AUTOMATION & PROCESS CONTROL

AI solution for CSG

NEW artificial intelligence 'Fitbit'-like tech will be deployed on Queensland's CSG wellheads this week developed by local start-up Movus, <i>Energy News</i> has learned.

AI solution for CSG

One of the LNG operators will also expand it to its transformers within a month if all goes well.
 
Representatives from angel investors, venture capital, private equity and corporate venture funds awarded Movus, started by technology strategy consultant Brad Parsons in January 2015, the Investor Award at KPMG Australia's Energise accelerator program last Friday.
 
Having developed an automation monitoring blueprint for BHP Billiton and Aurizon trains and condition monitoring blueprint for Sydney Trains while working for Perth-based Ajilon Australia, Parsons realised the technology was totally applicable for the billions of assets globally that sit below those big heavy assets.
 
Parsons has now developed a condition monitoring sensor that works in the industrial environment picking up the health of machinery using artificial intelligence and a machine learning engine on the backend.
 
It is magnetically attached and can be installed in minutes, the same as a Fitbit for humans.
 
Parsons has already talked about it with Energise sponsors Woodside Petroleum and Chevron Corporation, and will meet Wesfarmers and Western Australia's Water Corporation representatives in Perth in the next fortnight.
 
Having developed the tech for the water sector over the past 12 months, he aimed to win the Energise award to open up new horizons to the energy and natural resources sector.
 
Woodside has already started using predictive analytics for maintenance and process control in production operations across its LNG portfolio starting with Pluto, so Parsons will meet the oiler's representatives in the coming month to discuss how his tech adds value to what the operator already does.
 
Parsons has high hopes, though, as his tech is for legacy systems that don't have the instrumentation on board from the time of sale, or are not at the top end of the machine spectrum.
 
"Usually the top end of the machine spectrum have a lot of different sensors on bearing failure, over-temperature etc, but we calculated through the National Energy Association that there are 2.6 billion electric motors on the planet, and we know that over 90% of those are uninstrumented," Parsons said.
 
"That means there are thousands of machines out in the field that might not be the high-value stuff but it will impact the downtime of your plant."
 
"One of the key questions that comes up is about ‘intrinsically safe', where any instrumentation or sensors put into the oil and gas environment must have a level of safety so it won't spark and cause a fire in explosively hazardous areas.
 
Parsons is going through that process of accreditation is hopeful Movus' tech will pass with flying colours.
 
When it does, Movus expects to be the world's first condition monitoring sensor for explosive areas.
 
"That's why we haven't pushed into Woodside and Chevron yet, because that accreditation is absolutely crucial. Safety is everything," Parsons said.
 
He will discuss his technology further with Woodside in the next fortnight about whether it adds value to the oiler's existing predictive analytics technology where they not only have sensors on infrastructure such as the Pluto plant but know about it ahead of time.
 
Movus' sensors made their way last Friday to Cisco's innovation centre in Perth, to which Woodside is a big contributor, and Parsons will meet the oiler's executives to discuss how the test went. 
 
Movus' tech doesn't just detect the health of the machine but its run time, analysing how much power and energy it's actually using.
 
"There's a big gap in the knowledge of operators," Parsons said. "They buy the machine then install it; there's a guy that pays the power bill at the end of the day, and there's a guy who does the maintenance and they're completely separate.
 
"What we're doing combines them into a single interface."
 
NB: This article has been amended from its original version.
 

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