Predictive maintenance:

Fixing problems before they happen

Predictive maintenance

The maintenance of a large, complex factory floor can be a daunting, costly and time-consuming task.

Nevertheless, it is a task that is vital for the factory to operate efficiently and with minimal disruptions

to production.

The average impact of unplanned machine downtime in the process industries alone is roughly $20 billion

per year, which is around five per cent of annual production. This is attributed to lost sales, machine start-up

costs, parts replacement, idle labour and other factors. Not only that, but unplanned downtime can also lead

to lasting equipment damage, environmental harm and the endangerment of workers.

It is estimated that unplanned machine downtime costs up to 10 times more than planned downtime for

maintenance purposes. By gathering machine data over time and combining this with historical data and

a vigilant condition monitoring policy, facility managers can accurately foresee, and plan for, any upcoming

downtime. It is a realistic ambition for companies employing an efficient predictive maintenance programme

to have nearly zero unplanned downtime.

Applying AR technology

Infrared thermography is an AR-based technology that allows workers to view electrical systems, mechanical

equipment, building applications and fluid systems using thermal vision. Engineers can use it to spot faulty

connections, abnormal motors, pipe temperatures and tank levels without having to physically touch the equipment,

lowering the risk involved.

GE Aviation has successfully implemented AR into its maintenance strategy using its Skylight industrial AR

software platform. This allows mechanics to receive step-by-step instructions and images directly within their line

of sight through a pair of smart glasses, leading to an 8–11 per cent reduction in the time taken to carry out maintenance tasks.

Using AR in this way also allows manufacturers to employ a streamlined predictive maintenance programme. By

providing visualisations of potential points of failure within the system, workers can see at a glance if there is a problem

and, crucially, identify which parts are at fault. Replacement parts can then be ordered from an industrial parts supplier

without the need for costly unplanned downtime.

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