The Digital Twin can improve production and reduce downtime – Data gathered into digital twin technology can be used to optimize equipment, says Mathieu Bélanger-Barrette.

A digital twin is a virtual replica of a physical asset. But it’s a lot more than that. The digital twin is a combination of IoT, machine learning, and AI put together to create a replica of your physical machine. It will analyse and predict the impact of actions on that machine.

A digital twin draws on the Big Data trend. We have entered an era where data is one of the most important resources companies can get, either for their users and their machines. The goal now is to gather as much data as possible in order to detect trends or to react to given situations.

Moving to Predictive Solutions with the Digital Twin

If you’re managing a robot, you’re observing trends. If you see a particular use of a certain robot, it may prompt you to perform preventive maintenance more frequently to keep the unit from going down. What we generally see in the industry is reactive and proactive solutions to problems. With a digital twin – once you have enough data, experience, and a sufficient sample size – you get the ability to move to predictive, and eventually, prescriptive solutions.

The digital twin is not simply a robot or a turbine that acts like the actual machine that you are operating. The digital twin the summation of all the past experiences and previous data about your physical machine joined into a digital representation. The digital twin can draw on part experiences to predict when a certain failure or other unwanted event will occur, and it can learn how to avoid that event.