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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© 2001 Springer-Verlag London
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Chiang, L.H., Russell, E.L., Braatz, R.D. (2001). Principal Component Analysis. In: Fault Detection and Diagnosis in Industrial Systems. Advanced Textbooks in Control and Signal Processing. Springer, London. https://doi.org/10.1007/978-1-4471-0347-9_4
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DOI: https://doi.org/10.1007/978-1-4471-0347-9_4
Publisher Name: Springer, London
Print ISBN: 978-1-85233-327-0
Online ISBN: 978-1-4471-0347-9
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