Abstract
The haul trucks are one of the most often used assets in horizontal transport in underground copper ore mining. This haulage process has a cyclic form and in simple terms the machine drives from point A to point B, where its cargo box is respectively loaded and dumped. What is most important in its basic performance assessment is to identify each cycle and its parametrization in terms of total duration, idling, fuel consumption, and driving speed. In the literature, we can find a few similar works but the majority of them is based on a poorly available hydraulic pressure signal of the actuator in cargo box unloading system or braking system pressure signal. Unfortunately, all of them are not robust and unreliable in real, noisy signals. For this reason, searching for an innovative new concept of the solving problem seems right in this state. This paper describes the new method of the operation cycles identification for underground haul trucks, which is based on multidimensional techniques of operational data analysis using machine learning. The leading idea consists of three parts: searching characteristic non-hydraulic values in signals which correspond to cycles, identifying distinctive periods in haulage process and splitting signal into cycles accordingly. In the first step, the data mining techniques are used to find significant variables, afterwards SVM classifying model identifies unloadings, which are then applied to cluster data by DBSCAN algorithm. The whole process is presented on haul trucks real data from KGHM Lubin mine.
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References
Assumpção, M.R.P., de Medeiros, C.A.: Estudo sobre eficiência técnica na extração de nióbio. Revista de Ciência & Tecnologia, 17(35), 115–128
Bakirci, F., Yakut, E., Demirci, A., Gündüz, M.: Efficiency measurement in Turkish coal enterprises using data envelopment analysis and data mining. Can. Soc. Sci. 10(1), 103–110 (2014)
Chamroukhi, F., Samé, A., Aknin, P., Govaert, G.: Model-based clustering with Hidden Markov Model regression for time series with regime changes. In: The 2011 International Joint Conference on Neural Networks, pp. 2814–2821. IEEE (2011)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 1–27 (2011)
Ester, M., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96. no. 34 (1996)
Frank, B., Skogh, L., Filla, R., Fröberg, A., Alaküla, M.: On increasing fuel efficiency by operator assistant systems in a wheel loader. In: International Conference on Advanced Vehicle Technologies and Integration (VTI 2012), Changchun, China (2012)
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(2), 75–87 (2013)
Jakkula, B., Mandela, G., et al.: Improvement of overall equipment performance of underground mining machines- a case study. Model. Measur. Control C, 79(1) (2018). https://doi.org/10.18280/mmc_c.790102
Krot, P., Sliwinski, P., Zimroz, R., Gomolla, N.: The identification of operational cycles in the monitoring systems of underground vehicles. Measurement 151, 107111 (2020)
Kumar, U., Parida, A., Duffuaa, S.O., Stenström, C., Galar, D.: Performance indicators and terminology for value driven maintenance. J. Qual. Maint. Eng. (2013)
Mohammadi, M., Rai, P., Gupta, S.: Performance measurement of mining equipment. Int. J. Emerg. Technol. Adv. Eng. 5(7), 240–248 (2015)
Noble, W.S.: What is a support vector machine? Nat. Biotechnol. 24(12), 1565–1567 (2006)
Polak, M., Stefaniak, P., Zimroz, R., Wyłomańska, A., Śliwiński, P., Andrzejewski, M.: Identification of loading process based on hydraulic pressure signal. In: The Conference Proceedings of 16th International Multidisciplinary Scientific Geoconference SGEM 2016, pp. 459–466 (2016)
Saari, J., Odelius, J.: Detecting operation regimes using unsupervised clustering with infected group labelling to improve machine diagnostics and prognostics. Oper. Res. Perspect. 5, 232–244 (2018)
Schölkopf, B., et al.: New support vector algorithms. Neural Comput. 12(5), 1207–1245 (2000)
Schubert, E., et al.: DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. ACM Trans. Database Syst. (TODS) 42(3), 1–21 (2017)
Si, X.S., Wang, W., Hu, C.H., Zhou, D.H., Pecht, M.G.: Remaining useful life estimation based on a nonlinear diffusion degradation process. IEEE Trans. Reliab. 61(1), 50–67 (2012)
Śliwiński, P., Andrzejewski, M., et al.: Selection of variables acquired by the on-board monitoring system to determine operational cycles for haul truck vehicle. In: Mueller, C., et al. (eds.) Mining Goes Digital © 2019. Taylor & Francis Group, London, ISBN 978-0-367-33604-2 (2019)
Stefaniak, P., Gawelski, D., Anufriiev, S., Śliwiński, P.: Road-quality classification and motion tracking with inertial sensors in the deep underground mine. In: Sitek, P., Pietranik, M., Krótkiewicz, M., Srinilta, C. (eds.) ACIIDS 2020. CCIS, vol. 1178, pp. 168–178. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-3380-8_15
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)
Widodo, A., Yang, B.-S.: Support vector machine in machine condition monitoring and fault diagnosis. Mech. Syst. Signal Process. 21(6), 2560–2574 (2007)
Wodecki, J., Stefaniak, P., Śliwiński, P., Zimroz, R.: Multidimensional data segmentation based on blind source separation and statistical analysis. In: Timofiejczuk, A., Chaari, F., Zimroz, R., Bartelmus, W., Haddar, M. (eds.) Advances in Condition Monitoring of Machinery in Non-Stationary Operations, pp. 353–360. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-61927-9_33
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This work is supported by EIT RawMaterials GmbH under Framework Partnership Agreement No. 17031 (MaMMa-Maintained Mine & Machine).
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Gawelski, D., Jachnik, B., Stefaniak, P., Skoczylas, A. (2020). Haul Truck Cycle Identification Using Support Vector Machine and DBSCAN Models. 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_28
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