Measuring the Effectiveness of Mining Shovels

Abstract

Electric and hydraulic shovels are the dominant loading machinery in surface mining operations. Despite their critical role in production and their high capital and operating costs, no reliable and comprehensive quantitative performance metric is available. In this paper, a stochastic shovel effectiveness (SSE) measure is proposed for the purpose of quantifying the performance effectiveness of these shovels. The SSE is based on the widely used method of overall equipment effectiveness (OEE) in the manufacturing industry. The OEE measures the performance effectiveness of equipment by multiplying its mechanical availability, utilization and production quality. In manufacturing processes, quality rate is the ratio of the total number of products minus the number of defective products - equivalent to the number of acceptable products - to the total number of products. The SSE similarly uses the mechanical-availability and utilization terms, but instead of quality rate it uses a new parameter named bucket rate. The variability or randomness of the input data, that is, availability, utilization and bucket rate, are further incorporated into the SSE, and a final stochastic SSE distribution is derived in the form of a probability density function. One hydraulic and one electric shovel in a surface mining operation were selected to test the validity of the proposed method. The SSE scores for the two shovels, operating continuously for one year, were derived and compared. As with the OEE, the three-parameter SSE method yielded more representative results for overall performance measurement than a single-parameter approach. Using Monte Carlo simulation, a three-parameter Weibull and a normal distribution were derived for quantifying the overall effectiveness of hydraulic and electric shovels, respectively. As a decision aid, the proposed methodology promises to render a more informative tool than traditional metrics for mine equipment maintenance and management.

Department(s)

Mining Engineering

Keywords and Phrases

Electronic equipment; Machinery; Maintenance; Manufacturing; Mining; Monte Carlo analysis; Operations technology; Performance assessment; Probability density function; Stochasticity; Weibull theory

International Standard Serial Number (ISSN)

0026-5187

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2016 Society for Mining, Metallurgy and Exploration, All rights reserved.

Publication Date

01 Mar 2016

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