Digital Twin-Enabled Predictive Maintenance Framework for Smart Manufacturing Systems

Abstract

Unplanned equipment downtime has been a critical problem in intelligent manufacturing, with production losses of up to 1520 per year and maintenance costs of about 2530. Conventional preventive maintenance programs are based on fixed schedules that, in most cases, do not detect faults early, resulting in low asset utilization and unexpected failures. To overcome this shortcoming, the current paper introduces a Digital Twin-Enabled Predictive Maintenance (DT-PdM) framework that combines real-time sensor data, machine learning models, and a high-fidelity virtual copy of physical assets to improve fault detection and maintenance decision-making. The suggested structure is a tripartite logic consisting of (1) data collection based on the IoT-enabled sensors that record vibration, temperature, and acoustic data at 1-second intervals; (2) a digital twin model, which simulates the behavior of a particular operation with the help of physics-based simulation and data-driven learning; and (3) a predictive analytics engine that involves the use of Long Short-Term Memory (LSTM) networks and Random Forest classifiers to detect anomalies and estimate the Remaining Useful Life (RUL). This framework was tested in a smart CNC machining environment with more than 12 months of historical data (about 2.5 TB). The experimental findings indicate that unplanned downtime is reduced by 35%, maintenance scheduling accuracy is increased by 28%, and RUL prediction accuracy is 92.4%. Also, false alarms were reduced by 18 compared to traditional threshold-based surveillance systems. The implementation resulted in an approximate 22% reduction in general maintenance costs and increased equipment availability by 85% to 93%. The results validate the assertion that the combination of digital twin technology and predictive analytics is highly beneficial for operational reliability and cost. The suggested DT-PdM model provides a data-driven, scalable approach to the further development of intelligent maintenance policies in the Industry 4.0 production setting.