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\subsection{One-class outlier detection}  \subsubsection{PCA}  One of the most used tools for multivariate process control is PCA so this is a natural starting point for our investigation. While PCA is notoriously sensitive to outliers and robust version are often preferred, the trainingset is void of outliers and the choice of algorithm should thus not have a big effect. A quick comparison of NIPALS, Bayesian PCA \citep{Oba_2003}, Probabilistic PCA and Robust PCA  \strong{PCA variants}  \subsubsection{Random Forest}