Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis
By E. L. Russell, Leo H. Chiang, Richard D. Braatz. 2000
490 citations recorded by the source corpus.
Subfields: Analytical Chemistry, Control and Systems Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/43d695e896f62619d9f7ead753686bafb8fa912e
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In Analytical Chemistry
In Control and Systems Engineering
In Mechanical Engineering
Citations in the corpus
- A Review on Basic Data-Driven Approaches for Industrial Process Monitoring (2014, methodology): “CVA is originally proposed as a dimensionality reduction technique [66], [67], based on which the fault detection could be achieved by using the pairs of variables selected from the input–output data set that maximize the correlation [46], [68], [69].”
- Neural Networks-Based Adaptive Finite-Time Fault-Tolerant Control for a Class of Strict-Feedback Switched Nonlinear Systems (2019, background): “Meanwhile, some well-known fault detection and estimation (FDE) approaches have been proposed, and a great deal of efforts have been devoted to several systems in the exist literatures [30], [31].”
- Quality-Related Process Monitoring Based on Total Kernel PLS Model and Its Industrial Application (2013, methodology): “independent component analysis (ICA) and PCA for TEP [26, 30, 33].”
- Data Mining and Analytics in the Process Industry: The Role of Machine Learning (2017, background): “should be employed to include the dynamic information of the process data [22], [23].”