Komatsu 930E Maintenance Strategy Optimistion

Industry: Mining Sector

Services Required: Maintenance Strategy Optimisation, FMECA

Location: Australia 

The Komatsu 930E Electric Drive Haul Truck has been has been used by the client to remove overburden across multiple open cut mines in Australia.

The client had identified that their 930E fleet had not achieved their forecast availability with numerous major component failures increasing the burden on maintenance teams. Holistic Asset Management (HolisticAM) was engaged by the mining company to undertake a holistic review their fleet of Komatsu 930E Electric Drive Trucks from a maintenance perspective.

The objective of this project was to understand key drivers for unavailability, identify recommendations and assist with the implementation by working closely with site and corporate key stakeholders.

The project utilised a hybrid onsite-offsite execution approach and was delivered on time and within budget. The HolisticAM team remained engaged post strategy implementation to evaluate it’s effectiveness.

THE CHALLENGE

Leveraging our OEM and site-based experience we understand the challenges that mining companies face. The client had a FMECA based maintenance strategy for the truck, however was experiencing significant unplanned failures. During planned maintenance (PM) services defects were often detected when close to failure resulting in significant maintenance over runs. Over runs combined with unplanned failures resulted in a significant maintenance burden on the maintenance teams. This posed additional challenges including:

  • Uncertainty in the quality of the preventive maintenance strategy (particularly associated with on-condition tasks and planned component changeouts)
  • Unknown maintenance efficiency of the maintenance execution team

The client had also indicated that the current tactics had diverted from the FMECA.

THE SOLUTION

The client chose HolisticAM based on their OEM, condition-monitoring expertise, on-site experience and proven track of delivering results in the mining industry.

As with all projects executed by HolisticAM, our methodical approach includes identifying the current state, identify and review opportunities for improvement and presenting these opportunities to the client for consideration.

HolisticAM worked closely with the client’s engineering, planning and maintenance execution team to undertake the following reliability engineering tasks:

  • Downtime Data Analysis to determine systems, subsystems and maintainable items that contributed to unplanned maintenance on the trucks
  • Life Data Analysis (LDA) / Weibull was undertaken to determine the Failure Characteristics, Mean Time Between Failure (MTBF) and reliability.
  • Review of service day maintenance execution across all the maintenance crews
  • Review of Mean Time to Repair (MTTR) to identify maintenance efficiency
  • Identification of truck upgrades released by the OEM and implemented by other sites to reduce unplanned failures and improve component reliability
  • Developing proposals using Net Present Value methodology to justify the implementation of machine upgrades with a focus to reduce total cost of ownership

THE RESULTS

Key deliverables included:

  • Updated FMECA-based maintenance strategy
  • Revised service sheets
  • Improvements on PM service-day maintenance execution
  • Recommendations to reduce MTTR
  • Truck upgrade/improvement documentation detailing priority and value add to the business

“HolisticAM has been pivotal in transforming how we undertake maintenance on our assets. Their product knowledge, professionalism and their site experience sets them apart”

Engineering Manager

Added Value

HolisticAM was able to create value by:

  • Over the next 6 months each trucks available time had increase by 112 hours.
  • Increase in fleet availability and MTBF > 90 hours consistently
  • Decrease in MTTR by decreasing time to identify, acknowledge, repair and verify.
  • FMECA-based maintenance strategy with tasks focusing on preventive maintenance to ensure machine issues are detected in a proactive manner

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