A Brisbane company is helping businesses, such as beverage giant Asahi, leverage data from legacy processing equipment, with wireless sensors and AI tools.

The world is increasingly awash with data, with no sign of it receding any time soon. One driver of the flood of data is the burgeoning ‘internet of things’ (IoT), which refers broadly to the range of machines and sensors that are connected to both each other and the internet. IoT devices enable us to gather data as never before, greatly expanding our ability to monitor and improve performance. However, the amount of data being thrown up can be somewhat overwhelming for the humans trying to process it, making it difficult to realise the supposed benefits. At this hurdle artificial intelligence (AI) steps into the picture.

The scope of AI is not strictly defined, but generally refers to capabilities of machines that would otherwise require human intelligence. At the low end of the complexity scale are tasks like recognising numbers or letters in an image, while at the high end is the ability to manoeuvre a car through busy traffic. AI offers us a practical means of efficiently sifting through the data streaming out of IoT devices.

Moving with the times

Movus, a Brisbane-based firm founded in 2015 is looking to help businesses capitalise on the existing and growing potential of AI.

“In the work I was doing in the corporate environment, it was clear there was a real demand from a range of businesses for IoT and AI products,” the company’s founder and CEO Brad Parsons says.

Given the growth of the company in just three years, it looks like he was right – the firm’s team of three staff at inception has expanded to around 30 today and looks set to continue to grow.

Movus straddles both IoT – through its wireless FitMachine sensors – and AI, which interprets the data generated by those same sensors (which are capable of monitoring equipment vibration, noise and temperature). As an added bonus they are easy to install, fixed onto the side of machines using magnets, which are carefully set up so as not to disturb the operation of the machine itself. The Movus machine learning algorithms can then parse the sensor data to monitor the health of a machine and predict when it might require maintenance or be about to fail.

This enables firms to pursue a ‘conditional’ maintenance strategy (servicing machines only when monitoring indicates it is required) rather than traditional ‘preventative’ (at a regular interval regardless of condition) or ‘reactive’ (after machine failure) strategies. Maintenance shutdowns can thus be better planned to avoid costly production slowdowns or avoided outright by tweaking machine operation to prevent damage in the first place.

FitMachine in action

Multinational beverage manufacturer Asahi recently deployed the company’s FitMachine sensors in one of its Queensland bottling plants. The objective was to improve asset efficiency, reduce production downtime (both planned and unplanned) and achieve maintenance efficiencies. Within two months FitMachine sensors detected abnormal vibrations within one of the fillers on the production line and flagged the issue for staff at the plant to investigate. The staff found a fault in the drive motor bearings for the filler, which if left unchecked would have led to a breakdown in the filler and a significant interruption in production at one of the busiest times of the year for the plant.

This is a clear example of how the Movus sensors and AI analytics engine can be used to augment the skills of engineers and operators by enabling them to concentrate on the operation of the plant rather than sift through copious amounts of sensory data.

Where to from here?

Some challenges remain for Movus and other firms looking to push the adoption of AI by businesses. One of the biggest issues is that the algorithms and models which underpin AI systems are viewed as something of a black box, and potential customers can be put off by systems they do not understand. But positive results for early adopters, as with Asahi, will go a long way to building confidence in the ability of AI systems to deliver improvements.

The Movus service business model also relies on its ability to continuously improve its offering to clients.

“Clients can vote for features they want to see in the product or on the analytics dashboard,” Parsons says.

“Our development cycle for an update is two weeks, in which time we can have the improvement rolled out to all of our clients.”

Further, as more data is collected over time and across a range of client machines, the capability of the Movus AI-driven analytics to predict and avert machine failure will continue to improve. To this end, the future looks bright for both Movus and AI in general.

To find out more about using AI to Interpret Data, read the article in the latest issue of PKN here.

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