[1] 曾声奎, Michael G.Pecht,吴际.故障预测与健康管理(PHM)技术的现状与发展[J].航空学报, 2005, 26(5):626-632. ZENG S K, PECHT M, WU J. Status and perspectives of prognostics and health management technologies[J]. Acta Aeronautica et Astronautica Sinica, 2005, 26(5):626-632(in Chinese). [2] YE Z S, XIE M. Stochastic modelling and analysis of degradation for highly reliable products[J]. Applied Stochastic Models in Business and Industry, 2015, 31(1):16-32. [3] 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:南京航空航天大学, 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). [4] 任子强,司小胜,胡昌华,等.融合多传感器数据的发动机剩余寿命预测方法[J].航空学报, 2019, 40(12):223312. REN Z Q, SI X S, HU C H, et al. Remaining useful life prediction method for engine combining multi-sensors data[J]. Acta Aeronautica et Astronautica Sinica, 2019, 40(12):223312(in Chinese). [5] GARCÍA NIETO P J, GARCÍA-GONZALO E, LASHERAS F S, et al. Hybrid PSO-SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability[J]. Reliability Engineering&System Safety, 2015, 138:219-231. [6] 彭鸿博,蒋雄伟.基于相关向量机的发动机剩余寿命预测[J].科学技术与工程, 2020, 20(18):7538-7544. PENG H B, JIANG X W. Remaining useful life prediction for aeroengine based on relevance vector machine[J]. Science Technology and Engineering, 2020, 20(18):7538-7544(in Chinese). [7] 李京峰,陈云翔,项华春,等.基于LSTM-DBN的航空发动机剩余寿命预测[J].系统工程与电子技术, 2020, 42(7):1637-1644. LI J F, CHEN Y X, XIANG H C, et al. Remaining useful life prediction for aircraft engine based on LSTM-DBN[J]. Systems Engineering and Electronics, 2020, 42(7):1637-1644(in Chinese). [8] LI X, DING Q, SUN J Q. Remaining useful life estimation in prognostics using deep convolution neural networks[J]. Reliability Engineering&System Safety, 2018, 172:1-11. [9] ZHANG Y Z, XIONG R, HE H W, et al. Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries[J]. IEEE Transactions on Vehicular Technology, 2018, 67(7):5695-5705. [10] HU C H, PEI H, SI X S, et al. A prognostic model based on DBN and diffusion process for degrading bearing[J]. IEEE Transactions on Industrial Electronics, 2020, 67(10):8767-8777. [11] 彭开香,皮彦婷,焦瑞华,等.航空发动机的健康指标构建与剩余寿命预测[J].控制理论与应用, 2020, 37(4):713-720. PENG K X, PI Y T, JIAO R H, et al. Health indicator construction and remaining useful life prediction for aircraft engine[J]. Control Theory&Applications, 2020, 37(4):713-720(in Chinese). [12] LISTOU ELLEFSEN A, BJØRLYKHAUG E, SØY V, et al. Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture[J]. Reliability Engineering&System Safety, 2019, 183:240-251. [13] XU Y B, ZHANG J, LONG Z Q, et al. Daily urban water demand forecasting based on chaotic theory and continuous deep belief neural network[J]. Neural Processing Letters, 2019, 50(2):1173-1189. [14] 乔俊飞,潘广源,韩红桂.一种连续型深度信念网的设计与应用[J].自动化学报, 2015, 41(12):2138-2146. QIAO J F, PAN G Y, HAN H G. Design and application of continuous deep belief network[J]. Acta Automatica Sinica, 2015, 41(12):2138-2146(in Chinese). [15] ZHANG J J, WANG P, YAN R Q, et al. Long short-term memory for machine remaining life prediction[J]. Journal of Manufacturing Systems, 2018, 48:78-86. [16] 康守强,周月,王玉静,等.基于改进SAE和双向LSTM的滚动轴承RUL预测方法[J/OL].[2020-05-15] (2021-02-15).自动化学报, https://doi.org/10.16383/j.aas.c190796. KANG S Q, ZHOU Y, WANG Y J, et al. RUL prediction method of a rolling bearing based on improved SAE and Bi-LSTM[J/OL].[2020-05-15] (2021-02-15). Acta Automatica Sinica, https://doi.org/10.16383/j.aas.c190796(in Chinese). [17] 胡昭华,樊鑫,梁德群,等.基于双向非线性学习的轨迹跟踪和识别[J].计算机学报, 2007, 30(8):1389-1397. HU Z H, FAN X, LIANG D Q, et al. Trajectory tracking and recognition using bi-directional nonlinear learning[J]. Chinese Journal of Computers, 2007, 30(8):1389-1397(in Chinese). [18] SHAO H D, JIANG H K, LI X Q, et al. Rolling bearing fault detection using continuous deep belief network with locally linear embedding[J]. Computers in Industry, 2018, 96:27-39. [19] CHEN H, MURRAY A F. Continuous restricted Boltzmann machine with an implementable training algorithm[J]. IEE Proceedings-Vision, Image, and Signal Processing, 2003, 150(3):153-158. [20] CHEN Q L, PAN G Y, QIAO J F, et al. Research on a continuous deep belief network for feature learning of time series prediction[C]//2019 Chinese Control and Decision Conference (CCDC). Piscataway:IEEE Press, 2019:5977-5983. [21] ZHANG B, ZHANG L J, XU J W. Degradation feature selection for remaining useful life prediction of rolling element bearings[J]. Quality and Reliability Engineering International, 2016, 32(2):547-554. [22] LEI Y G, LI N P, GUO L, et al. Machinery health prognostics:A systematic review from data acquisition to RUL prediction[J]. Mechanical Systems and Signal Processing, 2018, 104:799-834. [23] YANG F, HABIBULLAH M S, ZHANG T Y, et al. Health index-based prognostics for remaining useful life predictions in electrical machines[J]. IEEE Transactions on Industrial Electronics, 2016, 63(4):2633-2644. [24] 周月.基于改进SAE和Bi-LSTM的滚动轴承RUL预测方法研究[D].哈尔滨:哈尔滨理工大学, 2020. ZHOU Y. Research on RUL prediction method of a rolling bearing based on improved SAE and Bi-LSTM[D]. Harbin:Harbin University of Science and Technology, 2020(in Chinese). [25] 魏晓良,潮群,陶建峰,等.基于LSTM和CNN的高速柱塞泵故障诊断[J].航空学报, 2021, 42(3):423876. WEI X L, CHAO Q, TAO J F, et al. Cavitation fault diagnosis method for high-speed plunger pumps based on LSTM and CNN[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(3):423876(in Chinese). [26] SONG Y, SHI G, CHEN L Y, et al. Remaining useful life prediction of turbofan engine using hybrid model based on autoencoder and bidirectional long short-term memory[J]. Journal of Shanghai Jiaotong University (Science), 2018, 23(1):85-94. [27] YU W N, KIM I Y, MECHEFSKE C. Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme[J]. Mechanical Systems and Signal Processing, 2019, 129:764-780. [28] PENG W W, YE Z S, CHEN N. Bayesian deep-learning-based health prognostics toward prognostics uncertainty[J]. IEEE Transactions on Industrial Electronics, 2020, 67(3):2283-2293. [29] GAL Y, GHAHRAMANI Z. Dropout as a Bayesian approximation:Representing model uncertainty in deep learning[C]//International Conference on Machine Learning, 2016:1050-1059. [30] 王玺,胡昌华,任子强,等.基于非线性Wiener过程的航空发动机性能衰减建模与剩余寿命预测[J].航空学报, 2020, 41(2):223291. WANG X, HU C H, REN Z Q, et al. Performance degradation modeling and remaining useful life prediction for aero-engine based on nonlinear Wiener process[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(2):223291(in Chinese). [31] 黄亮.基于随机过程的航空发动机剩余寿命预测及维修决策研究[D].南京:南京航空航天大学, 2019. HUANG L. Research on aeroengine remaining life prediction and maintenance decision based on stochastic process[D]. Nanjing:Nanjing University of Aeronautics and Astronautics, 2019(in Chinese). [32] SATEESH BABU G, ZHAO P L, LI X L. Deep convolutional neural network based regression approach for estimation of remaining useful life[C]//Database Systems for Advanced Applications, 2016. [33] ZHENG S, RISTOVSKI K, FARAHAT A, et al. Long short-term memory network for remaining useful life estimation[C]//2017 IEEE International Conference on Prognostics and Health Management. Piscataway:IEEE Press, 2017:88-95. |