Remaining Useful Life Prediction for Turbofan Engines Based on Deep Learning Insights from the CMAPSS Dataset

2026-08-05T22:17:16+02:00

Predicting the Remaining Useful Life (RUL) of turbofan engines is a vital component in the field of prognostics and health management (PHM). Accurate estimation of RUL enables timely maintenance and prevents sudden failures, which is essential for ensuring safety and reducing operational costs. This study introduces a novel hybrid deep learning model referred to as RCBLA (Residual Convolutional Bidirectional Long Short-Term Memory with Attention), designed specifically to handle the temporal and sequential characteristics of engine sensor data. The model architecture combines residual convolutional layers to enhance feature extraction, Temporal Convolutional Networks (TCN) for capturing long-range dependencies, Bidirectional LSTM layers for understanding both past and future contexts, and an attention mechanism to focus on the most critical time steps. Layer normalization is applied to improve convergence and training stability. The proposed model is evaluated using the FD001 and FD003 subsets of the C-MAPSS dataset. A piecewise linear degradation model is adopted, and only sensor measurements are used as input features. A custom learning rate scheduler is applied to optimize model convergence, and a dropout rate of 0.7 and 0.5 is used to prevent overfitting. The results demonstrate the model’s strong predictive capabilities, achieving RMSE values of 14.389 and 12.46 for FD001 and FD003, respectively. The scoring function yields 279.23 and 282.56 for FD001 and FD003, respectively. These outcomes highlight the model’s robustness and suitability for real-world RUL prediction, offering a reliable framework for improving PHM systems in aerospace applications.

Progressive Tree Expansion and Vertex-Based Pruning in UCT Strategies for the Stochastic Canadian Traveller Problem

2026-08-05T22:34:38+02:00

The Stochastic Canadian Traveller Problem (SCTP) models adaptive routing on a known graph in which the availability of an edge is revealed only after the traveller reaches an incident vertex, while edge-specific blocking probabilities are known in advance. This paper investigates simulation-based strategies for minimizing the expected travel cost. After summarizing the Optimistic Policy, Hindsight Optimization, Optimistic Rollout, and established UCT-based approaches, the paper formalizes and evaluates two original heuristic variants of Optimistic UCT, UCTO2 and UCTP, which had previously been proposed and implemented in a master's thesis. UCTO2 replaces complete depth-oriented rollout trajectories with progressive tree expansion and batch rollout evaluation of selected nodes. UCTP augments UCTO2 with a pruning mechanism that retains the more promising of alternative tree nodes associated with the same graph vertex. The methods are evaluated on two families of randomly generated graphs with 50 vertices, using ten graphs per family and 500 stochastic realizations per graph. In the reported experiments, UCTP achieved lower mean travel costs than the Optimistic Policy and UCTO while requiring substantially less computation time than the other evaluated UCT variants. HOP and ORO nevertheless attained the lowest overall mean costs. The evidence therefore supports the complete UCTP configuration as a favourable finite-budget alternative within the UCT family, not as a generally superior SCTP policy.

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Remaining Useful Life Prediction for Turbofan Engines Based on Deep Learning Insights from the CMAPSS Dataset

Progressive Tree Expansion and Vertex-Based Pruning in UCT Strategies for the Stochastic Canadian Traveller Problem