Jul 2026· International Journal of Machine Learning and Cybernetics· Vol 17· 0 citations· 45 references
Computer Science
TL;DR
A novel RUL prediction framework that integrates a spatiotemporal encoding mechanism with a variational dual-gated decoding architecture, which outperforms state-of-the-art baselines across multiple subsets, achieving superior performance in terms of RMSE and Score.
This study proposes a degradation-aware dynamic masking augmentation method combined with a multiscale CNN–Transformer network to address non-stationary monitoring data, limited informative degradation samples, and the difficulty of jointly modeling local degradation patterns and temporal dependencies.
Xu-Dong Song, Guo-Hua Wu, Meng-Dan Wang et al.· Machines· 0 citations
Remaining useful life (RUL) prediction is critical for improving reliability and supporting predictive maintenance in aero-engine systems. However, existing methods have limitations in jointly modeling the spatial correlations between multi-sensor signals and the temporal evolution characteristics of the degradation pr...
Shangyi Ren, Dayong Han, Zixiang Li et al.· Applied Sciences· 0 citations
The study demonstrates that combining CNN–LSTM with an appropriate optimization strategy improves the reliability and accuracy of RUL prediction for turbofan engines and shows that Stochastic Gradient Descent provides the best convergence behaviour and prediction accuracy for the proposed architecture.
Rajneesh Kumar, Shivam Ojha, A. Shelke et al.· Scientific Reports· 0 citations
Experimental results demonstrate that the hybrid architecture consistently outperforms the standalone TCN and Reservoir components, as well as other benchmark methods, achieving substantially improved PHM scores while retaining competitive RMSE performance.
Mahika Annie Verghese, C. Columbus, E. Elakiya· Scientific Reports· 0 citations
The proposed DOA-VMD-CNN-TimeXer framework for RUL-oriented prognosis through capacity trajectory prediction and reconstruction shows closer agreement with the measured degradation trajectories and more stable prediction behavior than the compared methods, indicating its effectiveness for lithium-ion battery RUL-orient...
The proposed CNN-BiLSTM model consistently outperforms CNN, DCNN, RNN, and BiLSTM approaches in terms of RMSE and MAE, providing more accurate and robust prediction results for aeroengine systems operating under complex degradation conditions.
