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What’s the difference between condition monitoring and predictive maintenance?

National IoT expert Freddie Coertze from ifm Australia disperses misconceptions about condition monitoring and predictive maintenance.

National IoT expert Freddie Coertze from ifm Australia disperses misconceptions about condition monitoring and predictive maintenance and explains the role of an Internet of Things (IoT) platform in the digitalisation of an industrial plant.

The word ‘predict’ is the clue to the core difference between condition monitoring and predictive maintenance says Freddie Coertze.

“With condition monitoring, you’re using one parameter – such as vibration – to monitor the condition of your asset and it will provide you with a notification if something is going wrong, which can help prevent a catastrophic failure, but also means your asset is already damaged,” he clarifies.

“On the flipside, predictive maintenance gives you a holistic view of the asset. It takes several parameters into consideration to determine the asset’s health and condition, such as vibration, temperature, flow, pressure and current. This means you have a better view of any anomalies, and can predict negative behaviour in the asset before it starts to degrade, allowing for intervention before costly damage.”

So what are the tools required for implementing predictive maintenance? According to Coertze, the essential ingredients are sensors, an IoT platform, and data analytics.

“The IoT platform is central to the process, because it provides visibility and integration across an entire operation. Traditionally, data would flow from sensors to the control system, which is isolated from other divisions in the business,” he explains.

“An IoT platform sits as a middleware layer between industrial equipment and enterprise systems. Importantly, it collects data from sensors on plant machinery, applies analytics to translate that data into useable insights, then shares those insights with key decision makers in the business.”

To simplify the process for businesses – particularly as they undergo digital transformation – Coertze says ifm developed the moneo platform. This is a self-service, plug and play IoT solution that includes a Data Science tool.

“It’s designed for ease of use. You can access valuable insights from your assets without the need for data science expertise. Everything is in-built, and importantly, it can grow as your requirements change,” Coertze concludes.

“We recommend people get in touch with us for a chat and a free 30 day trial. This way, they can experience what moneo can do first-hand.”

To learn more about the moneo platform please click here.

Or to access a free trial, you can register here.

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