A Novel Hybrid Transformer for RUL Prediction in Predictive Maintenance for Smart Manufacturing

Ensuring continuous operation and minimizing unexpected failures are critical priorities in industrial manufacturing. Due to this requirement, smart manufacturing has transformed maintenance strategies, moving from traditional scheduled approaches to predictive maintenance (PdM), which leverages actual machine health conditions. A powerful technique that enables accurate PdM is Remaining Useful Life (RUL) estimation. Accurate RUL prediction allows the assessment of machine health, thereby facilitating timely and appropriate maintenance decisions to sustain continuous operation and reduce repair costs. While several existing models have been developed for RUL estimation, they often struggle to capture long-term dependencies in time series data, limiting their predictive accuracy. In this study, we propose a novel architecture that combines a one-dimensional Convolutional Neural Network (1D-CNN) with two consecutive transformer encoders enhanced by Fourier transforms to address these challenges. The performance of the proposed model was evaluated based on two well-known benchmark datasets, C-MAPSS and its improved version, N-CMAPSS. The experiment results show that our approach outperforms current state-of-the-art methods in both prediction accuracy and computational efficiency, demonstrating its potential for practical applications.