Statistical process monitoring with independent component analysis

By Jong-Min Lee, C. Yoo, In-Beum Lee. 2004

726 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Control and Systems Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/254fc90b678a6262ebe648250132a19d6477fe45

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. Zhiqiang Ge
2. Zhihuan Song
3. Biao Huang
4. Steven X. Ding
5. Qingchao Jiang
6. Xue-feng Yan

In Control and Systems Engineering

1. Zhiqiang Ge
2. Biao Huang
3. Steven X. Ding
4. Zhihuan Song
5. Qingchao Jiang
6. Xue-feng Yan
7. Zhiwei Gao
8. Shen Yin

In Signal Processing

1. Steven X. Ding
2. Zhiqiang Ge
3. Qingchao Jiang
4. Xue-feng Yan

Citations in the corpus

• Data Mining and Analytics in the Process Industry: The Role of Machine Learning (2017, methodology): “[56] developed a process monitoring method based on the ICA method, in which three statistics have been used for process monitoring.”
• A Review on Basic Data-Driven Approaches for Industrial Process Monitoring (2014, neutral): “ICA has been recently extended and applied for the purpose of fault detection, particularly for the processes whose measurements are non-Gaussian distributed [63], [64].”
• Weighted kernel principal component analysis based on probability density estimation and moving window and its application in nonlinear chemical process monitoring (2013, methodology): “[8].”