Increase OEE with AI and predictive maintenance

The importance of Overall Equipment Effectiveness (OEE)

Increasing (international) competition, rising cost pressures, a massive shortage of skilled labour, Industry 4.0 are just some of the challenges that manufacturing companies face nowadays. In addition, there are everyday challenges such as the maintenance and servicing of machines and systems.

In order to remain successful on the market, one thing matters more than anything else: maximising the productivity of your machines and systems. The most important key measure for evaluating it – and increasing it – is overall equipment effectiveness (OEE). The OEE value indicates how well your machines and systems are utilised compared to their full potential during the planned production times. Losses in terms of availability, performance and quality can thus be better identified and quantified.

But how do you increase the OEE in your company?

Unplanned downtime is a crucial influence. This is obvious because lower plant and system availability equals lower OEE. But how do you avoid downtime? By addressing the cause. In addition to human error and a lack of materials, downtime is primarily caused by unplanned component and system failures. Failures of small components, such as pneumatic cylinders in particular, can have a major impact.

And it is exactly these kinds of failures in production that you can easily avoid and improve overall equipment effectiveness.

Increase OEE with predictive maintenance

Just one minute of unplanned downtime can cost up to 10,000 euros (in an expensive production facility)! That's 10,000 good reasons to review and optimise outdated maintenance concepts. Especially since a large proportion of unplanned downtime is caused by the failure of components such as pneumatic cylinders and can be easily avoided using predictive maintenance.

Predictive maintenance with AI solutions

But what exactly is predictive maintenance?

By continuously monitoring the condition of machines and systems, critical events or creeping deviations can be detected at an early stage. Sensors on your machines and systems collect data that is then forwarded to the system or artificial intelligence. And based on the values and calculations that are obtained, the AI suggests suitable maintenance and repair measures.

This means that it's not only the current status of components such as pneumatic cylinders that is monitored, but potential faults and anomalies in the systems are also predicted. This prevents unplanned downtime, as maintenance and servicing are instigated before malfunctions occur.

Predictive maintenance in practice: monitoring cylinders

Classic tools such as operating or machine data logging have been used for a long time for diagnostics and root cause analysis.

However, they have some disadvantages compared to predictive maintenance:

• They completely ignore a lot of data and correlations
• They are far too complex and too expensive in terms of traditional programming
• They do not provide forward-looking forecasts
• They do not instigate measures at an early stage

The solution: Festo AX Industrial Apps

With standardised AI apps for predictive maintenance, Festo is making predictive maintenance accessible and scalable for everyone. One of these is Festo AX Motions Insights Pneumatic, the AI app for pneumatic cylinders from all manufacturers. The app immediately detects anomalies and malfunctions in pneumatic drives or the control chain, helping to prevent machine downtime due to component failure.

Conclusion and outlook for Industry 4.0

Optimising overall equipment effectiveness using predictive maintenance is an important step for manufacturing companies to achieve greater efficiency and competitiveness. By integrating AI solutions such as Festo AX Motions Insights Pneumatic into your systems, you can easily improve maintenance and servicing and minimise unplanned downtime. AI enables you to optimise your production and the key company figures.

Future developments and trends such as the Internet of Things (IoT) and machine learning will continue to improve and boost predictive maintenance solutions. This means that companies that already rely on AI solutions will increase their OEE figures and secure a clear competitive advantage. So what are you waiting for?