Case Study: Detecting bearing lubrication issue in a compressor with DataMind AI™
June 22, 2025
At a coal mining site, DataMind AI™ identified abnormal vibration behavior in one of the site’s critical compressors – far above what was observed in comparable units.
Rather than flagging a generic fault, the system leveraged AI-driven sensor fusion and comparative analytics to trace the issue to an unexpected cause: under-lubrication.
By correlating vibration patterns with maintenance records, DataMind AI™ revealed that only 20g of grease had been applied – well below the OEM-recommended 50g. Once corrected, vibration levels normalized, confirming that lubrication quantity was the true driver of the anomaly.
This precise diagnosis allowed the team to avoid unnecessary bearing replacement, prevent 5 hours of unplanned downtime, and save ~$140,000 – while improving asset reliability and operational safety.
Conclusion
This case demonstrates how DataMind AI™ moves beyond detection – delivering actionable insights that help maintenance teams pinpoint root causes, reduce unnecessary interventions, and make smarter, cost-effective decisions.
