In manufacturing, predictive maintenance is playing a more vital role in modern operations. Predictive maintenance uses machine learning and historical data to predict when a machine is due for maintenance. This helps avoid a malfunction or breakdown, which can cause safety issues, downtime and excessive repairs — all of which risk costing manufacturers a lot of money.

For a variety of reasons, many manufacturing companies must rely on traditional maintenance methods. Despite checklists and set processes, it’s often easy to overlook warning signs until after a failure has already taken place. With the increasing availability of digital twin technology, however, the benefits of predictive maintenance are becoming more widely accessible and achievable.

What Are Digital Twins?

For the purposes of this article, a digital twin is a virtual copy of a physical device, system or process with the ability to simulate states and behaviors. It is a digital replica that mimics the properties and working dynamics of real objects. Digital twins mirror objects by collecting data through sensors; that data is then processed in real time through artificial intelligence (AI) and machine learning algorithms, which help create complex data visualizations.

Because digital twins are in near-constant communication with their mirrored objects and are continuously analyzing and accounting for new data, manufacturers can extrapolate entire lifecycles of devices with great accuracy. 

How Do Digital Twins Enhance Predictive Maintenance?

When digital twins are deployed with manufacturing equipment and machinery, they allow for a better understanding of how an asset will perform or behave at any point in time. Using a secure network, a digital twin can send real-time alerts, insights and even recommendations based on the data it has collected and analyzed from the machine it is mirroring.

Digital twin technology used in this way can help predict a fault before it actually happens. It can also anticipate when a piece of equipment might perform differently because of a change in the environment — as well as when general maintenance is necessary to keep an asset in good working condition. This allows manufacturers to improve and extend the lifespan of their equipment, reduce downtime and reduce overall maintenance costs, making digital twin technology key in predictive maintenance.

Challenges to Consider When Employing Digital Twins

The more digital twin technology is used by a manufacturer, the more complete picture it will have of the working assets and systems it owns. However, while digital twins allow for incredible accuracy where predictive maintenance is concerned, they don't come without challenges.

In order for digital twins to process data and communicate effectively, they need a robust and scalable network infrastructure, such as a dark fiber network. The lack of a powerful and reliable network like this can prevent companies from getting the most out of their digital twin technology.

Dark fiber is ideal because it is “unlit fiber,” meaning it is laid but not used by a network provider. Companies that have this unlit fiber, including manufacturers, can connect to network equipment on both ends, which allows them to establish their own high-speed private network.

This gives manufacturers full control over the network link, allowing them to configure it and scale it as needed, which can come in handy when deploying digital twin technology. However, this does mean that manufacturers will have to fully monitor the network on their own, including protecting it against cyberattacks.

It’s important for manufacturers to adopt a good network monitoring strategy. This requires using the right monitoring tools for their needs and being proactive about alerts. It means using security tools to regularly scan the network for vulnerabilities and any incidents that could indicate a potential threat, as well as using encryption to protect data.

It is also a good idea to limit who has access to network controls and digital twins. This prevents inside breaches and unauthorized access.

Conclusion

While the challenges of adopting a robust network and keeping it secure are significant, properly deployed digital twins can be a powerful tool for predictive maintenance. Staying ahead of equipment failures can improve operations by reducing unplanned downtime, increasing reliability and scaling more efficiently, which can ultimately boost profits.

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