Improving equipment maintenance with predictive analytics

09 May 2023

Andy Graham explains how predictive analytics can improve asset management or equipment maintenance strategies.

Predictive analytics makes use of artificial intelligence (AI) technology to continuously monitor and learn from the behaviour of an asset in real-time. It can provide alerts when the operation differs from the historical norm, giving early warning detection of equipment problems. 

Predictive analytics can offer a more effective way of dealing with operations employing powerful tools to identify issues that might otherwise be missed. An AI tool can learn about what is normal for a plant or an asset and can be programmed for certain thresholds and parameters. As soon as something starts to act abnormally or outside of those thresholds, an alert is sent to the relevant member of staff so that issues can be dealt with quickly and effectively, allowing production to continue unaffected and reducing unexpected production downtimes.

This kind of predictive maintenance does not just looking at the past 24-hours when monitoring machinery and lines. It can learn from up to the last five years to allow it to better understand what is going on. 

Early warnings can significantly reduce unplanned downtime and the accompanying loss of production. Plus, the real-time data can be employed to make other decisions that can help 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. 

Future predictions
Applying future predictions ensures that many production problems that would normally slip through the cracks and escalate are caught in good time. The ARC Advisory Group has found that, typical planned and preventive maintenance may only be identifying around 18% of ‘Failure Patterns’ problems. To catch the other 82%, predictive technology is needed to provide early warning. 

Predictive analytics software ties vital information with required actions for other critical plant systems, including integration with control, PLC,SCADA and safety solutions, with access to key data supported through the operator console.

It is not just production that sees the benefits of predictive analytics. Planned and preventive routine maintenance alone might frustrate customers who do not understand why their deliveries are not ready on time or product quality is variable. Customers want to know that targets will be met and predictive analytics can help give this assurance. 

For example, when a chemical company wanted to cover its entire value chain with multiple work streams, including predictive maintenance and augmented reality, SolutionsPT was able to suggest solutions for both and consequently, AVEVA was chosen to be the company’s predictive asset analytics partner. With the right software, they could take advantage of online predictive monitoring for critical equipment assets, allowing it to reduce unscheduled downtime and improve asset reliability, availability, and performance. 

Predictive analytics effectively equips engineers 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. 

Employing AI for early equipment failure detection will increase asset availability, reduce costs, and avoid unnecessary maintenance and downtime. Today, it can be an important element of a digital transformation – driving a wider shift from reactive to proactive and predictive analytical models for everything from maintenance to operations.

Andy Graham is Solutions Manager at SolutionsPT. 

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