Hyperight

ML Framework for Safety in Mining Industry – Soheila Ghane Ezabadi, AutoGrab

Session Outline

Latest benchmarking on safety performance by ICMM shows passive safety systems do not prevent accidents from happening. But significantly reduce accidents being fatal. ML-based solutions can address this gap by predicting accidents and demonstrating trends. And complex interrelation between leading indicators for actionable insights. This session at the Data Innovation Summit ANZ 2023 shares insights into a framework tailored to the mining industry requirements for developing Machine Learning solutions in eliminating accidents. This ultimately improves the safety performance in this industry.

Key Takeaways:

  • Advanced machine learning solutions enable mining companies to unfold non-linear correlation of leading indicators. From various categories including system, people, equipment, process, and culture.
  • In problem formulation, ensure prioritizing accidents, identifying key players and data sources. At this stage assume availability of all required data.
  • The biggest challenge is data collection! Start small by (1) adjusting the solution to the organization maturity level, (2) using leading indicators already known by users, however, providing a roadmap to the ultimate solution, and (3) carefully selecting model KPIs for this type of problems.
  • Bridge between model KPIs and business KPIs to be monitored and improved in cycles and demonstrate the learned trends among leading indicators using advanced libraries designed for interpreting Machine Learning solutions.

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