Predictive Maintenance & IoT Impact on Mining

Jul 20, 2021 | Insights

Many operations use preventative maintenance, the traditional method for keeping equipment maintained and running properly. Preventive maintenance is triggered by factors such as meter readings and manufacturer service recommendations. The downside to this method is that it’s easy to miss something if an equipment issue occurs outside the scheduled maintenance window.

IoT dramatically improves an operation’s ability to manage mining assets, prevent costly unplanned downtime, and improve health and safety using a predictive maintenance strategy.  You can learn more about IoT, IoE and how using predictive maintenance can extend machine life.

DINGO also believes that data analysis alone isn’t enough – human expertise also needs to be applied to troubleshoot and diagnose issues. Our team of Condition Intelligence experts has over 800 years of combined maintenance experience and manages the condition of over 150,000 vital components.

On average, DINGO customers see a 20% increase in components operating in normal condition, an increase in planned maintenance, improved availability, and a >10% gain in component life.

Below is an example of how Predictive Maintenance can impact a mining operation.

Canadian Copper Mine

A large Canadian copper mine partnered with DINGO to implement a comprehensive and holistic predictive maintenance program and hit best-in-class availability and life targets. The mine had been monitoring conditions with oil analysis, vibration and thermography for years, but its fleet health and life were not achieving the desired targets. As a result, the plan was to purchase two new trucks to supply the production capacity needed.

To address these challenges, the mine partnered with DINGO, which provided critical support in the following areas:

  • Organizing information, so the focus is on component health management and not just monitoring condition;
  • Providing recommended corrective actions to local team and tracks issues through resolution;
  • Using predictive analytics to identify emerging issues and plan condition-based work for existing maintenance windows;
  • Serving as a continuous source of expertise; and
  • Providing structure and focus through dashboard key performance indicators (KPIs), process control and benchmarking.

As a result, the mine didn’t need to buy the two new trucks – saving C$9 million (US$6.8 million) – and the increased availability and extended equipment life resulted in savings of over C$5.5 million (US$4.2 million) per year.

Implement Predictive Maintenance at Your Operation

It is becoming more difficult to maintain market share as the mining industry becomes more competitive. However, successful operations can improve component life, equipment availability, business-related process, and the bottom line. The Internet of Things (IoT) is developing as a more successful way to differentiate and gain a competitive advantage.

For over 25 years, DINGO has provided predictive maintenance solutions to asset-intensive industries. We combine deep maintenance expertise with industry-leading technology to help companies implement solutions that continuously improve equipment’s health and performance.

Contact DINGO for a brief Asset Health Consultation or use our Savings Calculator to estimate the annual savings that your operation could achieve.



“Trakka allows our team to run a highly effective condition-based maintenances program that helps our mine operate at the lowest cost per hour.”

– Maintenance Manager at a leading North American coal mine

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