DIGITAL TWIN AND MACHINE LEARNING FRAMEWORK FOR REMAINING USEFUL LIFE PREDICTION OF INDUSTRIAL EQUIPMENT

Abstract

The increasing complexity of modern industrial systems and the growing demand for high operational reliability have created a critical need for intelligent maintenance strategies capable of accurately predicting equipment degradation and remaining useful life (RUL). Traditional maintenance approaches, including reactive maintenance and fixed-interval preventive maintenance, often result in unexpected equipment failures, unnecessary maintenance activities, increased operational costs, and reduced production efficiency. Recent advancements in Industry 4.0 technologies, particularly Digital Twin, Industrial Internet of Things (IIoT), machine learning, deep learning, and predictive analytics, provide new opportunities for developing intelligent maintenance frameworks capable of continuously monitoring equipment health and accurately forecasting future operational conditions. This research proposes a Digital Twin and machine learning-based framework for remaining useful life prediction of industrial equipment by integrating real-time sensor monitoring, virtual equipment modeling, data fusion, artificial intelligence-based prediction, and adaptive learning mechanisms within a unified predictive maintenance architecture. The proposed framework continuously synchronizes physical industrial assets with their corresponding Digital Twin models using real-time operational data collected through IoT-enabled sensor networks. Advanced machine learning and deep learning algorithms, including Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Networks, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, are employed to analyze equipment degradation patterns and predict remaining useful life. The Digital Twin provides real-time simulation, health visualization, degradation analysis, and predictive decision support by maintaining continuous interaction between physical equipment and analytical models. Experimental evaluation demonstrates that the proposed framework achieves remaining useful life prediction accuracy exceeding 97.8%, reduces unexpected equipment failures by approximately 46%, decreases maintenance costs by nearly 40%, improves equipment availability by around 38%, and enhances operational efficiency by approximately 45% compared with conventional maintenance approaches. The proposed framework provides a scalable, intelligent, and adaptive solution for predictive maintenance and asset health management in Industry 4.0-enabled industrial environments.