A DIY toolbox for predictive maintenance

Assets are like fingerprints, says ifm digitalisation expert Freddie Coertze. Each one has a unique signature. This is why certain condition monitoring techniques might not be the solution for improving equipment health.

Assets are like fingerprints, says digitalisation expert Freddie Coertze. Each one has a unique signature. This is why certain condition monitoring techniques might not be the solution for improving equipment health, but predictive maintenance could be.

“I think there is a misconception about monitoring assets, that perhaps you can only monitor just one value such as vibration or temperature, and that will be effective in determining failures,” said Freddie Coertze, the IoT business development manager for ifm Australia. “To get a holistic picture of an entire asset, you should be monitoring different values and patterns.”

According to Coertze, a holistic approach to asset health is the fundamental difference between condition monitoring and predictive maintenance. Condition monitoring is reactive, while predictive maintenance is proactive.

“Condition monitoring focuses on identifying problems as they occur, while predictive maintenance uses data analysis from sensors, values and process information to detect anomalies quickly and ‘predict’ health issues,” he says. “Both approaches can provide benefits, but predictive maintenance is defined by the word ‘predict’. It has the potential to provide even greater benefits by giving advance warning to act before the equipment is already starting to fail, which in turn reduces downtime and maintenance costs.”

Circling back to the fingerprint, Coertze says that each asset behaves differently. This is why a solution needs to be able to interpret those unique characteristics or signatures. To make this simple for businesses – regardless of their size – the ifm Industrial Internet of Things (IIoT) platform has an in-built DataScience Toolbox with this capability.

“This is why moneo is so powerful. It makes getting the right information about your assets easy because it includes this DataScience Toolbox – you don’t need a data scientist involved to access machine health insights. It’s completely DIY,” he explains. “The DataScience Toolbox will establish what good behaviour is for each machine based on historical and current data. It will keep comparing the machine’s behaviour, and if there are any anomalies, it will bring that to immediate attention.”

The moneo DataScience Toolbox incorporates artificial intelligence (AI) algorithms that assess data recorded by sensors. It uses data with advanced machine learning technology to set parameters and make sense of detected anomalies and patterns. It does this with two specific tools – the moneo SmartLImitWatcher and moneo PatternMonitor.

“The SmartLimitWatcher will generate a model based on the status of a monitored process. If the data shows an anomaly outside the parameters in the model, it will set an early notification,” says Coertze. “Whilst the PatternMonitor is looking at structural changes in critical processes. Like its name, it will identify patterns in the process and report on any changes in volatility or levels. It alerts you to any undesirable process changes.”

In a nutshell, the moneo DataScience Toolbox empowers businesses to harness the benefits of predictive maintenance without the complexity or cost associated with such advanced digital technologies.

“That’s always been our aim with moneo. To make digitalisation quick and simple for businesses,” Coertze concludes. “Having that holistic view of each asset and receiving actionable insights that inform as to when to perform maintenance will allow you to get the most out of your machinery.”

For more information, visit ifm’s website here.

Related Articles:

Send this to a friend