Abstract
Load–haul–dump (LHD) machines are key assets in horizontal transportation in the underground mine. They perform ore haulage from mining faces to the nearest belt conveyors, dumping the material at the specific locations. Their operation can be described as cyclic. For maintenance staff of those machines, effectiveness-related demands are the greatest challenge, especially taking into consideration harsh and time-varying environmental conditions. The analysis of long-term data recorded on such machines can provide information about the changes in technical condition of the machine. Moreover, such observations can allow to track slowly progressing changes in technical condition that are effectively impossible to asses given only short-term measurement data. Besides the context of analyzing such data, it can be used as large-scale training dataset carrying a lot of useful information for the future development of diagnostic procedures. In this paper, authors propose statistics-based methodology for the analysis of long-term observations of diagnostic data recorded on LHD machines. Fusing information contained in various types of diagnostic variables (i.e., temperatures, pressures, operational parameters like engine rotational speed, torque, etc.) can allow to unravel underlying degradation processes occurring in the machine, with greatest focus on drive-related components.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Cutifani, M., Quinn, B., Gurgenci, H.: Increased equipment reliability, safety and availability without necessarily increasing the cost of maintenance. In: Mining Technology Conference, Freemantle, WA, pp. 10–11 (1996)
Gustafson, A., Schunnesson, H., Galar, D., Kumar, U.: The influence of the operating environment on manual and automated load-haul-dump machines: a fault tree analysis. Int. J. Min. Reclam. Environ. 27, 75–87 (2013)
Kumar, U.: Reliability analysis of load-haul-dump machines. Ph.D. thesis, Luleå Tekniska Universitet (1990)
Król, R., Zimroz, R., Stolarczyk, Ł.: Failure analysis of hydraulic systems used in mining machines operating in copper ore mine kghm polska miedz sa. Min. Sci. 128, 127 (2009)
Stefaniak, P., Zimroz, R., Obuchowski, J., Sliwinski, P., Andrzejewski, M.: An effectiveness indicator for a mining loader based on the pressure signal measured at a Bucket’s hydraulic cylinder. Procedia Earth Planet Sci. 15, 797–805 (2015)
Zimroz, R., Wodecki, J., Król, R., Andrzejewski, M., Sliwinski, P., Stefaniak, P.: Self-propelled mining machine monitoring system—data validation, processing and analysis. In: Mine Planning and Equipment Selection, pp. 1285–1294. Springer, Berlin (2014)
Gustafson, A., Lipsett, M., Schunnesson, H., Galar, D., Kumar, U.: Development of a Markov model for production performance optimisation. Application for semi-automatic and manual LHD machines in underground mines. Int. J. Min. Reclam. Environ. 28, 342–355 (2014)
Gustafson, A., Schunnesson, H., Galar, D., Kumar, U.: Production and maintenance performance analysis: manual versus semi-automatic LHDs. J. Qual. Maintenance Eng. 19, 74–78 (2013)
Laukka, A., Saari, J., Ruuska, J., Juuso, E., Lahdelma, S.: Condition-based monitoring for underground mobile machines. Int. J. Ind. Syst. Eng. 23, 74–89 (2016)
Wodecki, J., Stefaniak, P., Śliwiński, P., Zimroz, R.: Multidimensional data segmentation based on blind source separation and statistical analysis. In: Advances in Condition Monitoring of Machinery in Non-Stationary Operations, pp. 353–360. Springer, Berlin (2018)
Sawicki, M., Zimroz, R., Wyłomańska, A., Obuchowski, J., Stefaniak, P., Żak, G.: An automatic procedure for multidimensional temperature signal analysis of a SCADA system with application to belt conveyor components. Procedia Earth Planet Sci. 15, 781–790 (2015)
Wodecki, J., Stefaniak, P., Michalak, A., Wyłomańska, A., Zimroz, R.: Technical condition change detection using Anderson–Darling statistic approach for LHD machines engine overheating problem. Int. J. Min. Reclam. Environ. 1–9 (2017)
Freedman, D., Diaconis, P.: On the histogram as a density estimator: L2 theory. Zeitschrift für Wahrscheinlichkeitstheorie und Verwandte Gebiete 57, 453–476 (1981)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Michalak, A. et al. (2019). Condition Monitoring for LHD Machines Operating in Underground Mine—Analysis of Long-Term Diagnostic Data. In: Widzyk-Capehart, E., Hekmat, A., Singhal, R. (eds) Proceedings of the 27th International Symposium on Mine Planning and Equipment Selection - MPES 2018. Springer, Cham. https://doi.org/10.1007/978-3-319-99220-4_39
Download citation
DOI: https://doi.org/10.1007/978-3-319-99220-4_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99219-8
Online ISBN: 978-3-319-99220-4
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)