Mayne, Caleb (2014) Reliability-centered maintenance analysis of a Rio Tinto iron ore locomotive engine. [USQ Project]
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Abstract
This research project primarily aims to develop two pieces of knowledge:
- Identify whether Rio Tinto Iron Ore's (RTIO) current locomotive diesel engine maintenance regime is optimised.
- Identify the failure modes and the risk mitigated by each maintenance task that is performed.
The current maintenance program has been directly transferred from the Original Equipment Manufacturer (OEM) recommendations. As such, RTIO has not developed
an understanding of:
- How each maintenance task impacts the reliability of the locomotive fleet
- How much risk each maintenance task mitigates, and whether the task is worth-while
- Failure modes that are occurring but are not formally addressed in the maintenance management system
Without this knowledge, RTIO cannot be assured that the maintenance resources allocated to engine maintenance are utilised efficiently.
The literature reviewed did not find research that had analysed the maintenance regime applied to locomotive engines operating in a hot, semi-arid mining environment, revealing an opportunity for this research project to contribute to the body of knowledge on diesel engine maintenance.
The project applied the Reliability-Centered Maintenance (RCM) methodology to develop engine maintenance tactic recommendations. The data necessary for the analysis
was obtained from a variety of sources, including:
- Senior tradespeople, technicians and engineers
- Computerised maintenance records
- Production delay and failure records
- Component failure analysis reports
- Textbooks and academic research papers on engine failure analysis
- OEM training manuals
The RCM methodology inherently produces a database that details the failure modes and the risk mitigated by each maintenance task, providing a platform that can be
continually built on over time. As a result of this database, RTIO now possesses the knowledge necessary to evaluate the failure modes and the risk that is mitigated by each maintenance task, providing the foundation to make informed maintenance decisions as the operating context changes over time.
Finally, the maintenance task recommendations generated by the analysis were compared to the current maintenance tactics in order to establish the optimisation of the
current regime. The research project has concluded that the maintenance regime is generally optimised, but a number of minor improvements are identified.
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