- Quality Quandaries: The Application of Principal Component Analysis for Process Monitoring
- Quality Engineering
- Volume | Issue number
- 18 | 1
- Pages (from-to)
- Document type
- Faculty of Science (FNWI)
- Korteweg-de Vries Institute for Mathematics (KdVI)
When monitoring industrial processes, it is increasingly
common that a large number of process
parameters are observed simultaneously over time.
In that case, the observations will most likely be
correlated with each other (contemporaneously) and
in time (temporarily). To gain understanding of the
behavior of a system, especially when troubleshooting
a process, graphical and statistical tools appropriate
for such multivariate situations can be helpful. This
column demonstrates a few techniques, mostly graphical,
that are useful when dealing with such situations.
We focus primarily on contemporaneous correlation.
Indeed, one of our goals is to demonstrate principal
component analysis (PCA), a method akin to a Pareto
analysis. We assume no prior knowledge of PCA,
and rather than taking a typical mathematical
approach, we focus on the geometry of PCA to
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