Webinar: Beyond the Alarm – Real-Time Predictive Maintenance for Australia’s Mobile Fleets
August 23, 2026
Identifying Developing Faults Before OEM Alarms
Modern mining fleets generate thousands of operational signals every second. Yet traditional monitoring systems typically rely on fixed thresholds that may only trigger an alarm once a problem is already underway.
In this recorded webinar with Austmine, Michael Zolotov, CTO & Co-Founder of Razor Labs, explains how DataMind AI™ analyses relationships across onboard sensors and operational data to identify developing mobile fleet faults earlier.
The session explores how AI helps maintenance teams:
- Detect abnormal behaviour while individual signals remain within OEM limits
- Compare each truck against similar vehicles across the fleet
- Distinguish genuine equipment faults from temporary operating conditions
- Reduce alert noise and prioritise critical issues
- Turn diagnostics into recommended actions and SAP maintenance work orders
Michael also presents a practical example of a truck operating approximately 9°C hotter than the rest of the fleet without crossing its OEM alarm threshold.
Key Takeaways
- Fixed thresholds can miss faults developing across multiple signals
- Fleet-wide benchmarking reveals abnormalities that OEM alarms may overlook
- Operational context helps reduce false alarms
- AI provides failure-mode diagnostics and prioritised maintenance actions
- Closed-loop integration connects insights with maintenance execution
Want to identify mobile fleet faults before traditional alarms are triggered?
Schedule a discovery call to learn how DataMind AI™ can improve fleet reliability and reduce unplanned downtime.
