring Uneven Multistage/Multiphase Batch Processes using Trajectory‐Based Fuzzy Phase Partition and Hybrid MPCA Models
4. Compound dimensionality reduction based multi-dynamic kernel principal component analysis monitoring method for batch process with large-scale data sets
5. Pseudo Time-Slice Construction Using a Variable Moving Window k Nearest Neighbor Rule for Sequential Uneven Phase Division and Batch Process Monitoring
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