A SYSTEMATIC REVIEW OF DIGITAL TWIN SYSTEMS FOR IMPROVED PREDICTIVE MAINTENANCE OF EQUIPMENT IN SMART FACTORIES

Abstract

The deployment of intelligent systems in the management and monitoring of the components of production systems have led to improved quality and enhanced productivity on the manufacturing shop floor. This paper presents a systematic review of the digital twin and other intelligent systems for use in the predictive maintenance of equipment on the shop floor. Many databases, such as the Google Scholar, Scopus, IEEE Xplore, Research Gate, and Science Direct were used for data collection. The study revealed that intelligent systems such as the digital twin are effective tools for predictive maintenance of equipment in production systems. This has been found to improve productivity and reduce downtime in production systems. The study highlights the current trends, benefits and limitations in the deployment of intelligent systems such as the Digital Twin, for use in the predictive maintenance of equipment in smart factories.