Mastering the Art of Unsupervised Learning: Techniques and Guidance
Unsupervised learning, a cornerstone of artificial intelligence, has emerged as a powerful technique that enables machines to uncover patterns and insights from unlabelled data.
Monitor Real-time IoT and Machine Vision-based alerts (including personal protective equipment, hazards, and mobile equipment risk) to ensure safety compliance. Use Digital Twins for real-time visualisation and scenario modeling.
Predict OH&S incidents by creating forward-looking machine learning-based ‘Dynamic Employee Risk Profiles’ to calculate employee’s chances of being involved in an incident on any upcoming day.
Prevent incidents by retraining, proactively removing employees from potentially dangerous situations, and sending live compliance alerts to employees to immediately eliminate dangerous working conditions.
Real Time + Sensor Based IoT Monitoring
Monitor proper use of safety equipment
Understand complex video events in real-time
Machine Vision-Based Monitoring
We can expedite the delivery of your outcomes using IP and accelerators.
Optimise Throughput and Smart Preventative Maintenance
AI, ML, Machine Vision, and Data Science-based mining circuit optimisation, using real-world physics simulators, deep learning (ML), and gaming engines to understand and monitor mining circuit inputs, settings, and materials to optimise throughput. Sensors can also be deployed to monitor asset health and alert operators for conditions that typically preceded downtime events in the past. Similar projects generate ROIs greater than 900% or $90m annually in some cases.
IOT-based Location Awareness and Analytics to understand team locations, asset utilisation, and interactions. This results in the optimisation of plant, equipment, and people (including cost reductions, time-saving by minimising proximity for next likely use, scheduled cleaning, and maintenance).
Predict catastrophic equipment failure that could lead to injury or fatality by understanding causes of failure, the likelihood of failure, and predict failure events. Identify defective equipment using machine vision and IoT and determine safe asset use via statistical analysis and optimise asset safety. Use Critical Control Monitoring to identify asset and process failures in advance.
Risk Culture, Safety Culture, Engagement & Psychological Wellbeing Surveys
Various surveys across mining to identify areas of risk or concern based on questions around Risk Culture, Safety Culture/Compliance, Engagement and Phycological Health. Results facilitate drilling to problem departments, locations and sited to focus training and remediation activates and maximise training/remediation outcomes.
Automated Site Assessments
Machine Vision to generate and deliver automated building and site progress data and safety insights. The project involves using robots, drones, and other autonomous equipment to monitor sites to minimise human movement/risk and maximise reporting accuracy.
We’ve collectively delivered exceptional value/ROI in Mining and Resources
Dr. Joe Walsh
Physics, PHD, Dip. Management, Dip. QA; Over 50 International Presentations, Over 100 Peer Reviewed Research Papers;
Head of Data Science and Data Engineering
Eng (Hons) Major in Mining Engineering; Various mining roles including Drill & Blast Engineer (Downer EDI), Engineering Consultant (MEC Mining)
Project Lead and Technical Consultant
Comm, MBA, GAICD; Various mining projects (South 32, Newcrest, FMG, MMG, St Barbara, Evolution Mining, RIO, PYBAR, Anglo Gold, Adaro)
Management Consultant & Data/AI Strategist
Collective Mining Project Experience