Craning to see the benefits of predictive maintenance? This solution is taking industry to new heights

When explaining the value that ifm’s moneo solution can provide to an industrial plant, Manish Mongia uses the example of a crane. Why? Because cranes are expensive assets that can be difficult to monitor for maintenance.

“It’s really difficult to perform condition monitoring on cranes because their loads are changing all the time,” says the Systems Solutions Engineer from ifm Australia. “Whereas our moneo solution has been designed to take into account different variables. With a crane, for example, we would be able to measure both vibration and the motor current, giving us a more accurate picture of the crane’s condition.”

According to Manish, conventional condition-based monitoring with cranes tends to focus on vibration monitoring, but because cranes have dynamic loads, measuring this value alone can lead to false alarms about the crane’s health.

“In a crane, you would need to take these dynamic loads into account so that you’re not getting any false positives,” he says. “If the motor current is high and the vibration is high, that is acceptable. However, if the motor current is low and the vibration is high, that’s an anomaly and potential issue.”

This is where the in-built smarts of moneo – ifm’s Industrial Internet of Things (IIoT) platform – comes into play.

“The DataScience toolbox in moneo is designed for complex machines and dynamic processes which are changing all the time,” says Manish. “It takes into account parameters like temperature, pressure, vibration, motor current, flow, and it builds a mathematical model for you where you have dynamic limits rather than static limits. That means if your process changes, then your limits change accordingly.”

For the modern plant or factory, the type of analytics the DataScience toolbox is able to provide can be invaluable.

“This tool is completely self-service. You don’t need any data science expertise to set it up,” says Manish. “It gives you the ability to protect your assets by predicting issues well in advance. By having that visibility, you can intervene before an asset is already starting to degrade.”

A key point of difference with the moneo platform is that while it is highly sophisticated in its capabilities, it is simple to use.

“A reliability engineer can start getting valuable insights from this straight away. It’s easy to deploy, self-service, and you can start with one specific machine and then ‘grow as you know’ based on the benefits,” Manish concludes. “In essence, it empowers businesses to embrace the benefits of predictive maintenance without the complexities or costs normally associated with these advanced digital technologies.”

Want to learn more about moneo and the DataScience toolbox? Click here.

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