Digital twin technology is starting to receive interest in the industry and, more recently, in academics. The digital twin is best described as the seamless integration of data between a physical and virtual system in either direction. The internet of things (IoT), cloud computing, edge computing, digital twins, and artificial intelligence all bring challenges, applications, and enabling technologies. Despite the fact that the idea of the “digital twin” has been well established over the past few years, there are still many different interpretations that result from varied professional viewpoints. The digital twin is primarily introduced in this chapter, along with its advantages and practical applications in different sectors. The authors have presented a detailed review of the artificial intelligence-driven digital twin, sensor-driven digital twin, cloud-driven digital twin, and edge computing-driven digital twin. It looks at the architectures, enabling technologies, potential obstacles, and challenges of current research on digital twins.
Top1. Introduction
Digital twin is a digital representations of physical objects, processes, or services. A digital twin can be a digital replica of an object in the physical world, such as a jet engine or wind farm, or even larger items such as buildings or even whole cities. A digital twin is, in essence, a computer program that uses real-world data to create simulations that can predict how a product or process will perform. Before actual devices are manufactured and deployed, data scientists and IT specialists can run simulations on digital twins, which are virtual replicas of physical devices. Real-time IoT data can also be used by digital twins to optimize performance using AI and data analytics. Essential technologies for implementing a cyber-physical system (CPS) are thought to be digital twin technology. By fully using physical models, sensor updates, operation histories, and other data, simulation technology incorporates interdisciplinary, multi-physical quantity, multi-scale, and multi-probability information. It is a mapping technology that allows for the virtualization of the entire lifecycle of a piece of physical equipment (A. Fuller et.al., 2020).
When considered in the context of the digital twin's beginnings and current development, its applications predominantly center on the stages of product design, operation, and maintenance (Khajavi,2019). However, with the fast and easy adaptation of innovative information and communication technologies like big data, the Internet of Things, mobile Internet, and cloud computing, the digital twin has expanded beyond the conventional stages of product design and operation. This section defines digital twin technology to make it easier to comprehend.
In essence, a digital twin is computer software that represents how a process or product would work using data from the real world (A. Fuller et.al., 2020).To enhance accuracy, these systems can incorporate software analytics and the internet of things artificial intelligence. These virtual models have become a mainstay in contemporary engineering to spur innovation and boost efficiency.
1.1 Benefits of Digital Twin Technology
Digital twin technology provides massive advantages to society. Developing a digital twin enables the advancement of major technological trends; prevent expensive breakdowns in physical items, and test processes and services leveraging enhanced analytical, monitoring, and predictive skills. Digital Twin optimizes performance and efficiency, choices may be made quickly and more effectively using simulations, actionable insights, and an integrated picture of all online and offline data. It enables a full view of all historical data and real-time data in one location, data centers can be eliminated and unlock the value throughout a project's lifecycle (Khajavi et al., 2019). Maintenance and operations are greatly enhanced using real-time sensor data and predictive recommendations are made by machine learning and artificial intelligence. The value from operations, the amount of development effort, and the time it comes to the market is all immensely improved by creating digital twins of complex assets, factories, and processes.
1.2 Importance of Digital Twin Technology
Digital twins are effective for boosting performance and innovation. Think of it as your most talented product technicians equipped with the most cutting-edge monitoring, analytical, and predictive tools. Within the next five years, digital twins will represent billions of items. Physical world product specialists and data scientists, whose employment is to comprehend what data says us about operations, will have new chances to collaborate as a result of these proxies for the physical world (Marr. B,2017). For a better understanding of consumer demands, d eloping improvements to current products, operations, and services, and even driving the innovation of new businesses, digital twin technology aids businesses in enhancing the customer experience.
The organization of the rest of this book chapter is as follows. The second section presents the literature review on digital twin. Section 3 presents digital twin driven architecture. In section 4 we present artificial intelligence-driven digital twins with their applications and challenges in different sectors. Section 5 presents sensor-driven digital twin technologies. We present cloud and edge-driven digital twin technologies with their challenges in section 6. Finally, the conclusion is presented in the last section.