Nvidia brings Omniverse closer to connected digital twins – and full automation

Disclosure: Nvidia is a customer of the author.

Nvidia this month announced a series of improvements to its Omniverse creation and simulation tool. Collectively, they more closely connect metaverse instances to the real-world devices they emulate, ensuring that all associated digital twins are synchronized in real time with their real-world counterparts, greatly increasing realism.

This will have several short-term benefits for remote administration of any solution covered by a metaverse simulation; it will also provide a shorter path to full automation and define a framework that should make this last step faster and more reliable.

Let’s explore the Connected Metaverse this week and why it will accelerate full automation.

Connected digital twins to the rescue

The concept of connected digital twins is key to making simulations more realistic by using sensors to ensure twins realistically mimic their real-life counterparts. This would allow a remote (or even onsite) administrator to better locate and assess problems before they lead to failures. For example, in the case of a bearing sensor that would typically be invisible to the human eye, the sensors could translate a failure into a visual cue on the twin, highlighting the problem. (The admin can view the issue through a metaverse instance virtually or by using AR glasses.)

Quickly identifying out-of-specification equipment in danger of failure (due to excessive heat, noise or vibration) would facilitate preventive maintenance and provide a richer support interface than a traditional dashboard . This means that a technician is more likely to arrive on the scene with the necessary tools and parts to correct the problem rather than first making a diagnosis and then returning to fix the problem.

Add artificial intelligence (AI) to the mix

Nvidia also announced AI training to help diagnose a problem and give advice on how to fix it using synthetic data to reduce AI training time. Take that failed bearing, for example – rather than just replacing one, it might make more sense to replace several other perishable parts at the same time to minimize disassembly and assembly costs. The AI ​​could determine, based on historical repairs, that the bad bearing is a precursor to other failures, allowing technology to anticipate and fix future problems before they arise.

For example, non-critical repairs can often be handled more cheaply if the technology is on site and already working on something else.

Next step: robotic repairs?

When you combine Nvidia’s robotics efforts, repairs can bypass human technology and use a trained robotic repair that the remote admin can trigger with an AI interface. Depending on what best suits the circumstances, the administrator can initiate an automated AI response using the equipment already on site, dramatically speeding up the repair.

With this type of system in place, the role of the administrator becomes simpler because the tasks are well defined and their triggers are already fully instrumented and integrated into the solution. You may not need an administrator at all.

Go full automation

The path to full automation could take a decade or more. The first steps would be to fully instrument the areas to be covered, create connected digital twins of the infrastructure to be maintained, and then use AI based on a combination of real and synthetic data to optimize maintenance and repairs. This data could be used as part of the training package for on-site robots, while administrative functions are automated; the latter should be the easiest part of the process.

Ensuring data integrity and anticipating its eventual use for AI training would be essential to ensure rapid and efficient deployment of subsequent functions. I expect the most difficult step to be automating the repairs. Few systems are created today with the requirement that they be maintained by robot, but that will change over time.

I’ve visited sites that are continuing the augmented reality maintenance approach, suggesting that the initial move to connected digital twins may already be happening at multiple sites. We now have a reasonably well-defined path to full data center automation (that’s what Nvidia has demonstrated). This Nvidia video shows how you might initially use the metaverse to connect to a data center, and this one talks about automating an entire site. Finally, this video shows what could happen if an administrator had too much time and too little supervision.

Okay, that last one was a joke. But it demonstrates that in the metaverse, rules don’t have to apply, ultimately opening the door to innovations we can only imagine.