Why autonomous assets are good for reliability – the impact of ‘operator-related component’ failures on heavy mobile equipment reliability

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Published Oct 2, 2017
Melinda R. Hodkiewicz Zac Batsioudis Tyler Radomiljac Mark T.W. Ho

Abstract

This study examines the maintenance records for components necessary for the comfort and safety of the operators of heavy mobile equipment. The results show that air conditioners, ladders, driver’s seats and mirrors and other required operator-related components can have a significant impact on an asset’s reliability. Analysis was conducted on 10 years of work orders for five identical 1400HP shovels and three identical 1470HP shovels. The results suggest that removing operator-related components contribute to a 15% decrease in the number of work orders and an 8% increase in reliability. In an autonomous asset these components would not be required. The key to this analysis is a rule-based expert system used to clean more than ten thousand work orders and allocate events to specific sub-systems with associated failure modes. While the mining industry has moved to autonomous haul trucks and drills, there are as yet no autonomous shovels. For manufacturers looking at the business case for these units, the availability of data on the reliability increase from removing the operator-related components will be valuable information.

How to Cite

Hodkiewicz, M. R., Batsioudis, Z., Radomiljac, T., & Ho, M. T. (2017). Why autonomous assets are good for reliability – the impact of ‘operator-related component’ failures on heavy mobile equipment reliability. Annual Conference of the PHM Society, 9(1). https://doi.org/10.36001/phmconf.2017.v9i1.2449
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Keywords

maintenance, reliability, operator, shovel, excavator, heavy mobile equipment, work order

References
Ansell, J. I. & Phillips, M. J. (1990). Practical reliability data analysis. Reliability Engineering & System Safety, vol. 28, pp. 337-356.
Caroni, C. (2010). "Failure limited" data and TTT-based trend tests in multiple repairable systems. Reliability Engineering & System Safety, vol. 95, pp. 704-706.
Delghandi, S. H., Sayadi, A. R. & Hoseinie, S. H. (2014). Reliability analysis of loading system of hydraulic excavator. International Conference on Reliability Engineering, Godkand:
Durrant-Whyte, H., Geraghty, R., Pujol, F. & Sellschop, R. (2015). How digital innovation can improve mining productivity. Metals and Mining, vol. November, pp.
Hall, R. A. & Daneshmend, L. K. (2003). Reliability modelling of surface mining equipment: data gathering and analysis methodologies. International Journal of Surface Mining, Reclamation and Environment, vol. 17, pp. 139-155.
Ho, M. T., Hodkiewicz, M. R., Pun, C., Petchey, J. & Li, Z. (2013). Asset Data Quality - A case study on mobile mining assets. 8th World Congress on Engineering Asset Management October, Hong Kong:
Ho, M. T. W. (2015). A shared reliability database for mobile mining equipment. Doctoral dissertation. University of Western Australia, Perth, Australia
Hodkiewicz, M. & Ho, M. T. W. (2016). Cleaning historical maintenance work order data for reliability analysis. Journal of Quality in Maintenance Engineering, vol. 22, pp. 146-163.
Hodkiewicz, M. R. (2015). Maintainer of the future. Australian Journal of Multi-Disciplinary Engineering, vol. 11, pp. 135-146.
Kumar, U., Klefsjo, B. & Granholm, S. (1989). Reliability investigation for a fleet of load haul dump machines in a Swedish Mine. Reliability Engineering & System Safety, vol. 26, pp. 341-361.
Louit, D. M., Pascual, R. & Jardine, A. K. (2009). A practical procedure for the selection of time-to-failure models based on the assessment of trends in maintenance data. Reliability Engineering & System Safety, vol. 94, pp. 1618-1628.
O'Connor, P. D. T. (2012). Practical Reliability Engineering, John Wiley & Sons Ltd.
Section
Technical Research Papers