REMAINING USEFUL LIFE PREDICTION USING DIGITAL TWINS AND MULTI-SENSOR INDUSTRIAL DATA

Abstract

Accurate prediction of Remaining Useful Life (RUL) of industrial equipment is essential for improving reliability, reducing unexpected failures, and optimizing maintenance strategies in modern manufacturing environments. Traditional maintenance approaches often depend on scheduled inspections and historical failure records, limiting their ability to capture real-time equipment degradation patterns. This paper proposes a Remaining Useful Life prediction framework using Digital Twins and multi-sensor industrial data by integrating Digital Twin technology, Industrial Internet of Things (IIoT), machine learning, sensor fusion, cloud computing, and predictive analytics. The proposed methodology continuously collects operational data from industrial equipment, synchronizes physical and virtual models, analyzes degradation patterns, and predicts future equipment health conditions. Experimental evaluation demonstrates improvements in RUL prediction accuracy, failure forecasting capability, maintenance planning efficiency, equipment reliability, and operational sustainability. The proposed framework provides a scalable and intelligent solution for predictive maintenance in Industry 4.0 environments by enabling proactive decision-making and adaptive asset management. Keywords— Remaining Useful Life Prediction, Digital Twin, Multi-Sensor Data Fusion, Predictive Maintenance, IIoT, Machine Learning, Industrial Analytics, Smart Manufacturing.