Hello, my name is Sonika Krishna Gogineni and I would like to introduce you to the subject of enabling technologies for digital trends. This module deals with technologies, which are required to actually realize a digital twin. So when we think of implementing digital twins on a technological level the following questions may arise. What technologies do I need to actually make a digital twin? Are there solutions and supporting technologies already available for me to readily use? Are there any steps of procedures I need to follow to systematically development a digital twin? This module provides you with the answers to these questions. In order to make it easy to understand the technology blocks and the processes of building a digital twin, a simple system architecture was developed and is illustrated here. It consists of four phases derived from the digital twin lifecycle model. Namely initiation, modeling, enrichment and utilization, and reuse. Each of them have technology blocks which need to be considered in order to realize a digital twin. In the next few slides these will be explained phase by phase with examples. The initiation phase is all about identifying the scope of the digital twin and deriving the basic Requirements. As an example, I want to develop a digital twin for predictive maintenance of a car. The first step is to identify the scope of the digital twin and in this example the scope is a product digital twin of a car which monitors various parameters which helps in predictive maintenance. The business model is to schedule servicing better. The financial model is to help reducing maintenance and service costs. The next step is to identify the basic requirements in order to develop the digital twin based on the defined scope, business and the financial model. For example, the requirement to collect data from the cars engine and the requirement for the analysis of data and further such requirements are defined here. Once the basic requirements for the digital twin are clear the next phase is the modeling phase. For a physical object its unique instance is created in the digital world to form a digital twin. The model is connected with existing data and systems in order to interact with the company and provide functionalities. The digital twin is also connected with contextual and legacy data to provide a full representation of the object in its environment. As the picture shows, a bike is modeled in its production and probable work environment with its contextual environmental data. Once the digital twin model is ready and connected it needs to be enriched for data and used to achieve its goal. This brings us to the face of enrichment and utilization. In order to receive data from the physical object it is important to establish the communication between the physical object and the digital twin. The collected data needs to be stored, analyzed and visualized in order to fulfill the business model defined for the digital twin. Hence, in this face the following has to be considered. Connectivity protocols and standards, security, middleware, data storage and required data science based on the applications needs. An existing digital twin is a collection of models, simulations and operational data, legacy data and many more elements. Hence it is important to consider version management in order to maintain clear records of the digital twin, as it facilitates easy reuse. Hence, I would really like to thank you for your attention and would like to recommend you to our other videos.
