A Hybrid Large-Kernel CNN and Markov Feature Framework for Remaining Useful Life Prediction

Remaining Useful Life (RUL) prediction has become a crucial component in predictive maintenance and condition-based operation with the rapid advancement of industrial automation and the increasing complexity of mechanical systems. Although existing deep learning models, such as Long Short-Term Memory (LSTM) networks and conventional Convolutional Neural Networks (CNNs), have demonstrated effectiveness in modeling equipment degradation from multivariate sensor data, they still face several limitations. Recurrent architectures often suffer from vanishing gradients and struggle to capture long-term dependencies, while CNN-based methods typically rely on small convolutional kernels and deterministic feature extractors, limiting their ability to model long-range dependencies and stochastic degradation transitions. To address these challenges, this study proposes a novel hybrid deep learning framework that integrates large-kernel convolutional feature extraction with Markov transition modeling for RUL prediction. Specifically, the large-kernel CNN captures both local and global degradation patterns, while the Markov feature module encodes probabilistic state transitions to characterize the stochastic evolution of equipment health. Furthermore, a lightweight channel attention mechanism is incorporated to adaptively emphasize degradation-sensitive sensor information, thereby enhancing feature discriminability. Extensive experiments conducted on the NASA C-MAPSS turbofan engine dataset demonstrate that the proposed model consistently outperforms conventional CNN, LSTM, and hybrid baselines in terms of Root Mean Square Error (RMSE) and the NASA scoring metric. The results verify that combining deep convolutional representations with probabilistic transition information significantly enhances prediction accuracy and robustness in industrial RUL estimation tasks.