DIGITAL TWIN TECHNOLOGY FOR PREDICTIVE ANALYSIS AND OPTIMIZATION OF ENGINEERING SYSTEMS
DOI:
https://doi.org/10.69980/gcxn9d52Keywords:
Digital twin technology, predictive analysis, engineering systemsAbstract
Digital twin technology was examined for predictive analysis and optimization of engineering systems using a quantitative primary research design. Primary operational data were collected from a selected engineering system through sensors, performance monitoring devices, and operational records. The measured variables included temperature, vibration, pressure, load, energy consumption, operating speed, fault frequency, downtime, maintenance cost, and system output. A digital twin model was developed through data preprocessing, virtual modeling, predictive model integration, and optimization analysis. The model was trained and tested using the collected dataset, and its performance was evaluated through prediction accuracy, mean absolute error, root mean square error, fault detection rate, and response time. The results showed that the digital twin model achieved 93.80% testing accuracy and a 91.70% fault detection rate. Optimization improved system efficiency from 78.40% to 89.60%, reduced energy consumption from 148.62 kWh to 124.38 kWh, reduced downtime by 54.41%, and decreased maintenance cost by 29.96%. Statistical analysis confirmed significant differences between baseline and optimized conditions at the 0.05 significance level. Digital twin technology enhanced predictive decision-making, operational reliability, maintenance planning, and sustainable performance improvement in engineering systems across data-driven industrial and applied engineering environments, while supporting more accurate monitoring, fault prevention, and resource-efficient operation over time.
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