Digital Twin Integration in Engineering Asset Management: A Technical Approach from an Information Science Perspective

Abstract

Development of Industry 4.0 has changed the context of engineering asset management by implementing innovative digital technologies, in particular Digital Twin (DT). This study explores the possibilities of incorporation of digital twin systems in engineering asset management and takes a technically oriented path which is based on information science. Based on the Intelligent Manufacturing Dataset of Predictive Optimization, the correlation coefficient between key operational parameters like temperature, vibration, power consumption, latency of the network, loss of packets and quality control parameters and their contribution towards efficiency of assets are assessed. An extensive data-analytical approach that involved prep reprocessing, statistical computation, and visualization was utilized to derive usable intelligence. The main relationships obtained show that predictive maintenance scores relate to error rates significantly, which indicates the importance of real-time data to improve the approach to maintenance. This study identifies how a given operation mode affects the result of efficiency and how having latencies in the network severely affects the quality of production, explaining the need to establish an effective information exchange to achieve efficient digital twin work. The study also illustrates how leveraging the field of information science components such as data modeling, semantic interoperability and structured data governance in the implementation of digital twin can enhance engineering asset managerial decision-making and predictive analytics. The findings indicate that with the assistance of smart data integration, digital twins present significant potentials in minimizing the frequency of downtime, maximizing industrial operations, and enhancing the assurance of industrial resources. This study will be of both theoretical and praxis value as it presents an example of a model to evaluate digital twin efficacy based on real-time data analysis and information-based structures. When considering the restrictions related to the application of simulated datasets, the study still creates a solid group of precedents to be explored in the future on the topic of real-life uses, greater predictive capabilities, and superior asset lifecycle management possible through the implementation of digital twin systems applied in environments of smart factories.