Case Study Snippet: Sensor Fusion Identifies Mechanical Degradation in Sinter Fan Before Failure

Unexpected sinter fan failures in alloys production can cause major operational downtime and financial losses. To avoid these risks, a leading site implemented DataMind AI™ to monitor critical equipment in real time and flag early signs of mechanical deterioration.

In early March 2025, DataMind AI™ detected elevated axial vibrations in one of the site’s high-capacity sinter fans. Traditional inspections had missed contributing factors: an open suction cowling left unsealed after a previous incident, which disrupted airflow and gradually increased load on the impeller.

Additionally, a prior foreign object strike had likely caused undetected internal damage, further affecting system balance.
As vibration levels continued to rise, DataMind AI™ escalated the fan’s health status to Critical, prompting immediate inspection. The site’s team identified mechanical degradation in two key components: a misaligned coupling and an imbalanced impeller – confirmed by the need for substantial weight correction during rebalancing.

Thanks to early diagnostics powered by AI and multi-sensor fusion, the maintenance team intervened before failure occurred – avoiding equipment damage, preventing downtime, and protecting downstream assets.

This case demonstrates how DataMind AI™ enables predictive maintenance that drives faster, smarter decisions with minimal manual effort.