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The ifm moneo platform is an AI-assisted, self-service predictive maintenance tool.

Australian Mining learns how ifm Australia is using artificial intelligence to help miners reduce air compressor leaks.

When an air compressor begins to leak, the downstream effects can lead to a surprisingly expensive shock.

Leaks trigger a drop in system pressure, preventing air tools from working and negatively affecting production. On top of that, leaks cause an air compressor to cycle more frequently, adding to maintenance costs and potential unplanned downtime.

Freddie Coertze, national IoT (Internet of Things) business manager at ifm Australia, said the modern plant needs predictive maintenance tools to get the insights required to protect assets and prevent waste.

Why does the modern plant need data science tools to prevent compressed air waste? Because a compressor doesnt run on load all the time, Coertze told Australian Mining. It runs on variable load depending how much the factory or processing facility needs.

To fully understand how the asset isperforming, data needs to be collectedfrom the equipment and analysed but this is where there is a difference between solutions available.

The ifm moneo platform uses artificial intelligence (AI) to provide real-time insights into an air compressor that usually requires data science experts.

This is an AI-assisted, self-service predictive maintenance tool, Coertze said. It makes it very easy to harvest the data from a complex system, putting the power back into the hands of the business so they can achieve better productivity at their plant.

Coertze described a particular case in which moneo was used to monitor and improve an air compressor through an industrial personal computer (IPC) that the company provides. This IPC comments to an IO link master, which collects data from the sensor devise.

This allows the mine to monitor flow meters, humidity, temperature, pressure and vibration sensors, as well as a current transmitter to see how hard the compressor is working.

The moneo software draws on historical data to create set parameters within which the compressor should be working, along with the live streamed data. It then provides an analysis through the use of AI algorithms and machine-learning.

In the case where we monitored an air compressor at a site, the moneo platform determined that the compressor was running at a loss and consuming more energy than it should, which was especially evident when the plant was shut for the weekend, Coertze said. Because the solution gives a holistic picture of the whole asset, we were also able to predict a future failure. This was easily remedied without any major consequences.

Coertze adds that the moneo data science tool will provide greaterpredictability of all assets in a plant. The platform is also system-agnostic and can be integrated with existing infrastructure.

To protect, you need to predict, but the difference is that now we canharness the benefits of AI to make this a simpler process for any manufacturing or processing facility, Coertze said.

With moneo, we provide a pre-packaged self-service kit that you can expand on, depending on your changingrequirements.

Significantly, this platform is a middleware that can sit between your sensor level and a higher end system such as Scada.

And with the in-built AI and automated machine-learning, you dont need to involve a data scientist to get real-time, actionable insights.

This feature also appears in the April edition of Australian Mining.

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Reducing air compressor leaks with ifm - Australian Mining