Predict Failures, Prevent Accidents, Maximize Uptime.
AurumAI
Predictive Safety and Maintenance for Mining
Overview
AurumAI is a comprehensive AI platform designed for mining safety and equipment health. It ingests diverse data – from highwall scanners, drones, sensors on machinery, and environmental monitors – to perform predictive risk analytics. Using computer vision and ML, AurumAI detects early warning signs of slope instability, unsafe blast parameters, or machine fatigue. It issues actionable alerts (e.g. "blast design exceeds safety thresholds" or "loader X shows vibration anomaly") and prescribes preventive actions. The user interface shows intuitive risk heatmaps of the mine, equipment health dashboards, and guidance in plain language. By embedding generative AI capabilities, AurumAI can also summarize complex incident reports and answer safety queries, helping frontline staff and managers understand and act on insights without technical expertise.
Challenges
- Mining environments are noisy and dynamic.
- Slope failures are influenced by geology, weather, and mining activity in complex ways.
- Human analysis of every factor is infeasible due to complexity and volume of data.
- Blasting safety is sensitive to geology and timing.
Equipment maintenance faces risks of mechanical failures if not monitored proactively.
- High-risk hazards in mining require continuous monitoring and pattern recognition beyond manual capabilities.
Solution
- Uses satellite/drone imagery, geotechnical sensors, and historical data to train ML models that detect cracks and deformations indicating rock movement.
- AI optimizes blast plans and monitors fragmentation in real time to prevent unsafe outcomes.
- Continuously analyzes vibration, temperature, and oil-level data to predict mechanical failures before they occur, enabling scheduled downtime fixes.
- Automates detection of complex hazard patterns on large datasets, augmenting human oversight.
- Delivers actionable insights to address mining’s high-risk hazards efficiently and safely.
Key Features
How it works?
Data Aggregation
AurumAI connects to mining data sources: geological models, sensor networks (e.g. piezometers in slopes, dust and gas monitors), drone and satellite imagery, equipment telematics, and maintenance records.
Preprocessing & Analytics
Raw data is cleaned and aligned. Computer vision algorithms analyze imagery for visual indicators (cracks, ore stockpile levels). Time-series analytics monitor equipment sensor streams. The platform uses specialized models for each hazard type: a neural network might predict slope stability trends, while a Bayesian model forecasts engine wear.
Risk Scoring Engine
Each potential hazard is given a predictive risk score. For example, a slope risk score might combine moisture levels, recent blast vibrations, and small detected movements. AurumAI continuously updates these scores as new data arrives.
Alerts and Recommendations
When risks cross thresholds, the system generates highpriority alerts. E.g. if a slope shows accelerating displacement, or if a haul truck’s oil temperature is abnormally high, AurumAI alerts engineers with recommended actions (e.g. “restrict haul truck usage” or “inspect slope immediately”). The system also suggests optimal maintenance times and safe blasting windows.
User Interface & Integration
Supervisors access a web/mobile dashboard with drilldown maps and charts. The platform integrates with mining ERP/maintenance software to schedule work orders or send SMS alerts to field staff. Over time, as teams act on its advice, AurumAI learns which interventions prevent incidents and continually refines its models.
Which failures are prevented?
In a large open-pit gold mine, AurumAI monitors a deep highwall. Drone LIDAR scans detect a new microcrack. The AI model assesses that recent heavy rainfall and blasting have destabilized the wall, predicting a potential slide. Engineers are alerted with a red-level warning. Crews evacuate the area and reinforce the slope before any collapse occurs. Simultaneously, AurumAI flags an ore crusher motor showing rising vibration patterns; maintenance is scheduled during the next planned shift change. Production is briefly halted on a safer schedule instead of experiencing an unplanned shutdown. Management receives an AI-generated report summarizing the incidents and preventive actions taken, reinforcing the system value.
How are maintenance costs reduced?
By shifting from reactive to predictive safety, AurumAI significantly reduces incidents and costs. Predictive safety systems have cut serious mining incidents by up to 40% through early warnings. Predictive maintenance similarly reduces downtime; AI-driven maintenance can halve unexpected breakdown time and cut maintenance costs by 15–25%. Avoiding a single haul truck engine failure can save hundreds of thousands of dollars in repairs and lost production. Over a year, a gold mine using AI analytics might see double-digit decreases in maintenance labor and inventory costs, as machines are serviced only when needed. Importantly, the human impact – more stable haul roads, fewer surprise blasts, healthier equipment – creates a safer workplace. AurumAI data-driven approach also supports regulatory compliance and community trust by demonstrating an active safety culture. Overall, the platform delivers measurable ROI through less downtime, lower operating costs, and improved safety metrics.
Which studies support AurumAI?
Practical AI and sensor systems for pipelines and mining are advancing rapidly. For example, APA uses SCADA and remote monitoring to optimize asset performance. Analytics platforms and case studies demonstrate that AI-based maintenance can cut downtime by ~50% and reduce equipment maintenance costs by 15–30%. AI-powered hazard detection (for leaks or slope failures) catches issues minutes before failure, preventing major incidents. These outcomes align with APA’s safety and efficiency goals and the mining industry needs for AI-driven risk prevention.
Which failures cause downtime?
Modern gold mining operations face hazardous conditions where equipment failures or geological events can endanger lives and productivity. Risks include slope or haul road failures, unsafe blast outcomes, and unplanned machinery breakdowns. In remote, heavy-industry settings, preventing accidents and downtime is paramount, yet traditional safety measures (manual inspections, fixed maintenance schedules) leave gaps. Mining companies need to anticipate hazards and equipment issues before they occur to uphold zero-harm safety and keep operations running smoothly.
