| Citation: | GUO Xiaojing, XU Xiaohui, GUO Jiahao. Improved GRU-based self-attention optimization algorithm for aero-engine remaining useful life prediction[J]. Journal of Aerospace Power, 2024, 39（12）：20220984 doi: 10.13224/j.cnki.jasp.20220984
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Multivariate, high-dimensional and time-ordered aero-engine performance parameters can characterize life regressions, which are prone to gradient disappearance using conventional model training. A self-attention optimization algorithm was proposed to improve the gated recurrent units (GRU). Row gradients of source domain and inter-column correlations were analyzed. The feature weights were optimized by augmenting the strongly correlated lifetime columns, with its aim to accelerate model convergence and improve prediction accuracy. Experiments on the engine life prediction dataset (C-MAPSS) showed that the root mean square error (RMSE) of life obtained by the algorithm fell in the interval [10.52, 18.91] and the over-prediction index (score) in the interval [48.69, 204.98]. Compared with the traditional method, the effect of life prediction was greatly reduced, and an effective solution was provided for engine life prediction and advanced maintenance.
| [1] |
CHEN Zhenghua,WU Min,ZHAO Rui,et al. Machine remaining useful life prediction via an attention-based deep learning approach[J]. IEEE Transactions on Industrial Electronics,2021,68(3): 2521-2531. doi: 10.1109/TIE.2020.2972443
|
| [2] |
梁伟阁,张钢,王健,等.复杂机械设备健康状态预测方法研究综述[J].兵器装备工程学报,2022,43(7):67-77. LIANG Weige,ZHANG Gang,WANG Jian,et al. A review on health state assessment and remaining useful life prediction of mechanical equipment under intelligent manufacturing[J]. Journal of Ordnance Equipment Engineering,2022,43(7): 67-77. (in Chinese
LIANG Weige, ZHANG Gang, WANG Jian, et al. A review on health state assessment and remaining useful life prediction of mechanical equipment under intelligent manufacturing[J]. Journal of Ordnance Equipment Engineering, 2022, 43(7): 67-77. (in Chinese)
|
| [3] |
LIAO Guobo,YIN Hongpeng,CHEN Min,et al. Remaining useful life prediction for multi-phase deteriorating process based on Wiener process[J]. Reliability Engineering and System Safety,2021,207: 107361. doi: 10.1016/j.ress.2020.107361
|
| [4] |
LIU He,SONG Wanqing,NIU Yuhui,et al. A generalized cauchy method for remaining useful life prediction of wind turbine gearboxes[J]. Mechanical Systems and Signal Processing,2021,153: 107471. doi: 10.1016/j.ymssp.2020.107471
|
| [5] |
裴洪,胡昌华,司小胜,等.基于机器学习的设备剩余寿命预测方法综述[J].机械工程学报,2019,55(8):1-13. PEI Hong,HU Changhua,SI Xiaosheng,et al. Review of machine learning based remaining useful life prediction methods for equipment[J]. Journal of Mechanical Engineering,2019,55(8): 1-13. (in Chinese doi: 10.3901/JME.2019.08.001
PEI Hong, HU Changhua, SI Xiaosheng, et al. Review of machine learning based remaining useful life prediction methods for equipment[J]. Journal of Mechanical Engineering, 2019, 55(8): 1-13. (in Chinese) doi: 10.3901/JME.2019.08.001
|
| [6] |
黄亮,刘君强,贡英杰.基于Wiener过程的发动机多阶段剩余寿命预测[J].北京航空航天大学学报,2018,44(5):1081-1087. HUANG Liang,LIU Junqiang,GONG Yingjie. Multi-phase residual life prediction of engines based on Wiener process[J]. Journal of Beijing University of Aeronautics and Astronautics,2018,44(5): 1081-1087. (in Chinese
HUANG Liang, LIU Junqiang, GONG Yingjie. Multi-phase residual life prediction of engines based on Wiener process[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(5): 1081-1087. (in Chinese)
|
| [7] |
DU Jingcai,ZHANG Weige,ZHANG Caiping,et al. Battery remaining useful life prediction under coupling stress based on support vector regression[J]. Energy Procedia,2018,152: 538-543. doi: 10.1016/j.egypro.2018.09.207
|
| [8] |
ORDÓÑEZ C,LASHERAS F S,ROCA-PARDIÑAS J,et al. A hybrid ARIMA–SVM model for the study of the remaining useful life of aircraft engines[J]. Journal of Computational and Applied Mathematics,2019,346: 184-191. doi: 10.1016/j.cam.2018.07.008
|
| [9] |
KHAN S,YAIRI T. A review on the application of deep learning in system health management[J]. Mechanical Systems and Signal Processing,2018,107: 241-265. doi: 10.1016/j.ymssp.2017.11.024
|
| [10] |
BEJAOUI I,BRUNEO D,XIBILIA M G. Remaining useful life prediction of broken rotor bar based on data-driven and degradation model[J]. Applied Sciences,2021,11(16): 7175. doi: 10.3390/app11167175
|
| [11] |
HOCHREITER S,SCHMIDHUBER J. Long short-term memory[J]. Neural Computation,1997,9(8): 1735-1780. doi: 10.1162/neco.1997.9.8.1735
|
| [12] |
CHUNG J,GULCEHRE C,CHO K H,et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[EB/OL]. (2014-12-11) [2022-11-02]. https://doi.org/10.48550/arXiv.1412.3555.
|
| [13] |
ZHANG Xinyun,DONG Yan,WEN Long,et al. Remaining useful life estimation based on a new convolutional and recurrent neural network[C]//Proceedings of IEEE 15th International Conference on Automation Science and Engineering. Piscataway,US: IEEE,2019: 317-322.
|
| [14] |
LI Li,ZHAO Zhen,ZHAO Xiaoxiao,et al. Gated recurrent unit networks for remaining useful life prediction[J]. IFAC-PapersOnLine,2020,53(2): 10498-10504. doi: 10.1016/j.ifacol.2020.12.2795
|
| [15] |
陈保家,郭凯敏,陈法法,等.基于残差NLSTM和注意力机制的航空发动机剩余寿命预测[J].航空动力学报,2023,38(5):1176-1184. CHEN Baojia,GUO Kaimin,CHEN Fafa,et al. Residual life prediction of aero engines based on residual NLSTM and attention mechanism[J]. Journal of Aerospace Power,2023,38(5): 1176-1184. (in Chinese
CHEN Baojia, GUO Kaimin, CHEN Fafa, et al. Residual life prediction of aero engines based on residual NLSTM and attention mechanism[J]. Journal of Aerospace Power, 2023, 38(5): 1176-1184. (in Chinese)
|
| [16] |
SAXENA A,GOEBEL K,SIMON D,et al. Damage propagation modeling for aircraft engine run-to-failure simulation[C]// Proceedings of 2008 International Conference on Prognostics and Health Management. Piscataway,US: IEEE,2008: 1-9.
|
| [17] |
XIA Jun,FENG Yunwen,LU Cheng,et al. LSTM-based multi-layer self-attention method for remaining useful life estimation of mechanical systems[J]. Engineering Failure Analysis,2021,125: 105385. doi: 10.1016/j.engfailanal.2021.105385
|
| [18] |
郭晓静,殷宇萱,贠玉晶.基于改进LSTM的航空发动机寿命预测方法研究[J].机床与液压,2022,50(20):185-193. GUO Xiaojing,YIN Yuxuan,YUN Yujing. Aeroengine life prediction method based on improved LSTM[J]. Machine Tool and Hydraulics,2022,50(20): 185-193. (in Chinese
GUO Xiaojing, YIN Yuxuan, YUN Yujing. Aeroengine life prediction method based on improved LSTM[J]. Machine Tool and Hydraulics, 2022, 50(20): 185-193. (in Chinese)
|
| [19] |
许昱晖,舒俊清,宋亚,等.基于多时间尺度相似性的涡扇发动机寿命预测[J].浙江大学学报(工学版),2021,55(10):1937-1947. XU Yuhui,SHU Junqing,SONG Ya,et al. Remaining useful life prediction of turbofan engine based on similarity in multiple time scales[J]. Journal of Zhejiang University (Engineering Science),2021,55(10): 1937-1947. (in Chinese
XU Yuhui, SHU Junqing, SONG Ya, et al. Remaining useful life prediction of turbofan engine based on similarity in multiple time scales[J]. Journal of Zhejiang University (Engineering Science), 2021, 55(10): 1937-1947. (in Chinese)
|
| [20] |
HEIMES F O. Recurrent neural networks for remaining useful life estimation[C]//Proceedings of 2008 International Conference on Prognostics and Health Management. Piscataway,US: IEEE,2008: 59-65.
|
| [21] |
YU Wennian,KIM I Y,MECHEFSKE C. An improved similarity-based prognostic algorithm for RUL estimation using an RNN autoencoder scheme[J]. Reliability Engineering and System Safety,2020,199: 106926. doi: 10.1016/j.ress.2020.106926
|
| [22] |
MO H,LUCCA F,MALACARNE J,et al. Multi-head CNN-LSTM with prediction error analysis for remaining useful life prediction[C]//Proceedings of the 27th Conference of Open Innovations Association. Piscataway,US: IEEE,2020: 164-171.
|
| [23] |
刘丽,裴行智,雷雪梅.基于时间卷积注意力网络的剩余寿命预测方法[J].计算机集成制造系统,2022,28(8):2375-2386. LIU Li,PEI Xingzhi,LEI Xuemei. Temporal convolutional attention network for remaining useful life estimation[J]. Computer Integrated Manufacturing Systems,2022,28(8): 2375-2386. (in Chinese
LIU Li, PEI Xingzhi, LEI Xuemei. Temporal convolutional attention network for remaining useful life estimation[J]. Computer Integrated Manufacturing Systems, 2022, 28(8): 2375-2386. (in Chinese)
|
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