Abstract
Machine-level energy efficiency assessment supports the rapid detection of many technological problems related to a production cycle. The fast growth of data mining techniques has opened new possibilities that permit large amounts of gathered energy consumption data to be processed and analyzed automatically. However, the data that are available from control systems are not usually ready for such an analysis and require complex preparation – cleaning, integration, selection and transformation. This paper proposes a methodology for energy consumption data analysis that is based on a knowledge discovery application. The input information includes observations of the production system behavior and related energy consumption data. The proposed approach is illustrated on the use case of an energy consumption analysis that ws prepared for an automatic production line used in electronic manufacturing.
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Acknowledgment
This work was supported by the European Union from the FP7-PEOPLE-2013-IAPP AutoUniMo project “Automotive Production Engineering Unified Perspective based on Data Mining Methods and Virtual Factory Model” (grant agreement no. 612207) and research work financed from the funds for science in the years 2016–2017, which are allocated to an international co-financed project (grant agreement no. 3491/7.PR/15/2016/2).
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Cupek, R., Duda, J., Zonenberg, D., Chłopaś, Ł., Dziędziel, G., Drewniak, M. (2017). Data Mining Techniques for Energy Efficiency Analysis of Discrete Production Lines. In: Nguyen, N., Papadopoulos, G., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2017. Lecture Notes in Computer Science(), vol 10449. Springer, Cham. https://doi.org/10.1007/978-3-319-67077-5_28
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DOI: https://doi.org/10.1007/978-3-319-67077-5_28
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