Abstract: To maintain efficiency and continuity in Industry 4.0, intelligent manufacturing systems use enhanced problem detection and condition monitoring. Existing models typically miss uncommon and essential errors, causing expensive downtimes and lost production. ResXEffNet-Transformer (RXET), a hybrid deep learning model, improves defect identification and pre-dictive maintenance by integrating ResNet, Xception, Efficient-Net, and Transformer-based attention processes. The algorithm was trained on a five-year Texas industrial dataset using IoT-enabled gear and digital twins. To manage data imbalances and temporal irregularities, a strong preprocessing pipeline included Dynamic Skew Correction, Temporal Outlier Normalization, and Harmonic Temporal Encoding. The Adaptive Statistical Evolutionary Selector (ASES) optimized feature selection using the Stochastic Feature Evaluator (SFE) and Evolutionary Divergence Minimizer (EDM) to increase prediction accuracy. The RXET model beat traditional methods with 98.9% accuracy and 99.2%AUC. Two new performance metrics, Temporal Fault Detection Index (TFDI) and Fault Detection Variability Coefficient (FDVC), assessed the model’s capacity to identify problems early and consistently across fault kinds. Simulation findings showed the RXET’s superiority in anticipating uncommon but essential errors. Pearson correlation (0.93) and ANOVA (F-statistic: 8.52) validated the model’s robustness. The sensitivity study showed the best performance with moderate learning rates and batch sizes. RXET provides a complete, real-time problem detection solution for intelligent industrial systems, improving predictive maintenance and addressing challenges in Industry 4.0, digital twin technology, IoT, and machine learning. The proposed RXET model enhances operational reliability in intelligent manufacturing and sets a foundation for future advancements in predictive analytics and large-scale industrial automation.

Min Li, “Enhanced State Monitoring and Fault Diagnosis Method for Intelligent Manufacturing Systems via RXET in Digital Twin Technology” International Journal of Advanced Computer Science and Applications(IJACSA), 15(11), 2024. http://dx.doi.org/10.14569/IJACSA.2024.01511124

@article{Li2024,
title = {Enhanced State Monitoring and Fault Diagnosis Method for Intelligent Manufacturing Systems via RXET in Digital Twin Technology},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01511124},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01511124},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {11},
author = {Min Li}
}