Abstract
Fault detection and remaining useful life (RUL) prediction are critical tasks in self-healing
network (SHN) environments and industrial cyber–physical systems. These domains
demand intelligent systems capable of handling dynamic, high-dimensional sensor data.
However, existing optimization-based approaches often struggle with imbalanced datasets,
noisy signals, and delayed convergence, limiting their effectiveness in real-time applications.
This study utilizes two benchmark datasets—EFCD and SFDD—which represent electrical
and sensor fault scenarios, respectively. These datasets pose challenges due to class imbalance
and complex temporal dependencies. To address this, we propose a novel hybrid
framework combining Attention-Augmented Convolutional Neural Networks (AACNN)
with transformer encoders, enhanced through Enhanced Ensemble-SMOTE for balancing
the minority class. The model captures spatial features and long-range temporal patterns
and learns effectively from imbalanced data streams. The novelty lies in the integration
of attention mechanisms and adaptive oversampling in a unified fault-prediction architecture.
Model evaluation is based on multiple performance metrics, including accuracy,
F1-score, MCC, RMSE, and score*. The results show that the proposed model outperforms
state-of-the-art approaches, achieving up to 97.14% accuracy and a score* of 0.419, with
faster convergence and improved generalization across both datasets.