Fault Detection of Non-Linear Processes Using Kernel Independent Component Analysis

By Jong-Min Lee, S. Qin, In-Beum Lee. 2008

194 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/8e52bdddcfaa3a32979eda4b89611fd4a0904dff

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Qingchao Jiang
2. Xue-feng Yan

In Control and Systems Engineering

1. Qingchao Jiang
2. Xue-feng Yan

In Statistics and Probability

1. Qingchao Jiang
2. Xue-feng Yan

Citations in the corpus

• Weighted kernel principal component analysis based on probability density estimation and moving window and its application in nonlinear chemical process monitoring (2013, methodology): “Themissed detection rates for each fault in the TE process using PCA [15], WPCA [30], KPCA [22], KICA [45], P-WKPCA, and PM-WKPCA are summarized in Table 1.”