
case Studies
Case Study
Endpoint MP2 Pilot Saves >$10M: Early Fault Detection Prevents Major Mining Failures
This case study details a four-month pilot program where GreaseBoss tested its Endpoint MP2 device on mining excavators and ancillary equipment 1 .
- Prevent major equipment failures and save over $10 million in downtime and repair costs.
- Detect critical lube system faults, like pump electrical issues, significantly faster than OEM monitoring systems.
- Identify hidden problems such as mistuned autolube systems leading to uneven greasing and incorrect timer settings causing over-greasing.
- Gain system-wide insights by indirectly detecting failures (like bypassing injectors) on components not directly monitored.

Case Study
Eliminating Costly Mining Sizer Bearing Failures with Precision Lubrication
- Eliminate costly grease-related bearing failures and associated production losses.
- Achieve exceptional ROI with $60m in forecasted annual savings from a $50k investment.
- Ensure precise lubrication with real-time data comparing planned versus actual grease volumes.
- Proactively detect equipment issues like failing autolube pumps before they cause major damage.

Case Study
ELIMINATING EXCAVATOR PIN & BUSH FAILURES
A Tier 1 Australian coal mine was experiencing regular excavator pin and bush failures, costing up to $10 million AUD in downtime plus significant repair costs, due to a lack of visibility into actual grease application. GreaseBoss’s Critical Point Monitoring system provided this crucial data, quickly identifying issues like bypassing injectors and incorrect grease volumes, enabling early intervention.
- Prevent catastrophic excavator pin and bush failures and associated multi-million dollar downtime losses.
- Gain essential visibility into actual grease volumes delivered to critical excavator lubrication points.
- Identify lubrication system anomalies and injector faults far earlier than standard equipment monitoring allows.
- Reduce expensive repair costs and minimise equipment downtime through proactive, data-driven maintenance.
