A novel approach to digital twin-based energy efficiency monitoring and failure analysis in industrial applications

Machine tools are critical to modern manufacturing, yet their high energy consumption and vulnerability to faults present significant operational challenges. While predictive models can enhance energy optimization and fault diagnosis, their performance is often constrained by the scarcity of high-quality training data. To address this gap, this study presents a real-time digital twin (DT) framework that integrates OPAL-RT HIL simulation with OPC-UA-based cloud communication. The system enables both energy monitoring and synthetic fault data generation under diverse machining conditions. The DT operates in a bidirectional loop with a cloud-based data acquisition layer, allowing real-time parameter input and retrieval of simulated outputs. Model fidelity is verified by aligning simulation results with real-world CNC machine measurements and further confirmed through pattern-based external validation. The framework is applied to analyze energy consumption across varying machining parameters - such as electrospindle speed, feed rate, tool length, and depth of cut - and to simulate bearing fault scenarios for evaluating their impact on power consumption. These simulations produce labeled datasets suitable for future diagnostic and predictive maintenance applications. This work delivers a validated, closed-loop DT framework that unites high-fidelity OPAL-RT simulation, real-time OPC-UA data exchange, and synthetic data generation, extending predictive maintenance capabilities beyond those of prior modeling or diagnostic approaches. The proposed methodology offers a scalable foundation for energy-aware machining and real-time fault detection, contributing to sustainable manufacturing practices and operational resilience in smart industrial systems.