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Basic Guide to Predictive Maintenance

Jul 27, 2021 | Insights

How do you reduce the chance of unplanned equipment failure, reduce operating cost, extend component life, and increase availability, all while conducting the minimal amount of maintenance? On average, DINGO customers see a 20% increase in components operating in normal conditions, improved availability and a >15% gain in component life. At the core of these results is predictive maintenance.

If you are looking for information on predictive maintenance, what it is, how it works and if it is right for your organization, this resource should be helpful.

What is Predictive Maintenance?

Predictive maintenance is an effective maintenance strategy that forecasts machine failure and helps identify underlying problems that need to be fixed.

In a previous post, we’ve used an example of getting an oil change for your vehicle to explain preventative maintenance compared to predictive maintenance. The issue with only using scheduled maintenance is the condition of the asset is considered.

In the example of the car,  changing the oil on a set schedule may result in oil changes too frequently–or too late–as oil wear will vary depending on driving conditions.

However, many modern vehicles have sensors that constantly monitor the oil life and recommend based on the oil’s actual condition rather than a set number of miles driven. Similarly, predictive maintenance is based on the equipment’s real operating condition.

 TRAKKA and Predictive Maintenance

DINGO’s Asset health software, TRAKKA®, captures predictive health information from each of your machines and then uses a proprietary analytics platform to assess equipment condition.

If problems are found, the system seamlessly integrates with your existing workflow, alerts the team and recommends corrective actions.

This early warning system equips maintenance teams with the actionable knowledge required to promptly rectify impending issues resulting in increased availability and fewer unplanned failures.

DINGO’s solutions increase availability, extend component life, and reduce operating costs by continuously improving your equipment’s health and performance.

 Different Types of Maintenance Strategies

We have discussed some of the issues with preventive maintenance in our last article, “Turning Mining Data into Actionable Intelligence.” Preventative maintenance depends on fixed factors such as manufacturer service recommendations, but machines don’t wait for scheduled maintenance to fail.

Successful mining operations are proactive and anticipate failures before they happen; this is possible with predictive maintenance. But what are some of the other types of maintenance strategies?

 Preventive maintenance (PMs)

Preventative maintenance (PM) depends on fixed factors such as service recommendations to maintain mining equipment and assets. Preventive maintenance is completed at predetermined intervals and does not consider the equipment condition.

While preventative maintenance aims to prevent and reduce equipment downtime, machines don’t wait for routine maintenance intervals to fail.

Condition-based maintenance (CBM)

Unlike preventative maintenance that relies mostly on routine maintenance intervals, Condition-based maintenance continuously monitors equipment performance with a combination of sensors, scheduled tasks and visual inspections.

Condition-based maintenance can significantly reduce maintenance costs compared to preventative maintenance or a run-to-failure strategy as maintenance and repairs are based on equipment’s current condition.

Condition-based maintenance shares common traits with predictive maintenance using sensor data to measure and predict equipment failure, but Predictive maintenance takes the usage of the data a step further.

Predictive maintenance (PdM)

Predictive maintenance is a maintenance strategy that forecasts machine failure and helps identify underlying problems that need to be fixed.

Predictive maintenance takes the sensor data and uses algorithmic formulas to predict machine failure. Our data scientists developed DINGO’s predictive analytics models using real failure data and then validated by maintenance experts.

By continuously improving the health and performance of your equipment, DINGO’s solutions increase availability, extended component life and reduce operating costs.

Implementing Predictive Maintenance

It can be challenging to change the status quo.  Often, technically proficient teams feel as if they are doing’ well enough’ and there isn’t justification for changing. Additionally, it’s not uncommon for mines to have data silos; this can lead to a focus on functional priorities and objectives that don’t consider the “big picture.”

To overcome this barrier, it is helpful to benchmark KPIs vs. similar operations to get an accurate picture of performance and understand what’s possible.  Use this information to set clear universal goals that encourage continuous improvement, cross-functional ownership, and a sense of teamwork. It’s also essential to create an environment where each employee understands how they contribute to the program’s overall success. Track performance in a system that’s readily accessible by all and acknowledge and celebrate success as you go.

Predictive Maintenance in Mining Industry

For more information, contact a DINGO expert.


“Partnering with Dingo allows us to leverage remotely located condition intelligence experts from the get-go for a fraction of the normal cost to company. This allows our few reliability experts to focus on the top issues and make important decisions to ensure our fleet availability is where it needs to be instead of having them analyzing data and looking for the issues.

-Martin Pichette, Mine Operations and Maintenance Director at Lamaque Mine

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