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.
: The operation of complex equipment is typically monitored by multiple sensors, and the vast amount of status data generated from this monitoring provides strong support for predicting the remaining useful life (RUL). Due to the influence of unstable operational conditions, the degradation trajectory of the equipment often exhibits a high degree of nonlinearity. Conventional approaches for processing univariate time series data often struggle to effectively identify inherent degradation trends and unstable fluctuations, while exhibiting limited capability in comprehensive modeling of multi-source time series data. This paper proposes a novel spatiotemporal neural network for RUL prediction. Firstly, a temporal decomposition block (TDB) is utilized to decompose the multi-source time series into trend and unstable components. Subsequently, temporal dependency features are extracted using gated recurrent units (GRU), and the Koopman operator is employed to linearly model these features in a high-dimensional space. A channel interaction learning block (CILB) is applied to capture dependencies between sensors and enhance feature representation capabilities. Finally, the prediction module utilizes the linear layer of residual structure to generate the final RUL prediction result, and a method combining ensemble learning and kernel density estimation (KDE) is used to obtain the probability density function of the RUL. The experimental results based on the C-MAPSS dataset show that the prediction accuracy of this method is superior to other existing methods, especially exhibiting better performance under complex
Xinjian Gao, Enzhi Dong, Zhonghua Cheng et al.· Computers, Materials & C...· 0 citations
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.
Xudong Song, Guohua Wu, Meng-Dan Wang et al.· Machines· 0 citations
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.
Dan Xu, Xinyu Qian, Yang Zhou et al.· International Journal of Mac...· 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, Amit Shelke et al.· Scientific Reports· 0 citations
Remaining Useful Life (RUL) prediction is critical for predictive maintenance in safety-critical systems such as aerospace engines. While deep learning models achieve high predictive accuracy, they often lack interpretability and reliable uncertainty estimation. This paper proposes the Hybrid Deep Evidential Clustering (HDEC) framework to address both challenges. A CNN–LSTM–GRU backbone first extracts degradation features from multivariate time-series data. These features are then clustered using NN-EVCLUS, an evidential clustering approach based on Dempster–Shafer theory, which groups engines according to their degradation stage. A dedicated RUL predictor is trained for each cluster to enable specialized and interpretable predictions. Engines with uncertain cluster membership are handled through soft memberships, allowing RUL estimation as a weighted combination of cluster-specific predictors instead of hard assignment. Experiments on the NASA C-MAPSS dataset demonstrate that HDEC improves predictive performance while providing well-calibrated uncertainty estimates and interpretable degradation-regime assignments.
Mohamed Ali Ben Azzouna, S. Ben Ayed, Lilia Rejeb· International Conference on...· 0 citations
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.
Q. Zhang, X. J. Yang, S. H. Zhu et al.· Advanced Electromagnetics· 0 citations
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