Enhancing Maintenance Management of Critical Equipment Using Digital Twin

Attia Hussien Gomaa *

Industrial Engineering, Mechanical Eng. Department, Faculty of Eng. Shubra, Benha University, Cairo, Egypt.
 
Research Article
Global Journal of Research in Engineering and Technology, 2022, 01(01), 046-054.
Article DOI: 10.58175/gjret.2022.1.1.0015
Publication history: 
Received on 04 September 2022; revised on 23 October 2022; accepted on 26 October 2022
 
Abstract: 
Digital twin (DT) technology develops virtual models of objects digitally, simulating their behavior in the real world based on data. Recent advances in DT have greatly facilitated the development of predictive maintenance for critical equipment, enabling accurate identification of equipment conditions, proactive prediction of faults, and enhanced reliability. This research aims to explore the previous studies on DT for proactive maintenance applications of critical equipment. The literature review shows the importance of DT in maintenance management for improving equipment RAMS (reliability, availability, maintainability, and safety). Finally, the findings of this study will be valuable to professionals who desire and aspire to implement DT to achieve maintenance excellence.
 
Keywords: 
Manufacturing; Maintenance; Fault prediction; Digital twin; Machine learning; Continuous improvement
 
Full text article in PDF: