Why GE Digital Believes All Machines Should Have A ‘Digital Twin’
GE Digital CEO and Chief Digital Officer Bill Ruh sits down to explain the development of Predix and how the company is using it in industrial IoT
Q: How do you define a Digital Twin, and what benefits does it bring?
A: General machine learning has a place, but how do you turn it into something of value? For us, that value is utilizing that technology, as well as simulation and modeling together. So we use machine learning, simulation and modeling to create what we call a Digital Twin.
Machine learning is utilized to look at this vast volume of data that’s being thrown off machines, and being collected, so we can look for patterns.
Machine learning can say, “Hey, something’s going to break.” So what do you do about it? Fix it now, should I fix it tomorrow, or should I wait a year to fix it?
By the way, that question is worth a lot of money to people. So the ability to know when a part is going to break on a jet aircraft engine is awfully important–especially to the people who are flying! But the ability to know, “Oh, I can fix it tonight after normal operations,” and in a scheduled downtime, or do I have to delay the next flight? Those are important considerations to be made.
We think that you’ll be able to determine whether a part wil break hundreds of flights ahead of time. Everything then becomes normal maintenance. This is no unscheduled downtime. That is, we think, a game-changer for the world.
The Digital Twin is the ability to see that something’s going to occur. And then it’s the ability to model out and do a million simulations, which cloud computing allows us to now do. It will tell you, “Here is the most efficient way to deal with this situation.” That’s what we see as a Digital Twin; you’re building a model of a specific machine, constantly monitoring it, always looking to make it more efficient.
In the future, I would say, every human will have their own Digital Twin. All the data we get from these medical machines, as well as from FitBits, et cetera, we can finally take and put it into something and allow that Digital Twin to say, “Here are suggestions on how to eat, on how to exercise … oh, by the way, now’s the time to go to the doctor.”
This ability for every machine, biological or mechanical, to be able to have a Digital Twin, that’s the magic that moves it away from general machine learning and into delivering outcomes that are unique for an individual machine.
Originally published on eWeek
