Abstract
In the literature, many applications of Digital Twin methodologies in the manufacturing, construction and oil and gas sectors have been proposed, but there is still no reference model specifically developed for risk control and prevention. In this context, this work develops a Digital Twin reference model in order to define conceptual guidelines to support the implementation of Digital Twin for risk prediction and prevention. The reference model proposed in this paper is made up of four main layers (Process industry physical space, Communication system, Digital Twin and User space), while the implementation steps of the reference model have been divided into five phases (Development of the risk assessment plan, Development of the communication and control system, Development of Digital Twin tools, Tools integration in a Digital Twin perspective and models and Platform validation). During the design and implementation phases of a Digital Twin, different criticalities must be taken into consideration concerning the need for deterministic transactions, a large number of pervasive devices, and standardization issues. Practical implications of the proposed reference model regard the possibility to detect, identify and develop corrective actions that can affect the safety of operators, the reduction of maintenance and operating costs, and more general improvements of the company business by intervening both in strictly technological and organizational terms.
Highlights
A Digital Twin is a digital model of a particular physical element or a process with data connections that enable convergence between the physical and virtual states at an appropriate rate of synchronization [1]
Different enabling technologies of Industry 4.0 such as the Internet of Things (IoT), Cloud Systems and Big Data Analytics contribute to the creation of what is the Digital Twin of a physical process, i.e., a mathematical model able to describe the process, the product or the service in order to carry out analyses and apply company strategies
This paper has proposed a reference model for the implementation of Digital Twin models with the purpose of enhancing the safety level of employees in the workplace
Summary
A Digital Twin is a digital model of a particular physical element or a process with data connections that enable convergence between the physical and virtual states at an appropriate rate of synchronization [1]. Digital Twin solutions integrate artificial intelligence, machine learning and software analytics with data collected in production plants to create digital simulation models that are updated when production process parameters or working conditions change [2]. This is a self-learning system, using data collected from various sources: from sensors that transmit operating conditions; from experts, such as engineers with a deep knowledge of the industrial domain; from other fleets of similar machines; as well as integrating historical data related to the past use of plant components [3]. Digital Twin is a leading opportunity to increase safety through serious games for industrial safety, as process simulation can train and improve industrial resilience [11]
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