𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Principal-component analysis of multiscale data for process monitoring and fault diagnosis

✍ Scribed by Seongkyu Yoon; John F. MacGregor

Publisher
American Institute of Chemical Engineers
Year
2004
Tongue
English
Weight
293 KB
Volume
50
Category
Article
ISSN
0001-1541

No coin nor oath required. For personal study only.

πŸ“œ SIMILAR VOLUMES

Nonlinear Real-time Process Monitoring a
Nonlinear Real-time Process Monitoring and Fault Diagnosis Based on Principal Component Analysis and Kernel Fisher Discriminant Analysis
✍ X. Zhang; W.-W. Yan; X. Zhao; H.-H. Shao πŸ“‚ Article πŸ“… 2007 πŸ› John Wiley and Sons 🌐 English βš– 278 KB

## Abstract The aim of this paper is to propose a novel real‐time process monitoring and fault diagnosis method based on the principal component analysis (PCA) and kernel Fisher discriminant analysis (KFDA). There is a need to develop this method in order to overcome the inherent limitations of the

Principal component variable discriminan
Principal component variable discriminant plots: A novel approach for interpretation and analysis of multi-class data
✍ Nils B. Vogt πŸ“‚ Article πŸ“… 1988 πŸ› John Wiley and Sons 🌐 English βš– 271 KB πŸ‘ 1 views

Principal component analysis is a useful method for analysing data-matrices. By analysing separate class models, i.e. disjoint principal component modelling as in the SIMCA or FCVPC programs (developed for supervised and unsupervised principal component analysis respectively), the principal componen