Nonlinear process monitoring using kernel principal component analysis

By Jong-Min Lee, C. Yoo, S. Choi, Peter A. Vanrolleghem, In-Beum Lee. 2004

1,044 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Control and Systems Engineering, Mechanical Engineering

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. Jin Jiang
2. Qingchao Jiang
3. Xue-feng Yan
4. Jianping Ma

In Control and Systems Engineering

1. Jin Jiang
2. Qingchao Jiang
3. Xue-feng Yan
4. Jianping Ma

In Mechanical Engineering

1. Jin Jiang
2. Jianping Ma

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): “The T statistic is the sum of normalized squared scores, expressed as [9,19]”
• Applications of fault detection and diagnosis methods in nuclear power plants: A review (2011, methodology): “Recently, kernel-based machine learning techniques (Shawe-Taylor and Cristianini, 2004) have been used for pattern recognition (Vapnik, 1995) (Burges, 1998) and fault detection (Lee et al., 2004) (Widodo and Yang, 2007) (Mahadevan and Shah, 2009) (Ma and”