Reducing Machine Downtime wit Predictive Maintenance for Aertssen

The challenge of unplanned downtime

For Aertssen Group, operational continuity depends on the availability of their machinery. But unexpected breakdowns disrupt projects and drive up costs. Traditional maintenance—based on fixed intervals or gut feeling—often leads to unnecessary checks or, worse, missed problems.

To tackle this, Aertssen set out to test a new approach: predictive maintenance powered by AI.

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Predicting issues before they happen

In close collaboration with Mr. Watts, Aertssen launched a proof of concept to explore how AI could support more targeted maintenance. The goal: use existing data to detect early signs of wear and tear, allowing teams to intervene before failures occur.

• The AI model analyses historical and real-time machine data.
• It identifies risk patterns and flags assets that require attention.
• Maintenance efforts become more efficient, data-driven, and timely.

This project proved that predictive maintenance can significantly reduce downtime—by focusing on what matters, when it matters.

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Laying the foundation for smarter operations

With this POC, Aertssen took an important step toward a more proactive maintenance strategy. Instead of relying on standard checklists, teams now get actionable insights that help prioritise work and prevent costly surprises.

The results form a solid foundation, we are now scaling across the organisation.

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