Case Study: DataMind AI™ Prevents Conveyor Motor Failure by Detecting Hidden Bearing Fluting
May 25, 2025
At a large coal mining operation, Razor Labs deployed DataMind AI™ to monitor multiple conveyor drives – critical components responsible for continuous material transport. Shortly after deployment, the system flagged early-stage deterioration in one of the motors. While traditional monitoring tools indicated a general bearing issue, they failed to identify the actual root cause due to high noise levels.
DataMind AI leveraged advanced envelope demodulation and sensor fusion—combining vibration, current, and tacho data—to isolate operational noise and reveal fluting patterns: a form of electrical damage often missed by conventional methods. This precise diagnosis allowed the team to resolve a grounding issue before replacing the bearing, preventing recurring failures and extending component lifespan.
Thanks to this proactive approach, the site avoided 10 hours of unplanned downtime and approximately $191,000 in production losses.
Conclusion
This case highlights how DataMind AI moves beyond surface-level alerts—delivering actionable root cause diagnostics that improve maintenance accuracy, reduce costs, and protect operational continuity in mining and heavy-industry environments.
