A method to benchmark high-dimensional process drift detection
This work addresses the need for reliable drift detection in manufacturing processes, but it is incremental as it focuses on benchmarking and evaluation rather than proposing a new detection method.
The paper tackles the problem of detecting drifts in high-dimensional process curves from manufacturing by introducing a synthetic data generation framework and a new evaluation metric called temporal area under the curve. It benchmarks existing machine learning algorithms, revealing that they often struggle with datasets containing multiple drift segments.
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 machine learning algorithms for process drift detection. An evaluation score, called the temporal area under the curve, is introduced, which allows to quantify how well machine learning models unveil curves belonging to drift segments. Finally, a benchmark study comparing popular machine learning approaches on synthetic data generated with the introduced framework is presented that shows that existing algorithms often struggle with datasets containing multiple drift segments.