Rozpoznawanie czynności maszyn z użyciem metody grupowania

Marek KĘSEK; Romuald OGRODNIK; Marta PODOBIŃSKA-STANIEC

doi:10.29227/IM-2023-01-40

Machine Activity Recognition Using Clustering Method

Marek KĘSEK Ph.D., DSc, Eng.; AGH University of Science and Technology, Cracow, Poland https://orcid.org/0000-0001-6217-8435
Romuald OGRODNIK Ph.D. Eng.; AGH University of Science and Technology, Cracow, Poland https://orcid.org/0000-0003-4025-9191
Marta PODOBIŃSKA-STANIEC Ph.D. Eng.; AGH University of Science and Technology, Cracow, Poland https://orcid.org/0000-0002-3250-0646

DOI: https://doi.org/10.29227/IM-2023-01-40

Keywords: Machine Activity Recognition, clustering, process mining, performance of individual machines, operational efficiency

Abstract

Machine activity recognition is important for benchmarking and analysing the performance of individual machine, machine maintenance needs and automated monitoring of work progress. Additionally, it can be the basis for optimizing manufacturing processes. This article presents an attempt to use object clustering algorithms for recognizing the type of activity in the production complex. For this purpose, data from the production process and the k-means algorithm were used. The most common object clustering algorithms were also discussed. The results and the presented analysis approach demonstrate that this method can be successfully utilized in practice.

Published

2023-09-01

How to Cite

KĘSEK, M., OGRODNIK, R., & PODOBIŃSKA-STANIEC, M. (2023). Machine Activity Recognition Using Clustering Method. Test, 1(1 (51), 327–332. https://doi.org/10.29227/IM-2023-01-40

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Issue

Vol 1 No 1 (51) (2023): Inżynieria Mineralna

Section

ARTICLES

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