Resource Center

Basic Guide to Predictive Maintenance

Basic Guide to Predictive Maintenance

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...

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See how the leading companies in mining, oil and gas, wind energy and rail sectors save millions annually with DINGO’s Asset Health solutions.

The Secret to Improving Mean Time Between Failures

The Secret to Improving Mean Time Between Failures

Mean Time Between Failure (MTBF) is a closely monitored key performance indicator for mining operations.  MTBF is the sum of lengths of the operational periods divided by the number of observed failures. In other words, it measures the mean time, in hours, from when a...

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Making The Machines Work For You

Making The Machines Work For You

DINGO’s Chief Operations Officer, Gary Fouche, thinks data is one of the most important drivers of the business. We asked him how data is made even more valuable when predictive analytics and machine learning come into play. What excites you most about predictive...

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Delving Into Thick Data

Delving Into Thick Data

Australia's Mining Monthly - Big data is being touted as a way to save mining companies billions, however, like most things, context is all important. While it brings a flood of information, finding the one piece that can unlock savings can be tough. DINGO managing...

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Australia’s Mining Monthly: Come Together

Australia’s Mining Monthly: Come Together

Australia's Mining Monthly - A COLLABORATIVELY- developed series of machine learning algorithms has been nominated for an Australia’s Mining Monthly award. The algorithms, developed by DINGO and the Queensland University of Technology were nominated for the Innovation...

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