Manufacturing: Predict Machine Failures Before They Happen

A manufacturing company with 25 production lines and 180 employees struggles with unplanned downtime: on average, 12 unplanned failures per year, with an average downtime of 4.2 hours and costs of €8,500 per hour. That translates to direct downtime costs exceeding €400,000 annually, before accounting for knock-on effects from delayed deliveries and scrap.

The existing maintenance strategy was time-based: maintenance by the calendar, regardless of actual machine condition. This led to premature maintenance (unnecessary cost) and overdue maintenance (failures shortly after the maintenance interval).

Sensor Data Is Confidential Business Information

Machine data — temperature trends, vibration patterns, pressure readings, run times — reveals production capacity, shift patterns, and utilisation. This data must not be transferred to external cloud services.

Local Predictive Maintenance with SoverIQ

SoverIQ Stack runs on the plant server, connected directly to the sensor gateway. The model was trained on 3 years of historical sensor data and corresponding maintenance records:

• Real-time anomaly detection: deviations in vibration, temperature, noise, or current draw from normal patterns
• Failure probability: rolling forecast per machine — probability of failure in the next 72 hours
• Root cause hypotheses: which component is likely affected? Bearing, gearbox, pump, drive?
• Maintenance recommendation: optimal maintenance timing relative to production plan and parts availability
• Maintenance log: document results and use for future model improvement

Results

Unplanned failures fell from 12 to 3 per year — a 75% reduction. Direct downtime cost savings: over €300,000 annually. Maintenance costs simultaneously fell by 18%, because maintenance now happens on condition rather than by calendar. Payback on the SoverIQ project: 4 months.