Utilising Predictive Analytics to Improve Equipment Maintenance​

Let’s say equipment maintenance takes up an average of 30 minutes each day. That’s a valuable slot wasted by maintenance tasks that could be better predicted and planned. Utilise that time more effectively and you can optimise your plant and achieve significantly better results.

Here’s a closer look at how predictive analytics can improve equipment maintenance and what that might means for the plant.

The Impact of Predictive Analytics

Predictive analytics uses AI to continuously monitor and learn from asset behaviour in real-time. It provides alerts when the operation differs from the historical norm, giving early warning detection of equipment problems.

Predictive analytics is a more effective way of dealing with your operations. It involves using powerful tools to identify issues that might otherwise be missed.

Consider how AI monitors equipment compared to a typical human worker. This worker might be an incredibly valuable asset to your team, but they do still have a key flaw – they’re human.

There are some issues a human just wouldn’t notice. This is in part because the worker has a long list of responsibilities and they aren’t typically given the time they need to look at something closely enough to spot a problem.

An AI tool, on the other hand, can learn about what is normal for your plant and assets and can be programmed for certain thresholds and parameters. As soon as something starts to act abnormally or outside of those boundaries, an alert is sent to the relevant member of staff. Issues are dealt with quickly and effectively, allowing production to continue unaffected.

This kind of predictive maintenance isn’t just looking at the past 24 hours when keeping an eye on machinery and lines. It can learn from up to the last five years to better understand what’s going on. This advanced analytical approach empowers your staff, letting them spot red flags that indicate something needs their attention.

Early warnings significantly reduce unplanned downtime and the accompanying loss of production that’s so frustrating for everyone involved. Plus, real-time data of this kind allows you to make other important decisions that will optimise production. For example, machinery can be moved to where it can add the absolute most value, or production might be moved to a different line to reduce risk or increase throughput or quality.

Applying predictive analytics ensures you catch a lot of the problems that would normally slip through the cracks and escalate. The ARC Advisory Group found that with typical planned and preventive maintenance, you might only be identifying around 18% of “Failure Patterns”problems. To catch the other 82%, you need predictive technology providing early warning, giving you peace of mind and making sure production targets are met.

It isn’t just production that sees the benefits of predictive analytics. Customer confidence is vital, although it can be harder to track than other metrics. Planned and preventive maintenance alone might frustrate customers who don’t understand why their deliveries aren’t ready on time or product quality is variable. Customers want to know that targets will be met and issues are dealt with. Predictive analytics can help with this and lead to happy customers.

Predictive analytics helps to equip staff with the right tools to do their job; by giving them early warning information about assets that need maintenance; helping to organise spare parts and consumables in good time; assisting in resource planning; providing better information “in-context” empowering them to make better decisions.

Predictive analytics uses historical and real-time data from all plant production assets, providing the right information to the right people at the right time, driving appropriate actions. This improves overall asset performance and effectively manages corresponding engineering and maintenance activities.

Using AI for early failure detection increases asset availability, reduces costs and avoids unnecessary maintenance and downtime. It can be part of a customer’s digital transformation, driving a wider shift from reactive to proactive and predictive models for everything from maintenance to operations.

Predictive analytics software ties vital information with required actions for other critical plant systems. This includes integration with all Control/PLC/SCADA and Safety solutions, where access to key data is supported through the operator console.