| Citation: | ZHAO H L，BAI L D. Remaining life prediction of engine by improved similarity with interval partition[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（10）：3005-3012 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0762
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The traditional similarity matching method is liable to introduce pseudo-similar engines, which leads to low prediction accuracy. To address this issue, a remaining useful life (RUL) prediction method of improved similarity with interval partition was proposed, which combined the engine performance degradation characteristics and similarity matching characteristics. Firstly, the health index was constructed based on the selected parameters by using the stacked autoencoder. Then, based on the known running cycles of the test engine, the interval was divided, and the traditional similarity matching method was used for preliminary screening. For the test engines in different intervals, the uncertainty correction and degradation consistency test were performed to remove the abnormal engine from the preliminarily selected reference engines, and the final remaining life prediction results were obtained. The C-MAPSS dataset of NASA was used for verification, and the results show that the prediction accuracy is 34% higher than the current similarity matching method, which proves the effectiveness of the proposed method.
| [1] |
中国民用航空局. 中国民用航空发展第十三个五年规划[EB/OL]. (2017-02-16)[2022-08-18].
Civil Aviation Administration of China. The Thirteenth Five-Year Plan for the development of China’s civil aviation[EB/OL]. (2017-02-16)[2022-08-18].
|
| [2] |
尉询楷, 杨立, 刘芳, 等. 航空发动机预测与健康管理[M]. 北京: 国防工业出版社, 2014: 132-133.
WEI X K, YANG L, LIU F, et al. Aero-engine prediction and health management[M]. Beijing: National Defense Industry Press, 2014: 132-133(in Chinese).
|
| [3] |
ELSHEIKH A, YACOUT S, OUALI M S. Bidirectional handshaking LSTM for remaining useful life prediction[J]. Neurocomputing, 2019, 323: 148-156. doi: 10.1016/j.neucom.2018.09.076
|
| [4] |
周俊. 数据驱动的航空发动机剩余使用寿命预测方法研究[D]. 南京: 南京亚洲成人在线一二三四五六区, 2017.
ZHOU J. Research on data-driven prediction methods for remaining useful life of aero-engine[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2017(in Chinese).
|
| [5] |
PECHT M. Prognostics and health management of electronics[M]. New York: Wiley, 2008.
|
| [6] |
CHEN X, JIN G, QIU S Q, et al. Direct remaining useful life estimation based on random forest regression[C]//Proceedings of the Global Reliability and Prognostics and Health Management. Piscataway: IEEE Press, 2020: 1-7.
|
| [7] |
车畅畅, 王华伟, 倪晓梅, 等. 基于1D-CNN和Bi-LSTM的航空发动机剩余寿命预测[J]. 机械工程学报, 2021, 57(14): 304-312.
CHE C C, WANG H W, NI X M, et al. Residual life prediction of aeroengine based on 1D-CNN and Bi-LSTM[J]. Journal of Mechanical Engineering, 2021, 57(14): 304-312(in Chinese).
|
| [8] |
郭庆, 李印龙, 郑天翔. 基于非线性 Wiener 过程航空发动机性能退化预测[J]. 推进技术, 2021, 42(9): 1956-1963.
GUO Q, LI Y L, ZHENG T X. Performance degradation prediction of aero-engine based on nonlinear Wiener process[J]. Journal of Propulsion Technology, 2021, 42(9): 1956-1963(in Chinese).
|
| [9] |
ZHANG H, ZHANG Q, SHAO S Y, et al. Attention-based LSTM network for rotatory machine remaining useful life prediction[J]. IEEE Access, 2020, 8: 132188-132199. doi: 10.1109/ACCESS.2020.3010066
|
| [10] |
WANT T. Trajectory similarity based prediction for remaining useful life estimation [D]. Cincinnati: University of Cincinnati, 2010.
|
| [11] |
张妍, 王村松, 陆宁云, 等. 基于退化特征相似性的航空发动机寿命预测[J]. 系统工程与电子技术, 2019, 41(6): 1414-1421. doi: 10.3969/j.issn.1001506X.2019.06.32
ZHANG Y, WANG C S, LU N Y, et al. Remaining useful life prediction for aero-engine based on the similarity of degradation characteristics[J]. Systems Engineering and Electronics, 2019, 41(6): 1414-1421(in Chinese). doi: 10.3969/j.issn.1001506X.2019.06.32
|
| [12] |
CAI H S, JIA X D, FENG J S, et al. A similarity based methodology for machine prognostics by using kernel two sample test[J]. ISA Transactions, 2020, 103: 112-121. doi: 10.1016/j.isatra.2020.03.007
|
| [13] |
YU W N, KIM I Y, MECHEFSKE C. An improved similarity-based prognostic algorithm for RUL estimation using an RNN autoencoder scheme[J]. Reliability Engineering & System Safety, 2020, 199: 106926.
|
| [14] |
XUE B, XU F M, HUANG X, et al. Improved similarity based prognostics method for turbine engine degradation with degradation consistency test[J]. Applied Intelligence, 2022, 52(9): 10181-10201. doi: 10.1007/s10489-021-03034-6
|
| [15] |
TKACZ E, KOZANECKA D, KOZAN-ECKI Z. Investigations of oil-free support systems to improve the reliability of ORC hermetic high-speed turbomachinery[J]. Mechanics and Mechanical Engineering, 2011, 15(3): 355-365.
|
| [16] |
王昆, 郭迎清, 赵万里, 等. 基于SSAE和相似性匹配的航空发动机剩余寿命预测[J]. 北京亚洲成人在线一二三四五六区学报, 2023, 49(10): 2817-2825.
WANG K, GUO Y Q, ZHAO W L, et al. Remaining useful life prediction of aeroengine based on SSAE and similarity matching[J]. Journal of Beijing University of Aeronautics and Astronautics, 2023, 49(10): 2817-2825(in Chinese).
|
| [17] |
李浩, 王卓健, 李哲, 等. 基于堆栈自编码器和DeepAR的航空发动机剩余寿命预测[J]. 推进技术, 2022, 43(11): 72-80.
LI H, WANG Z J, LI Z, et al. Prediction of remaining useful life of aero-engine based on stacked autoencoder and DeepAR[J]. Journal of Propulsion Technology, 2022, 43(11): 72-80(in Chinese).
|
| [18] |
KHELIF R, MALINOWSKI S, CHEBEL-MORELLO B, et al. RUL prediction based on a new similarity-instance based approach[C]//Proceedings of the IEEE 23rd International Symposium on Industrial Electronics. Piscataway: IEEE Press, 2014: 2463-2468.
|
| [19] |
RAMASSO E. Investigating computational geometry for failure prognostics in presence of imprecise health indicator: results and comparisons on C-MAPSS datasets[C]//Proceedings of the 2nd European Conference of the Prognostics and Health Management Society. Piscataway: IEEE Press, 2014: 1-13.
|
| [20] |
MALHOTRA P, TV V, RAMAKRISHNAN A, et al. Multi-sensor prognostics using an unsupervised health index based on LSTM encoder-decoder[EB/OL]. (2016-08-22)[2022-08-18].
|
| [21] |
SAXENA A, GOEBEL K, SIMON D, et al. Damage propagation modeling for aircraft engine run-to-failure simulation[C]//Proceedings of the International Conference on Prognostics and Health Management. Piscataway: IEEE Press, 2008: 1-9.
|
| [22] |
李杰, 贾渊杰, 张志新, 等. 基于融合神经网络的航空发动机剩余寿命预测[J]. 推进技术, 2021, 42(8): 1725-1734.
LI J, JIA Y J, ZHANG Z X, et al. Remaining useful life prediction of aeroengine based on fusion neural network[J]. Journal of Propulsion Technology, 2021, 42(8): 1725-1734(in Chinese).
|
| [23] |
SUN W J, ZHAO R, YAN R Q, et al. Convolutional discriminative feature learning for induction motor fault diagnosis[J]. IEEE Transactions on Industrial Informatics, 2017, 13(3): 1350-1359. doi: 10.1109/TII.2017.2672988
|
| [24] |
WU J, HU K, CHENG Y W, et al. Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network[J]. ISA Transactions, 2020, 97: 241-250. doi: 10.1016/j.isatra.2019.07.004
|
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