Multi-Sensor Anomaly Detection in Mining

Fusing data from geophones, LiDAR, and in-pit cameras to detect micro-seismic activity and ground deformation indicative of slope instability. The system provides early warnings days or weeks before a visible collapse, enabling proactive intervention.

• Real Example: Identified a developing highwall failure 72 hours in advance at a copper mine, allowing for safe evacuation and re-routing of haul trucks, preventing an estimated $15M+ in lost production and equipment damage.
• ROI Driver: Directly avoids multi-million dollar incidents, protects capital assets, and ensures regulatory compliance by demonstrating proactive risk management.

Continuously analyzes vibration, acoustic, and thermal data from shovels, draglines, and haul trucks to identify subtle anomalies signaling impending mechanical failure.

• Real Example: Detected abnormal bearing vibrations in a primary crusher motor, triggering maintenance two weeks before a catastrophic failure. This avoided a 10-day unplanned shutdown, saving over $2M in lost throughput and repair costs.
• ROI Driver: Transforms maintenance from reactive to predictive, reducing unplanned downtime by 25-40% and extending the operational life of multi-million dollar assets.

Integrates drone-based photogrammetry, subsurface piezometer data, and satellite InSAR to monitor dam structure and seepage in real-time. AI models detect deviations from baseline stability models that human analysts might miss.

• Real Example: System flagged anomalous moisture migration in a dam wall section, leading to the discovery of internal erosion. Remediation was completed before any structural compromise, averting a potential environmental disaster and massive liability.
• ROI Driver: Mitigates extreme environmental, financial, and reputational risk. Provides auditable, continuous compliance reporting for regulators and investors.

Monitors infrared thermal cameras for belt overheating, acoustic sensors for roller bearing failures, and vision AI for belt misalignment or material spillage. Creates a unified health score for the entire transport network.

• Real Example: Identified a seized idler roller through a combination of heat signature and abnormal sound profile. The roller was replaced during a planned shift change, preventing a belt fire and a 48-hour production halt.
• ROI Driver: Ensures continuous material flow, the lifeblood of mining revenue. Reduces conveyor-related stoppages by over 30%, directly protecting daily tonnage targets.

Correlates data from fixed gas sensors, airflow monitors, and worker wearable devices to model ventilation effectiveness and predict hazardous gas build-up in real-time, especially after blasting operations.

• Real Example: AI model predicted a dangerous CO accumulation in a return airway 30 minutes post-blast based on airflow patterns, triggering automatic fan adjustments and alerting personnel, ensuring zero exposure incidents.
• ROI Driver: Protects the most valuable asset: your workforce. Reduces safety incident rates, lowers insurance premiums, and ensures adherence to the strictest health and safety standards.

Unifies all anomaly detection streams into a single pane-of-glass command center. Provides prioritized alerts, root-cause analysis, and recommended actions, shifting teams from firefighting to strategic management.

• Real Example: A major gold operator consolidated data from 12 different legacy systems. The dashboard enabled the central control room to identify a causal link between crusher settings and downstream processing bottlenecks, optimizing overall plant efficiency by 5%.
• ROI Driver: Empowers data-driven decision-making at speed. Reduces mean-time-to-repair (MTTR), improves cross-departmental coordination, and unlocks latent capacity across the entire value chain.