Abstract
This paper outlines the recommendation of analytical tools likely to be derived from the data recorded within the industrial automation system. The means might facilitate optimization of process efficiency, especially in terms of energy efficiency. Basically, each electromechanical device is electrically charged and controlled by the industrial automation system. A kind of the signal usually depends on various operational modes of the given device which are classified by its load. Available signal segmentation and statistical methods lead to the automatic identification of these modes and working patterns or abnormal performances caused by poor technical condition. Therefore, simple electrical signal allows to count the real device performance time and utilities usage, to identify its working modes, to recognize process losses, to specify KPI factors and to develop diagnostics. This paper describes multidimensional processing of conveyor stream data along with their exemplary use in real-time data. The algorithm of identifying operational regimes is characterized based on machine learning and further in-context analyses paired with visualisations.
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Notes
- 1.
Working shift - for the workers it usually means 7.5 or 6 h under the ground.
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Acknowledgment
This work is supported by EIT RawMaterials GmbH under Framework Partnership Agreement No. 17031 (MaMMa-Maintained Mine & Machine).
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Stefaniak, P., Śliwiński, P., Duda, N., Jachnik, B. (2020). Multidimensional Analysis of SCADA Stream Data for Estimating the Energy Efficiency of Mining Transport. In: Hernes, M., Wojtkiewicz, K., Szczerbicki, E. (eds) Advances in Computational Collective Intelligence. ICCCI 2020. Communications in Computer and Information Science, vol 1287. Springer, Cham. https://doi.org/10.1007/978-3-030-63119-2_26
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