Supervised context classification methods for an industrial machinery

The paper describes a method of supervised context classification for an industrial machinery. The main objective of this study is to compare single and ensemble classifiers in order to classify groups of contexts which are based on an operating state of the device. The applied research was conducted with the assumption that only classic and well-practised classification methods would be adopted. The comparison study was carried out using real data recorded from an industrial machinery working underground in a mine in Poland. The achieved results confirm the effectiveness of the proposed approach and also show its limitations.