MAVEN APM — Asset Performance Management

Equipment Health Scoring — Every asset receives a continuously updated health score based on multiple condition indicators including vibration, temperature, pressure, current draw, and performance deviation. Colour-coded dashboards provide instant plant-wide health visibility.

Predictive Maintenance — Machine learning algorithms analyse condition monitoring data to predict remaining useful life and optimal maintenance timing. Shift from time-based to condition-based maintenance, reducing costs while improving reliability.

Vibration Analysis — Advanced vibration monitoring for rotating equipment including turbine gearboxes, electrolyser auxiliaries, compressors, and pumps. Frequency-domain analysis detects bearing wear, imbalance, misalignment, and looseness.

Time-to-Failure Prediction — Statistical models estimate remaining useful life for critical components, enabling maintenance teams to plan interventions during scheduled outages rather than responding to unexpected failures.

Intelligent Work Orders — Automated work order generation when predicted maintenance windows approach. Integrates with CMMS/EAM systems for seamless maintenance workflow management.