Case Study: DataMind AI™ Prevents Sinter Fan Failure by Detecting Lubrication Issues and Mechanical Looseness
March 31, 2025
DataMind AI™ was deployed at a chrome smelter to monitor critical assets, including sinter fans responsible for maintaining airflow and process stability during the sintering stage. Any failure in these fans can cause serious disruption to production.
DataMind AI™ identified early signs of lubrication degradation and mechanical looseness in one of the fans – well before traditional monitoring systems would have flagged it. The system detected a shift in vibration patterns and escalated the issue to Alarm, enabling the maintenance team to act in time.
By replacing the bearing during a planned shutdown, the team avoided unplanned downtime and protected equipment from further damage.
Conclusion
This case demonstrates how DataMind AI™ empowers maintenance teams to detect hidden mechanical issues early, reduce downtime, and improve equipment reliability through AI-driven predictive maintenance.
The full case study includes detailed vibration trends, spectral insights, and expert analysis.
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