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Article

Enhancement of Machinery Activity Recognition in a Mining Environment with GPS Data

Faculty of Civil Engineering and Resource Management, AGH University of Science and Technology, 30-059 Cracow, Poland
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Author to whom correspondence should be addressed.
Academic Editor: Nikolaos Koukouzas
Energies 2021, 14(12), 3422; https://doi.org/10.3390/en14123422
Received: 6 May 2021 / Revised: 31 May 2021 / Accepted: 3 June 2021 / Published: 10 June 2021
(This article belongs to the Special Issue Mining Innovation)
Fast-growing methods of automatic data acquisition allow for collecting various types of data from the production process. This entails developing methods that are able to process vast amounts of data, providing generalised knowledge about the analysed process. Appropriate use of this knowledge can be the basis for decision-making, leading to more effective use of the company’s resources. This article presents the approach for data analysis aimed at determining the operating states of a wheel loader and the place where it operates based on the recorded data. For this purpose, we have used several methods, e.g., for clustering and classification, namely: DBSCAN, CART, C5.0. Our approach has allowed for the creation of decision rules that recognise the operating states of the machine. In this study, we have taken into account the GPS signal readings, and thanks to this, we have indicated the differences in machine operation within the designated states in the open pit and at the mine base area. In this paper, we present the characteristics of the selected clusters corresponding to the machine operation states and emphasise the differences in the context of the operation area. The knowledge obtained in this study allows for determining the states based on only a few selected most essential parameters, even without consideration of the coordinates of the machine’s workplace. Our approach enables a significant acceleration of subsequent analyses, e.g., analysis of the machine states structure, which may be helpful in the optimisation of its use. View Full-Text
Keywords: sensor data; mining machinery; activity recognition; clustering; GPS data sensor data; mining machinery; activity recognition; clustering; GPS data
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MDPI and ACS Style

Gackowiec, P.; Brzychczy, E.; Kęsek, M. Enhancement of Machinery Activity Recognition in a Mining Environment with GPS Data. Energies 2021, 14, 3422. https://doi.org/10.3390/en14123422

AMA Style

Gackowiec P, Brzychczy E, Kęsek M. Enhancement of Machinery Activity Recognition in a Mining Environment with GPS Data. Energies. 2021; 14(12):3422. https://doi.org/10.3390/en14123422

Chicago/Turabian Style

Gackowiec, Paulina, Edyta Brzychczy, and Marek Kęsek. 2021. "Enhancement of Machinery Activity Recognition in a Mining Environment with GPS Data" Energies 14, no. 12: 3422. https://doi.org/10.3390/en14123422

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