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.
Abstract
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.
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
To improve the operational readiness of combat aircraft engines and reduce unplanned maintenance costs, accurately estimating the remaining useful life (RUL) is critical. Traditional maintenance often proves insufficient under dynamic mission profiles. In this study, a deep learning-based predictive maintenance model capable of autonomously extracting features from multivariate sensor data was developed. Using the NASA C-MAPSS FD001 and FD004 datasets, data were converted into sequential blocks via 50- and 30-step sliding windows, respectively. The model's architectural superiority in autonomously extracting temporal degradation features was validated against RF, CNN-LSTM, and BiLSTM baselines. On FD001, it achieved an R-squared (R2) of 0.8901, a 13.28 RMSE, and a 320.34 NASA risk score, demonstrating generalizability on the multi-regime FD004 dataset with a 15.71 RMSE. The proposed maintenance protocol achieved a 0.9973 AUC at the critical 30-cycle threshold, ensuring high reliability. Additionally, a decision-support simulator has been developed to validate this protocol under aggressive combat flight profiles.
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
Shipborne pump systems play a crucial role in transportation, drainage, and auxiliary operations, directly supporting the safe operation of ships and the daily activities of crew members. Accurate prediction of the remaining useful life (RUL) of such systems is therefore essential for early fault awareness, maintenance planning, and mission assurance. Although RUL prediction has been widely investigated for components such as rolling bearings and aircraft engines, effective prediction approaches specifically developed for marine mechanical pump systems are still lacking. To fill this research gap, this study proposes a time–frequency Transformer–convolutional neural network (CNN) gated (TFTCG) model for pump RUL prediction. Vibration signals are characterized by complex temporal structures, exhibiting both short-term transient behaviors and long-term degradation trends. To capture these properties, CNNs are employed to extract local and detailed features, while a transformer-based architecture is introduced to model global dependencies and degradation patterns throughout the entire lifecycle. In addition, a gating mechanism is incorporated to adaptively fuse time-domain and frequency-domain representations, enabling effective integration of local anomalies and global degradation trends and further improving prediction performance. The proposed model jointly addresses fault diagnosis and RUL prediction, allowing it to simultaneously determine the current degradation stage of the pump and predict its future degradation trajectory from real-time input data. Comparative experiments conducted on the C-MAPSS dataset demonstrate the superior performance of the TFTCG model, which achieves a lower root mean square error of 9.76 and a Prognostics and Health Management (PHM) score of 124 compared with existing approaches. The effectiveness and generalization capability of the proposed method are further validated using a simulated marine mechanical pump dataset with an RMSE of 4.87 and a PHM score of 11.21.
Wenpu Wang, Jiqiu Li, Yuqing Li et al.· Structural Health Monitoring· 0 citations
Unplanned equipment failure remains one of the costliest problems in modern manufacturing, driving research toward Predictive Maintenance (PdM) strategies that anticipate failures before they occur using real-time sensor data. This paper proposes a hybrid deep learning framework combining Convolutional Neural Networks (CNN) for local spatial feature extraction and Bidirectional Long Short-Term Memory (BiLSTM) networks with a temporal attention mechanism for capturing long-range degradation trends in multivariate time-series sensor data. The framework is designed to estimate Remaining Useful Life (RUL) and detect early fault signatures in Industry 4.0
environments equipped with IoT-based condition-monitoring sensors. We formulate the problem as a windowed sequence-to-value regression task, describe a sliding-window feature pipeline, and benchmark the framework's data
pipeline against classical machine learning regressors (Random Forest, Gradient Boosting, Support Vector
Regression, and a Multi-Layer Perceptron) on a run-to-failure sensor dataset. Experimental results show that
ensemble tree-based models achieve strong baseline performance (RMSE as low as 9.96 cycles, R2 = 0.946), establishing a validated pipeline and baseline for the full CNN-BiLSTM-Attention model. The proposed architecture, evaluation protocol, and ablation plan are presented in full so the framework can be reproduced and extended on industrial-scale datasets such as NASA C-MAPSS or live plant telemetry.
Renuka Surendra Deshpande· International Journal of All...· 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