Explore our solutions marketplace

Discover innovation with our solution finder

How does it work?

Learn more about how Axora can help you


Explore our knowledge

Read our expert insights, reports, watch webinars


Explore our community

Share knowledge in our expert community

Join us

Connect, collaborate and chat

About us

Learn more about Axora

Find out more about Axora and our team

Metals & Mining

Reducing unplanned mining plant downtime through AI-based predictive maintenance

Key facts

<3 mths

Return on Investment

3-6 mths



higher asset utilisation

Next Steps
Book a call
Email sales

Top business benefits

<3 months typical ROI

3-6 months typical setup

Increased asset utilisation and uptime

Increased mine throughput

Improved asset health and maintenance

Reduces the number of unplanned downtime events

Works with car dumpers, stackers, reclaimers, mills and crushers and more

Mobile or fixed plant capabilities

In more detail

Full description

Unplanned downtime is a major issue. Failures in equipment such as car dumpers, stackers, reclaimers, mills and crushers have a disruptive impact and of course a costly consequence in reduced throughput.

This advanced AI-based predictive solution enables preventative maintenance, thus reducing plant failures, lost production, spare parts use, labour costs, whilst increasing throughput.

The challenge

Mining throughput deeply depends on plant machinery functioning at peak uptime, and whilst planned maintenance is an essential process to overall mine performance, unplanned downtime is significantly disruptive and very costly. Equipment such as car dumpers, stackers, reclaimers, mills and crushers all need to be kept running as reliably as possible to maximise tonnage throughput.

Many issues can be predicted with the right technology and datasets, combined with advanced AI models, in the fast developing area of predictive maintenance.

The approach

This advanced AI platform, which has been used in a large variety use cases across many industries, has been proven to deliver highly reliable predictive maintenance recommendations for large and small mining operations with several successful case studies available.

Small customisations are typically used to fit the customer dataset used to power the model, and the solutions uses proprietary artificial neural networks with advanced explainability features and AutoML for scaling.

Customers are required to have sufficient sensors and stored data (3-5 years) of the equipment and a downtime accounting system to record and store malfunctions. The team will then work with the customer on data integration and input into the system.

Read less

Questions & Answers

Is this solution for you?

Talk to one of our experts who can match the latest innovations to your business needs.