Predictive Maintenance
Predictive Maintenance focuses on anticipating equipment degradation before failures occur, using a combination of high-frequency sensor data, physics-informed models and advanced machine-learning techniques. Our research develops hybrid predictive models that couple domain knowledge with deep time-series architectures to estimate Remaining Useful Life (RUL) and identify early-stage anomalies.
By deploying lightweight inference agents at the edge, we enable real-time health monitoring, automated alerting and data-driven maintenance planning—significantly reducing unplanned downtime and extending asset lifetime.
Hybrid Predictive Modelling
Fusion of physics-informed models with deep time-series architectures to forecast degradation trends and estimate Remaining Useful Life (RUL).
Early Anomaly Detection
High-resolution monitoring systems capable of identifying subtle deviations and fault precursors long before functional failure occurs.
Edge-based Health Inference
Lightweight on-equipment inference agents enabling real-time diagnostics, automated alerts and continuous health scoring in the field.
Data-driven Maintenance Planning
Optimised maintenance schedules informed by predictive insights, reducing unplanned downtime and extending the operational lifespan of critical assets.
