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TY - JOUR AU - Zhou, Xin AU - Yang, Lechang PY - 2025 DA - 2025/11/12 TI - Remaining Useful Life Prediction Using Optimized Multi-source Features and Model Fusion JO - ICCK Transactions on Systems Safety and Reliability T2 - ICCK Transactions on Systems Safety and Reliability JF - ICCK Transactions on Systems Safety and Reliability VL - 1 IS - 2 SP - 114 EP - 127 DO - 10.62762/TSSR.2025.167369 UR - https://www.icck.org/article/abs/TSSR.2025.167369 KW - remaining useful life prediction KW - multi-branch fusion model KW - aviation engine KW - lithium battery AB - Remaining Useful Life (RUL) prediction is critical for ensuring equipment safety and optimizing maintenance schedules, directly impacting system reliability and maintenance efficiency. However, in real-world industrial scenarios, factors such as operating condition fluctuations and load variations lead to inconsistent data distributions, making it challenging for existing models to achieve satisfactory adaptability and accuracy. To address this issue, this paper proposes a deep learning framework based on a multi-branch serial-parallel fusion of CNN-BiLSTM-Transformer architectures. Through innovative model architecture design and optimized training strategies, the framework aims to enhance prediction accuracy and robustness under cross-condition and cross-distribution scenarios. Specifically, a training strategy combining snapshot ensemble and cosine annealing learning rate scheduling is introduced to improve model generalization through multi-model ensemble prediction. Experiments conducted on the C-MAPSS aero-engine dataset and the A123 lithium battery dataset validate the effectiveness of the proposed method. The results show that on the FD004 dataset, the proposed model reduces RMSE by 7.61% and Score by 26.39% compared to the best baseline methods; on the main test set of the A123 battery dataset, RMSE is reduced by 16.18%. Overall, the proposed model outperforms traditional machine learning models under both single and complex operating conditions, and its prediction results are comparable to those of mainstream models, demonstrating the feasibility and effectiveness of the proposed method in improving RUL prediction accuracy under cross-condition and cross-distribution scenarios. SN - 3069-1087 PB - Institute of Central Computation and Knowledge LA - English ER -
@article{Zhou2025Remaining,
author = {Xin Zhou and Lechang Yang},
title = {Remaining Useful Life Prediction Using Optimized Multi-source Features and Model Fusion},
journal = {ICCK Transactions on Systems Safety and Reliability},
year = {2025},
volume = {1},
number = {2},
pages = {114-127},
doi = {10.62762/TSSR.2025.167369},
url = {https://www.icck.org/article/abs/TSSR.2025.167369},
abstract = {Remaining Useful Life (RUL) prediction is critical for ensuring equipment safety and optimizing maintenance schedules, directly impacting system reliability and maintenance efficiency. However, in real-world industrial scenarios, factors such as operating condition fluctuations and load variations lead to inconsistent data distributions, making it challenging for existing models to achieve satisfactory adaptability and accuracy. To address this issue, this paper proposes a deep learning framework based on a multi-branch serial-parallel fusion of CNN-BiLSTM-Transformer architectures. Through innovative model architecture design and optimized training strategies, the framework aims to enhance prediction accuracy and robustness under cross-condition and cross-distribution scenarios. Specifically, a training strategy combining snapshot ensemble and cosine annealing learning rate scheduling is introduced to improve model generalization through multi-model ensemble prediction. Experiments conducted on the C-MAPSS aero-engine dataset and the A123 lithium battery dataset validate the effectiveness of the proposed method. The results show that on the FD004 dataset, the proposed model reduces RMSE by 7.61\% and Score by 26.39\% compared to the best baseline methods; on the main test set of the A123 battery dataset, RMSE is reduced by 16.18\%. Overall, the proposed model outperforms traditional machine learning models under both single and complex operating conditions, and its prediction results are comparable to those of mainstream models, demonstrating the feasibility and effectiveness of the proposed method in improving RUL prediction accuracy under cross-condition and cross-distribution scenarios.},
keywords = {remaining useful life prediction, multi-branch fusion model, aviation engine, lithium battery},
issn = {3069-1087},
publisher = {Institute of Central Computation and Knowledge}
}
ICCK Transactions on Systems Safety and Reliability
ISSN: 3069-1087 (Online)
Email: [email protected]
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