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Category: Mining and Resources

  • Loader Truck Breakdown Prediction

    Loader Truck Breakdown Prediction

    Loader Truck Breakdown Prediction

    Overview

    Byrnecut was interested in the use of machine learning for predicting Loader truck breakdowns 3 to 7 days in advance. We have offered to demonstrate what is possible on their telemetry and maintenance related data using our Predictive Maintenance product.

    Problem

    Unplanned Loader truck breakdowns are very costly even more so in terms of indirect costs or production reduction due to the delay caused in the mining process. Reducing unplanned breakdown by predicting emerging breakdown type problems will reduce overall costs significantly. Telemetry and maintenance data from 4 loader trucks was provided to prove this.

    Solution

    • Predictive model: Trained on historical data from loader trucks to predict emission and transmission related breakdowns in unseen trucks.
    • Actionable timing: Designed to trigger alert at least up to 3 days ahead of a predicted breakadown.

    Result

    • Early warning: Predicting a emission and transmission related breakdowns up to 7 days in advance, with average of 92% breakdown detection rate and 0.9% premature alerts per month on loader trucks unseen by the model
    • Operational gains: Significant reduction in production downtime. Better maintenance planning with risk based priortisation.

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  • Automated First-Break Picking for Seismic Surveys

    Automated First-Break Picking for Seismic Surveys

    Automated First-Break Picking for Seismic Surveys

    Automated First-Break Picking for Seismic Surveys

    Overview

    We worked with HiSeis to automate first-break picking using deep learning that detects refracted wavelets across millions of geophone traces, achieving accuracy within 4 milliseconds of an expert.

    Problem

    Manual first-break picking is slow and error-prone in noisy data. Existing tools and published techniques at the time were not accurate enough for HiSeis to reliably identify wavelets refracting from different rock formations.

    Solution

    • Deep-learning approach: Applied computer-vision techniques to learn patterns in audio-vibration and geophone sensor data to predict target wavelets among large volumes of traces.
    • Expert-grade accuracy: September AI’s method performed within a 4 millisecond error of an expert technician and, in many cases, outperformed the human.

    Result

    • Significantly improved accuracy and consistency than manual picking.
    • Efficiency gains that reduce time and cost associated with manual first-break picking.

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  • SAG Mill Throughput Optimisation

    SAG Mill Throughput Optimisation

    SAG Mill Throughput Optimisation

    SAG Mill Throughput Optimisation

    Overview

    We helped BHP to improve Semi-autogenous grinding (SAG) mill throughput by deploying a machine learning model that ingests real-time, site-specific data across drilling, blasting, crushing, and SAG operations. Its explainable machine learning outputs show which factors drive each prediction, so operators can trust and act on recommended set-points.

    Problem

    Physics-based and commercial simulation tools were not accurate enough for real-time control. Just as importantly, they lacked explainability — operators couldn’t see which inputs influenced predictions, making it hard to adjust set-points with confidence.

    Solution

    • Integrated ML model: Uses live plant data from drilling, blasting, crushing, and SAG mill operations to estimate throughput and recommend set-points (e.g., crusher gap settings, SAG mill speed, percentage of solids) for the current operating context and constraints.
    • Explainable outputs: The model’s inbuilt explanatory power highlights the features influencing each prediction, helping operators understand why recommendations are made and increasing adoption in live plant operations.

    Result

    • Prediction uplift: 42% better than the existing physics-based model and 27% better than commercial simulation software on average.
    • Throughput gain: Applying the optimal set-points increased throughput by 6% on average.
    • Operator confidence: Clear explanations supported decision-making in live operations.

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