Abstract
In underground mining of metal ores, horizontal transport of material is performed using self-propelled machines, especially Load-Haul-Dump machines. For example, in KGHM underground mines, where room-and-pillar system is used to deposit exploitation, the haulage process is provided by wheel loaders and haul trucks with suitably adjusted operation configuration. In case of shorter haulage routes, only wheel loaders take part in haulage process. Currently, there is observed a global tendency reliant on develop predictive maintenance as well as navigation or production optimization using Industrial Internet of Things (IIOT). Unfortunately, analytics development in this domain requires full insight into machine’s workflow in mining excavations and multivariate analysis in order widely understanding of machine operating contexts. In this article, a quick method to haulage cycle identification on example of wheel loader has been proposed. Developed algorithm is based on hydraulic pressure signal segmentation which provides to recognize loading operation, haulage and return of machine to mining face after unloading material in dumping point. The method is based on smooth hydraulic pressure signal in order to reduce signal interference but introduce to apply a convolution of smoothed signal with inverted step function.
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Change history
19 November 2020
In the originally published version of the chapter 27, the first names and surnames of the authors were used in an incorrect order. This has been corrected.
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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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Koperska, W., Skoczylas, A., Stefaniak, P. (2020). A Simple Method of the Haulage Cycles Detection for LHD Machine. 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_27
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DOI: https://doi.org/10.1007/978-3-030-63119-2_27
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