Abstract
By projecting the data into a lower dimensional space that accurately characterizes the state of the process, dimensionality reduction techniques can greatly simplify and improve process monitoring procedures. Principal Component Analysis (PCA) is such a dimensionality reduction technique. It produces a lower dimensional representation in a way that preserves the correlation structure between the process variables, and is optimal in terms of capturing the variability in the data.
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© 2000 Springer-Verlag London
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Russell, E.L., Chiang, L.H., Braatz, R.D. (2000). Principal Component Analysis. In: Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes. Advances in Industrial Control. Springer, London. https://doi.org/10.1007/978-1-4471-0409-4_4
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DOI: https://doi.org/10.1007/978-1-4471-0409-4_4
Publisher Name: Springer, London
Print ISBN: 978-1-4471-1133-7
Online ISBN: 978-1-4471-0409-4
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