Abstract
In Section 4.7, it was shown how DPCA can be applied to develop an autoregressive with input ARX model and to monitor the process using the ARX model. The weakness of this approach is the inflexibility of the ARX model for representing linear dynamical systems. For instance, a low order autoregressive moving average ARMA (or autoregressive moving average with input ARMAX) model with relatively few estimated parameters can accurately represent a high order ARX model containing a large number of parameters [199].
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag London
About this chapter
Cite this chapter
Chiang, L.H., Russell, E.L., Braatz, R.D. (2001). Canonical Variate 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_7
Download citation
DOI: https://doi.org/10.1007/978-1-4471-0347-9_7
Publisher Name: Springer, London
Print ISBN: 978-1-85233-327-0
Online ISBN: 978-1-4471-0347-9
eBook Packages: Springer Book Archive