Computer aided design of an asset such as a motor has existed for decades. Over time, sensors have been installed in physical devices to monitor parameters such as temperature and frequency. Predictive analytics can then be used to analyze the generated data to predict failures. Up until now, Digital Twins have been created to model a machine such as a motor, generator, or medical device, to improve its operational performance during its product life cycle. This is the first
generation of Digital Twins.
To explain how advanced Digital Twins operate, let’s use a simple example using the app Waze. You can do a what-if simulation of your drive from A to B with Waze taking into consideration environmental conditions such as traffic, weather
and road blockages. Then you can use the app to monitor and guide you during your trip, offering itinerary updates in real time and obtaining relevant information from other drivers through social networking.
The difference between the Waze app and the simpler first-generation Digital Twins mentioned above, is that it is working in a complex physical environment, using a map and location services in combination with situational relevant information from other systems, all while collaborating with the user in real time.
A far more comprehensive use would be to monitor a city’s traffic with continuous updates from tens or hundreds of thousands of sensors and coordinate the relevant traffic control systems. By optimizing traffic lights, accident management, infrastructure utilization, and emergency response, a Digital Twin can intelligently control the overall flow of vehicles and improve the effectiveness of the system.
Large commercial areas with many pedestrians such as in an airport or a train station, can use digital twins to improve the customer experience. For example, a Digital Twin of a train station can guide passengers using AR (Augmented Reality) from one platform to another. In frustrating situations, such as a 25 minute train delay, local retail and food outlets can automatically offer discounted items or food-to-go. Focused advertisements can combine demographic information with situational context to understand the current customer experience and create new sales opportunities.
Real Time and Event Driven
A Digital Twin should be able to receive events and analyze them anywhere, add context from historical profiles, relevant external sources, and pass the information on as it is happening.
For example, if a fire alarm goes off, that event is instantly analyzed and contextualized to determine if it is a real threat. The alert is sent to relevant business systems such as security, door control, and emergency services. These systems are able to respond in real time and dynamically change as the situation evolves. This type of asynchronous data must be dealt with as it occurs – not put in a database for later analysis.