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

Melinda R. Hodkiewicz, Zac Batsioudis, Tyler Radomiljac, Mark Tien-Wei Ho

Research output: Chapter in Book/Conference paperConference paperpeer-review

6 Citations (Scopus)
309 Downloads (Pure)

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 1400 HP shovels and three identical 1470 HP 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.
Original languageEnglish
Title of host publicationProceedings of the Annual Conference of the Prognostics and Health Management Society 2017
EditorsMatthew J. Daigle, Anibal Bregon
PublisherPHM Society
Pages552-558
Number of pages7
Volume8
ISBN (Electronic)9781936263059
ISBN (Print)9781936263264
Publication statusPublished - 2017
EventAnnual Conference of the Prognostics and Health Management Society 2017 - St. Petersburg, United States
Duration: 2 Oct 20175 Oct 2017

Publication series

NameProceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
ISSN (Print)2325-0178

Conference

ConferenceAnnual Conference of the Prognostics and Health Management Society 2017
Country/TerritoryUnited States
CitySt. Petersburg
Period2/10/175/10/17

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