ME home
 
  SME FaceBook SME Twitter SME LinkedIn RSS Feed

Subscriber or
SME Member Log On

WEB-ONLY CONTENT

Go to SME eNEWS

MINING INDUSTRY EVENTS

2017 George A. Fox Conference  - Conference
Jan 23, 2019
2019 SME Annual Conference & Expo  - Conference
Feb 24, 2019 - Feb 27, 2019
CMA 121st Nat'l Western Mining Conference  - Conference
Feb 24, 2019 - Feb 27, 2019
Fuutre of Mining Australia 2019  - Conference
Mar 25, 2019 - Mar 26, 2019

METAL PRICES


Au
Ag
Pt
Pd
Ni
Cu
Al
Pb

AGGREGATES
AND MINERALS
MARKETPLACE


http://aggregatesmineralsmarketplace.com
The Mining Engineering, SME and NSSGA
Online Buyers Directory Site
The Online Global Mining and Minerals Library Site
March 2016
Volume 68    Issue 3

Measuring the effectiveness of mining shovels

Mining Engineering, 2016, Vol. 68, No. 3, pp. 45-50
Dindarloo, S.R.; Siami-Irdemoosa, E.; Frimpong, S.

DOI: https://doi.org/10.19150/me.6501

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.


Please login to access this article.

OR

If you are not an SME member, you can join SME by clicking the button below.