Title: Analysing human effect on the reliability of mining equipment

Authors: Burak Ozdemir; Mustafa Kumral

Addresses: Department of Mining and Materials Engineering, McGill University, 3450 University Street, Montreal, Quebec H3A 0E8, Canada ' Department of Mining and Materials Engineering, McGill University, 3450 University Street, Montreal, Quebec H3A 0E8, Canada

Abstract: In the mineral industries, the heavy equipment is widely used, and the operator performance is a significant factor for fulfilling intended functions of this equipment. This study aims to analyse and interpret the effect of the human factor on the reliability of the equipment used in the mining industry. In doing so, a combined approach based on the reliability analysis, statistical inference and a machine learning technique is proposed. A case study was conducted on haul trucks in a mining operation. The case study showed that the reliability drops of haul trucks vary in the range of 0.84% and 2.45% in a shift. It was also calculated that 16.9% of these reliability drops were associated with operator habits. A truck's condition at the beginning of a shift and the initial reliability contribute to 36.3% and 31.1% of the reliability drops, respectively. The findings can be used in scheduling training programs, short-term mine planning, and simulation of material handling systems.

Keywords: human factor; equipment reliability; scoring; machine learning.

DOI: 10.1504/IJHVS.2019.102716

International Journal of Heavy Vehicle Systems, 2019 Vol.26 No.6, pp.872 - 887

Received: 09 Dec 2017
Accepted: 14 May 2018
Published online: 02 Oct 2019 *

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