Process curves are multivariate finite time series data coming from manufacturing processes. This paper studies machine learning that detect drifts in process curve datasets. A theoretic framework to synthetically generate process curves in a controlled way is introduced in order to benchmark...

As machine learning is widely applied to improve the efficiency and effectiveness of manufacturing systems, the automated selection of appropriate algorithms and hyperparameters becomes increasingly important. This paper presents a model selection approach to multivariate anomaly detection for...