Nonlinear Dynamic Process Monitoring Using Canonical Variate Analysis and Kernel Density Estimations
By Odiowei P. Pabara-Ebiere, Yi Cao. IEEE Transactions on Industrial Informatics, 2010
330 citations recorded by the source corpus.
Subfields: Control and Systems Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/888da590795c92ab59b9d7c6bfe413d8862243c1
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In Control and Systems Engineering
In Mechanical Engineering
Citations in the corpus
- Improved PLS Focused on Key-Performance-Indicator-Related Fault Diagnosis (2015, background): “On the other hand, based on the large amount of data from available sensor measurements, event-logs as well as records, data-driven techniques have received considerable attention nowadays for control and monitoring purposes [9]–[13].”
- 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].”