Data Science – Predictive Maintenance Scheduling in Mining
Posted on December 27, 2023 Big Data Machine Learning & AI Mining
Mining equipment maintenance is critical for sustained production. Each piece of equipment performs an important role and breakdowns can lead to costly repairs and production downtime.
Mines typically use a preventative maintenance strategy to keep their equipment in good working order. Preventative maintenance has been shown to
- provide improved safety,
- reduce maintenance cost, by up to a factor of 4 in some cases,
- increase equipment reliability and availability, and
- extend equipment lifespan.
As each mine is different, with different demands on the various equipment, the “best” maintenance schedule is different for each site. To help optimize existing maintenance practices, data-driven and statistical methods can be used to enable predictive maintenance. Predictive maintenance involves monitoring equipment sensor data and mine data and using data-driven models to help inform the maintenance schedule. Predictive maintenance can take many forms, including:
- Equipment sensor data (e.g., engine temp., fuel efficiency) can be monitored and analytic algorithms (often using machine learning) can be used to predict when maintenance should be performed on a specific piece of equipment. Compared to a routine maintenance schedule carried out at set time intervals, this can help reduce the unnecessary replacement of functional parts.
- Statistical methods can be used to determine when to order new parts before the need for them becomes critical—avoiding potentially long lead times.
- Multiple models can be integrated together to help determine how best to schedule equipment maintenance given the predicted maintenance needs, parts inventory, manpower availability, and even repair shop layout (which for underground mines can be a very important constraint).
Embracing data-driven predictive maintenance strategies is not just about preserving equipment; it’s about safeguarding production efficacy, reducing costs, and ensuring sustainability; paving the way for smarter and more efficient mining operations.