Comparison of Time Series Clustering Algorithms for Machine State Detection
Martin J.P. Hennig, Manfred Grafinger, Detlef Gerhard, Stefan Dumss, Patrick Rosenberger · Procedia CIRP · 2020
New developments in domains like mathematics and statistical learning and availability of easy-to-use, often freely accessible software tools offer great potential to transform the manufacturing domain and their grasp on the increased manufacturing data repositories sustainably. One of the most exciting developments is in the area of machine learning. Time series clustering could be utilized in machine state detection which can be used in predictive maintenance or online optimization. This paper presents a comparison of freely available time series clustering algorithms, by applying several combinations of different algorithms to a database of public benchmark technical data.