A ‘virtual engineer’ which uses artificial intelligence to predict when machines need repairing could significantly improve the efficiency of maintenance scheduling.
The system, created by scientists at the University of Portsmouth, uses sensors on vulnerable parts of the machine, such as the bearings, to ‘learn’ how it operates while predictive software monitors analyses performance to calculate when a part is wearing out and alerts technicians.
The engineer is informed which part of the machine will soon be faulty, rather than waiting for it to breakdown and then spending time identifying which problem has occurred.
Dr David Brown, head of the university’s Institute of Industrial Research (IIR), said: “The machines in many processing plants and factories are running day and night and an unscheduled stoppage can cause havoc and can result in huge costs. This new diagnostic system prevents potential mechanical failure by identifying the faulty or worn out part before it causes a problem.
“It’s the first time this kind of technology has been used on this scale in the processing industry. The traditional approach to machine maintenance is being blown out of the water by real time diagnostics.”
One company testing the system is Stork Food & Dairy Systems (SFDS). Generally manager Luke Axel-Berg said:
“An unplanned stoppage on a production line can be a total disaster. It can spell chaos for a processing plant, especially a dairy plant where milk is literally arriving every single day. The cows don’t stop producing milk because a machine has broken. Instead the milk must be sent to an alternative location putting unexpected pressure on another plant.
“Our customers are already calling for a ‘zero-fault’ level on their machines. Until now it’s been impossible to guarantee that level of customer service but this new diagnostic system looks set to change all that by taking away the risk.”
SFDS is working in collaboration with the IIR on a Knowledge Transfer Partnership.
Photo: a typical bottling process in action
