A review of machine learning kernel methods in statistical process monitoring

By Anastasios Apsemidis, Stelios Psarakis, Javier M. Moguerza. 2020

102 citations recorded by the source corpus.

Abstract The complexity of modern problems turns increasingly larger in industrial environments, so the classical process monitoring techniques have to adapt to deal with those problems. This is one of the reasons why new Machine and Statistical Learning methodologies have become very popular in the statistical community. Specifically, this article is focused on machine learning kernel methods techniques in the process monitoring field. After explaining the idea of kernel methods we thoroughly examine the process monitoring articles that make use of kernel models and the way in which these models are combined with other Machine Learning approaches. Finally, we summarize the whole picture of the literature and mention some remarkable points.

Subfields: Control and Systems Engineering, Statistics and Probability