The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Joint Learning of Degradation Assessment and RUL Prediction for Aeroengines via Dual-Task Deep LSTM Networks
Abstract
Health assessment and prognostics are two key tasks within the prognostics and health management frame of equipment. However, existing works are performing these two tasks separately and hierarchically. In this paper, we design and establish dual-task deep long short-term memory networks for joint learning of degradation assessment and remaining useful life prediction of aeroengines. This enables a more robust and accurate assessment and prediction results making for the increment of operational reliability and safety as well as maintenance cost reduction. Meanwhile, the target label functions that match the network training are constructed in an adaptive way according to the health state of an individual aeroengine. Experiments on the popular C-MAPSS lifetime dataset of aeroengines are employed to verify the accuracy and effectiveness. The performance of our proposed work exhibits superiority over other state-of-the-art approaches and demonstrate its application potential.
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Cited by
Health status assessment and remaining useful life prediction of aero-engine based on BiGRU and MMoE
Zhang, Y., Xin, Y., Liu, Z.-W., Chi, M., & Ma, G. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.108263 · Cited by 268
Temporal and Heterogeneous Graph Neural Network for Remaining Useful Life Prediction
Wen, Z., Fang, Y., Wei, P., Liu, F., Chen, Z., & Wu, M. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3592788 · Cited by 34
Ai, S., Song, J., & Cai, G. (2022) · Mathematics · doi:10.3390/math10101733 · Cited by 11
Spatio-Temporal Attention Graph Neural Network for Remaining Useful Life Prediction
Huang, Z., He, Y., & Sick, B. (2023) · 2023 International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci62032.2023.00022 · Cited by 10
Metadata
- Authors
- Miao, H., Li, B., Sun, C., & Liu, J.
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2019-09
- Volume
- 15
- Issue
- 9
- Pages
- 5023-5032
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2917169831
- Cited by
- 293
- References
- 32
- FWCI
- 16.51
- Type
- journal article
- Language
- en
- Open access
- closed
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- Miao, H., Li, B., Sun, C., & Liu, J. (2019). Joint Learning of Degradation Assessment and RUL Prediction for Aeroengines via Dual-Task Deep LSTM Networks. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/tii.2019.2900295
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