Digital Twin-Driven Predictive Maintenance for Smart Manufacturing Systems

Abstract

Unplanned equipment failure remains a principal source of productivity loss in modern manufacturing, accounting for a substantial fraction of total operating cost. This paper presents a digital twin-driven predictive maintenance (PdM) framework that couples a high-fidelity virtual representation of rotating machinery with data-driven prognostics for remaining useful life (RUL) estimation. Multivariate condition data comprising vibration, temperature, and motor-current signatures are streamed from the physical asset through a cloud-native microservice pipeline into the digital twin, where a hybrid convolutional-recurrent model fuses physics-based degradation indicators with learned temporal features. Validated on the NASA C-MAPSS turbofan degradation benchmark and an in-house bearing test-rig dataset, the proposed model attains a root-mean-square error of 11.3 cycles and a PHM08 penalty score of 410, outperforming standalone LSTM (15.2 cycles) and CNN-LSTM (13.6 cycles) baselines. Field emulation indicates a 42% reduction in unplanned downtime, a 28% reduction in maintenance cost, and a 16% improvement in overall equipment effectiveness relative to a reactive maintenance baseline. The results confirm that integrating digital twin modeling with scalable cloud-native analytics provides a deployable decision-support tool for condition-based maintenance in Industry 4.0 environments.