Digital twin for predictive maintenance

Abstract

Digital twin engineering is a disruptive technology that creates a living data model of industrial assets. The living model will continually adapt to changes in the environment or operations using real-time sensory data as well as forecast the future of the corresponding infrastructure. A digital twin can be used to proactively identify potential issues with its real physical counterpart, allowing the prediction of the remaining useful life of the physical twin by leveraging a combination of physics-based models and data-driven analytics. The digital twin ecosystem comprises sensor and measurement technologies, industrial Internet of Things, simulation and modeling, and machine learning. This paper will review the digital twin technology and highlight its application in predictive maintenance applications.

Advancing Offshore Oil and Gas Facilities: A Comprehensive Review of Innovative Maintenance Strategies for Enhanced Reliability and Efficiency

“…Digital twins are revolutionizing the way predictive maintenance is implemented in the offshore oil and gas industry (Liu et al, 2023). By integrating data from physical assets into virtual representations, digital twins provide real-time insights and predictive capabilities, enabling operators to optimize maintenance strategies and enhance asset performance.…”