## 3 Description of Methods and Tools  
A digital twin can be based on a two-dimensional (2D) or three-dimensional (3D) geometric entity for mechanical equipment or a building, up to a more sophisticated multi-physics model that will represent an entire structure interacting with the environment and its users throughout its lifecycle. It could even be a network of digital twins that would supervise the operation of a whole system.  
According to analyses, there are 5 ways to categorize a digital twin, as shown in Figure 1.  
The first category concerns the collection of all the system information that is required to create the digital twin, such as engineering drawings, the operation instruction of the plant, etc. The second category contains the collection of historical data from a device to create a digital path. This includes various sensor data from the device as well as recording periods of maintenance or the occurrence of a fault. The third category collects the data from all possible devices, which provides the possibility of collecting information that would help in a statistical analysis of the data, such as the average failure time, the standard deviation, which part fails more easily, etc.  
In the second category, the hierarchy of digital twins is concerned as shown in Figure 2. Firstly, the digital twin of a part is created for better capture of the physical, mechanical, and electrical properties of a component depending on the operational conditions, e.g., a structure under a thermal loading or an electronic circuit. Next, there is the digital twin of the whole product, which consists of many pieces created in the previous stage and studying how they interact with each other. The system twin is the highest and most complex level of analysis, in which an integrated system of products that interact with each other is modeled and can be studied and maintained in parallel by monitoring the operating process through platforms to optimize efficiency and effectiveness. The last category, not necessarily involved with the above or necessary in every application, concerns the digital twin of a process and not a physical one [3].  
In the third category, we have the digital twin depending on the use that is required and how each implementation group's categorization is formed. For example, Siemens considers that, for applications in digital twin, there are 3 categories. The product category focuses on individual products. The production category is about the representation of a production process and is used to design processes that are robust and flexible, and finally, the digital twin performance category brings together the operational data of a production process to optimize performance [4].  
Another case is that of XMPRO, where the digital twin reflects the current state of a device. In this case, it is simply a dashboard, similar to the one used for supervisory control and data acquisition (SCADA), and the digital twin is used to simulate possible scenarios, utilize the data in it, and adjust the system accordingly [5].  
Figure 1: DT classification, based on information given in [3].  
The application and the idea behind the digital twin are common but differ in implementation, as shown in various application examples. The case of a machine and its operation in a system differs from a production process or an economic digital twin.  
The last categorization of a digital twin concerns its complexity. This complexity is expressed in terms of autonomy, intelligence, learning, and fidelity. A scale of 1 to 5 is used, where 5 refers to a digital twin that replaces a human being for certain mundane tasks. This classification system is not easy to use as the digital twin is likely to be competent in one category and not in others. However, these classifications will be improved in the future, and this is due to the use of artificial intelligence that has the ability to be integrated on top of the digital twin [6].