| [1]LOHMEYER W Q, ANICETO R J, CAHOY K L.Communication satellite power amplifiers: Current and future SSPA and TWTA technologies[J].International Journal of Satellite Communications and Networking, 2016, 34(2):95-113[2]赵浩天, 邱实, 刘明, 等.基于全生命周期数据驱动的通讯卫星行波管退化评估方法[J].航空学报, 2025, 46(12):331376-331376[3]LIU S S, XIE Q M, CHEN Z F, et al.High linear power E-band traveling-wave tube for communication applications[J].IEEE Transactions on Electron Devices, 2021, 68(6):2984-2989[4]SOCUéLLAMOS J M, DIONISIO R, LETIZIA R, et al.Experimental validation of phase velocity and interaction impedance of meander-line slow-wave structures for space traveling-wave tubes[J].IEEE Transactions on Microwave Theory and Techniques, 2021, 69(4):2148-2154[5]袁利, 王淑一.航天器控制系统智能健康管理技术发展综述[J].航空学报, 2021, 42(4):525044-525044[6]EZHILARASU C M, SKAF Z, JENNIONS I K.The application of reasoning to aerospace Integrated Vehicle Health Management: Challenges and opportunities[J].Progress in Aerospace Sciences, 2019, 105(000):60-73[7]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.[J].Mechanical Systems and Signal Processing, 2018, 107(000):241-265[8]YAIRI T, TAKEISHI N, ODA T, et al.A data-driven health monitoring method for satellite housekeeping data based on probabilistic clustering and dimensionality reduction[J].IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(3):1384-1401[9]何家辉, 程志君, 郭波.联合字典学习与的遥测数据异常检测方法[J].航空学报, 2023, 44(13):327931-327931[10]闫媞锦, 夏元清, 张宏伟, 等.一种非规则采样航空时序数据异常检测方法[J].航空学报, 2021, 42(4):525019-525019[11]HUNDMAN K, CONSTANTINOU V, LAPORTE C, et al.Detecting spacecraft anomalies using LSTMs and nonparametric dynamic thresholding[[J].Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York: ACM, 2018, :387-395[12]YANG L, MA Y, ZENG F, et al.Improved deep learning based telemetry data anomaly detection to enhance spacecraft operation reliability[J].Microelectronics Reliability, 2021, 126(000):114311-114311[13]CUI L F, ZHANG Q Z, SHI Y, et al.A method for satellite time series anomaly detection based on fast-DTW and improved-KNN[J].Chinese Journal of Aeronautics, 2023, 36(2):149-159[14]SADR M A M, ZHU Y Y, HU P.An anomaly detection method for satellites using Monte Carlo dropout[J].IEEE Transactions on Aerospace and Electronic Systems, 2022, 59(2):2044-2052[15]ZHAO H T, QIU S, LIU M, et al.Satellite anomaly detection based on reconstruction discrepancy theory utilizing a new dual-branch reconstruction model[J].Science China Technological Sciences, 2024, 67(10):3294-3307[16]YIN S, DING S X, XIE X C, et al.A review on basic data-driven approaches for industrial process monitoring[J].IEEE Transactions on Industrial Electronics, 2014, 61(11):6418-6428[17]LUO T Y, LIU M, SHI P, et al.A hybrid data preprocessing-based hierarchical attention BiLSTM network for remaining useful life prediction of spacecraft lithium-ion batteries[J].IEEE Transactions on Neural Networks and Learning Systems, 2023, 35(12):18076-18089[18]周治国, 马文浩, 刘杰强, 等.小卫星健康状态自主模糊综合评估方法[J].电子与信息学报, 2022, 44(10):3553-3565[19]张永, 龚众望, 郑英, 等.工业设备的健康状态评估和退化趋势预测联合研究[J].中国科学: 技术科学, 2022, 52(1):180-197[20]李天梅, 司小胜, 张建勋.多源传感监测线性退化设备数模联动的剩余寿命预测方法[J].航空学报, 2023, 44(8):227190-227190[21]ZAMANZADEH DARBAN Z, WEBB G I, PAN S, et al.Deep learning for time series anomaly detection: A survey[J].ACM Computing Surveys, 2024, 57(1):1-42[22]RUFF L, KAUFFMANN J R, VANDERMEULEN R A, et al.A unifying review of deep and shallow anomaly detection[J].Proceedings of the IEEE, 2021, 109(5):756-795[23]LU J, LIU A, DONG F, et al.Learning under concept drift: A review[J].IEEE Transactions on Knowledge and Data Engineering, 2018, 31(12):2346-2363[24]XU J, WU H, WANG J, et al..Anomaly transformer: Time series anomaly detection with association discrepancy[J].arXiv preprint, 2021, arXiv:2110.02642:-[25]TULI S, CASALE G, JENNINGS N R.TranAD: Deep transformer networks for anomaly detection in multivariate time series data[J].Proceedings of the VLDB Endowment, 2022, 15(6):1201-1214[26]YANG Y Y, ZHANG C L, ZHOU T, et al.DCdetector: Dual attention contrastive representation learning for time series anomaly detection[J].Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York: ACM, 2023, :3033-3045[27]PEYRé G, CUTURI M.Computational optimal transport[J].Foundations and Trends in Machine Learning, 2019, 11(5-6):355-607[28]KIM T, KIM J, TAE Y, et al.Reversible instance normalization for accurate time-series forecasting against distribution shift[J].International Conference on Learning Representations, 2022, :-[29]VASWANI A, SHAZEER N, PARMAR N, et al.Attention is all you need[J].Advances in Neural Information Processing Systems. Long Beach: Neural Information Processing Systems Foundation, 2017, :5998-6008[30]CHO K, VAN MERRI?NBOER B, GULCEHRE C, et al.learning phrase representations using RNN encoder-decoder for statistical machine translation[J].Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Doha: Association for Computational Linguistics, 2014, :1724-1734[31]HOCHREITER S, SCHMIDHUBER J.Long short-term memory[J].Neural Computation, 1997, 9(8):1735-1780[32]BAI S J, KOLTER J Z, KOLTUN V.An empirical evaluation of generic convolutional and recurrent networks for sequence modeling[J].arXiv preprint, 2018, arXiv.1803.01271[33]ZHOU H Y, ZHANG S H, PENG J Q, et al.Informer: Beyond efficient transformer for long sequence time-series forecasting[J].Proceedings of the AAAI Conference on Artificial Intelligence, 2021, 35(12):11106-11115[34]ZENG A, CHEN M X, ZHANG L, et al.Are transformers effective for time series forecasting?[J].Proceedings of the AAAI Conference on Artificial Intelligence, 2023, 37(9):11121-11128[35]YI K, ZHANG Q, FAN W, et al.Frequency-domain MLPs are more effective learners in time series forecasting[J].Advances in Neural Information Processing Systems. New Orleans: Neural Information Processing Systems Foundation, 2023, 36:76656-76679 |
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