Jul 2026· International Journal of Machine Learning and Cybernetics· Vol 17· 0 citations· 45 references
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.
Accurate remaining useful life (RUL) prediction is essential for condition-based maintenance and safe aero-engine operation. To address non-stationary monitoring data, limited informative degradation samples, and the difficulty of jointly modeling local degradation patterns and temporal dependencies, this study proposes a degradation-aware dynamic masking augmentation method combined with a multiscale CNN–Transformer network. The 21 sensor variables in the NASA C-MAPSS dataset are first grouped by physical meaning and reconstructed into four-dimensional state features. A degradation-state score integrating local variance and trend slope is then used to adapt temporal masking probabilities across degradation stages, while feature masking probabilities are assigned according to feature importance. Masked positions are filled with adjacent unmasked observations, and invalid augmented samples are removed through trend consistency verification. A multiscale CNN with channel attention extracts local degradation features, and a Transformer encoder captures temporal dependencies. Bayesian optimization is used to determine key hyperparameters. On FD001, the proposed method achieves an MSE of 348.3970, an MAE of 8.3908, and an R2 of 0.9227, reducing MSE and MAE by 4.27% and 28.42%, respectively, compared with ML-RFR. It also achieves the highest R2 of 0.9369 on FD003. Cross-dataset and multi-seed experiments further confirm its applicability and stability.
Xudong Song, Guohua 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 process. Hence, this study develops a knowledge-enhanced spatiotemporal framework for system-level aero-engine RUL prediction. Firstly, a graph based on the Pearson correlation coefficient (PCC) is constructed from monitoring data to capture data-driven dependencies among sensors. Afterwards, a thermodynamic-cycle-mechanism prior is incorporated into the PCC-based graph through the Hadamard product, forming a knowledge-enhanced graph that emphasizes physically meaningful sensor relationships. Subsequently, an enhanced graph attention module is designed to extract discriminative spatial representations from the knowledge-enhanced graph. Furthermore, relational representations between adjacent time steps are constructed to capture implicit temporal correlations and local degradation dynamics. Finally, a dual-stream GRU with an attention mechanism is employed to model the fused feature stream and relational feature stream for RUL prediction. Experiments on the CMAPSS and N-CMAPSS datasets demonstrate that the proposed method achieves competitive and overall superior performance compared with nine state-of-the-art methods. KESTF achieves the best average RMSE/Score of 12.83/580 on CMAPSS and 5.94/3468 on N-CMAPSS, validating its effectiveness and robustness.
Shangyi Ren, Dayong Han, Zixiang Li et al.· Applied Sciences· 0 citations
Accurate prediction of Remaining Useful Life (RUL) is essential for predictive maintenance of turbofan engines, as it helps reduce unexpected failures and maintenance costs while improving system reliability. The objective of this study is to develop an accurate and robust deep learning framework for RUL prediction using multivariate time-series sensor data and to investigate the influence of different optimization algorithms on prediction performance. A hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model is proposed, where convolutional layers extract local degradation features and LSTM layers learn the long-term temporal relationships present in engine degradation data. A sliding-window strategy together with early RUL capping is employed to improve training stability and model generalization. The proposed model is evaluated on all four subsets (FD001–FD004) of the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset and achieves RMSE values of 12.79, 21.94, 15.09, and 33.71, respectively. Among the evaluated subsets, the model delivers the best performance on FD001 and outperforms several existing deep learning approaches reported in the literature. In addition, five optimization algorithms are systematically compared under the same experimental settings. The results show that Stochastic Gradient Descent (SGD) provides the best convergence behaviour and prediction accuracy for the proposed architecture. Overall, the study demonstrates that combining CNN–LSTM with an appropriate optimization strategy improves the reliability and accuracy of RUL prediction for turbofan engines.
Rajneesh Kumar, Shivam Ojha, Amit Shelke et al.· Scientific Reports· 0 citations
Accurate prediction of Remaining Useful Life (RUL) is essential for predictive maintenance in the aerospace industry, where unexpected failures pose significant safety risks and increase operational costs. Conventional deep learning models, such as Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), have demonstrated strong predictive capabilities; however, they often incur high computational costs, are sensitive to noise, and struggle to capture long-term degradation patterns. To overcome these issues, this study presents a new hybrid deep learning model that combines Temporal Convolutional Networks (TCNs) with Reservoir Computing, leveraging the strengths of both architectures. The model is tested using the well-known NASA C-MAPSS dataset, a standard benchmark for RUL estimation. Performance is measured using both Root Mean Squared Error (RMSE) and a penalty-based PHM score that emphasizes timely failure prediction. The model attains test RMSE values of 14.81, 16.26, 15.57, and 17.97 on FD001, FD002, FD003, and FD004, respectively. Correspondingly, the PHM scores are reduced to 57.1, 204.46, 190.95, and 446.73 across the same subsets. 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. These results suggest that the proposed method provides a practical, real-time solution for predictive maintenance in aeroengine health monitoring, thereby improving reliability and reducing maintenance costs.
Mahika Annie Verghese, C. Columbus, E. Elakiya· Scientific Reports· 0 citations
Remaining useful life (RUL) prediction is essential for lithium-ion battery health management, yet accurate prognosis remains difficult because battery degradation trajectories are strongly non-stationary and often contain mixed-scale fluctuations together with local regeneration-like disturbances. To address this issue, this study proposes a DOA-VMD-CNN-TimeXer framework for RUL-oriented prognosis through capacity trajectory prediction and reconstruction. In the proposed framework, the Dream Optimization Algorithm (DOA) is first used to adaptively optimize the key variational mode decomposition (VMD) parameters so that the measured capacity sequence can be decomposed into modal components with clearer temporal structures. A CNN-enhanced TimeXer predictor is then constructed for component-wise forecasting, where the CNN branch extracts local fluctuation features and the TimeXer branch models long-range temporal dependencies. Finally, the predicted modal components are reconstructed into the capacity trajectory to support threshold-based end-of-life inference. Comparative experiments on four CALCE lithium-ion battery cells under 5:5 and 6:4 train–test splits show that the proposed framework achieves RMSE values ranging from 0.010316 to 0.014528, while all corresponding R2 values remain above 0.994. In addition, the proposed method 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-oriented prognosis under the evaluated CALCE settings.
As the core propulsion system of an aircraft, the safety and reliability of aeroengines are directly related to flight safety and operational efficiency. Modern health monitoring systems increasingly rely on electromagnetic sensing technologies and multi-source signal acquisition to provide reliable condition information under complex operating environments, making accurate remaining useful life prediction essential for predictive maintenance and intelligent decision-making. To improve the prediction accuracy of aeroengine remaining useful life, this paper proposes a hybrid model integrating convolutional neural networks and bidirectional long short-term memory networks. The convolutional neural network is first employed to automatically extract spatial features from multi-dimensional sensor data, after which the bidirectional long short-term memory network captures temporal dependencies and degradation trends throughout the engine operating process. The proposed framework enables end-to-end mapping from multi-state monitoring parameters to remaining useful life without requiring explicit degradation modeling or manual feature engineering. Validation on the CMAPSS benchmark dataset demonstrates that 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. The proposed method not only enhances predictive maintenance capability for intelligent propulsion systems but also offers valuable methodological support for multi-sensor information fusion and electromagnetic-enabled condition monitoring in advanced aerospace applications.
Q. Zhang, X. J. Yang, S. H. Zhu et al.· Advanced Electromagnetics· 0 citations