Anticipate failures 7–30 days ahead and eliminate unplanned downtime
An unplanned failure on a production line costs an average of €260,000 per hour in production losses, emergency repairs and client penalties. Scheduled preventive maintenance wastes resources on equipment that doesn't need it. AI predictive maintenance continuously analyzes vibration, temperature and electrical consumption signatures of your equipment to detect failure precursors before they occur.
Corrective maintenance costs 3–5× more than preventive maintenance. Preventive maintenance replaces still-healthy parts. Without prediction, unplanned downtime disrupts production, client deadlines and team safety. And technicians spend time on repetitive manual inspections.
Vibration, temperature and current sensors are connected to critical equipment. Data is continuously collected via the IoT pipeline and analyzed by ML models (isolation forest, LSTM, XGBoost) trained to recognize precursor signatures of failures specific to your machines. An alert is generated 7–30 days before the predicted failure, with the probable defect type and recommended urgency.
Criticality analysis of each piece of equipment (production impact, replacement cost, historical MTBF). Sensor deployment prioritization.
Vibration, thermal and electrical sensor installation. Real-time IoT pipeline connection via MQTT or proprietary protocol.
Using historical failure data to train anomaly detection and RUL (Remaining Useful Life) prediction models.
Predictive alert generation with defect type, urgency and recommended intervention. Integration with your CMMS (SAP PM, Maximo, Fiix).
Models detect precursor signatures well before failure. Your maintenance team plans ahead.
With early alerts, emergency shutdowns become planned stops. Production is no longer disrupted.
Less replacement of healthy parts, fewer costly emergencies, optimized spare parts inventory.
Free 30-minute audit. We analyze your context and deliver a concrete roadmap.