How leading miners strengthened tailings safety through predictive monitoring
Multi-sensor data with AI detects behavioral anomalies early. Teams get clearer risk signals and time to intervene.
Context
Equipment failures often occurred without sufficient warning, exposing the operation to production losses. Traditional inspections provided limited lead time to act. The site required a system that could detect anomalies far earlier.
Solution
Predictive analytics were applied across multi-sensor data streams, including vibration, temperature, and pressure. Dashboards and alarm thresholds were configured to highlight emerging risks. Teams received clear indicators when asset behavior deviated from expected baselines.
Results
The system enabled earlier anomaly detection, allowing teams to respond before failure. This improved risk management and helped prevent unplanned downtime.
Data Inputs
Piezometers, inclinometers, weather, InSAR, imagery