This paper proposes an autoregressive prediction method based on time convolutional network with attentional mechanism(time convolution network with squeeze and excitation, SE-TCN), to solve the problem of missing telemetry data of satellite panel temperature due to short entry time, framing error, and other reasons. Temperature telemetry data is considered to be a strong regularity of periodic signal, so this paper adopts the SE-TCN model to map from historical data to the data in the future, which completes the missing value interpolation and effectively solves the problem that the interpolation deviation of the physical model and statistical method is too large. At the same time, in order to characterize the interpolation effect on the actual missing data set, the calculation method of the evaluation index is added in this paper. Compared with long short-term memory network and time convolutiion network models, SE-TCN has a better interpolation effect on both the test set and the actual missing data set.
XU Kaikai
,
ZHANG Rui
. An interpolation method for temperature telemetry data of one-dimensional low-sampling satellite panel based on SE-TCN[J]. Journal of University of Chinese Academy of Sciences, 2023
, 40(6)
: 810
-820
.
DOI: 10.7523/j.ucas.2022.032
[1] 王亚坤, 杨凯飞, 张婕, 等. 卫星在轨故障案例与人工智能故障诊断[J]. 中国空间科学技术, 2022, 42(1):16-29. DOI:10.16708/j.cnki.1000-758X.2022.0002.
[2] Lepot M, Aubin J B, Clemens F. Interpolation in time series:an introductive overview of existing methods, their performance criteria and uncertainty assessment[J]. Water, 2017, 9(10):796. DOI:10.3390/w9100796.
[3] Sibson R. A brief description of natural neighbour interpolation[M/OL]//John W & Sons. Interpreting Multivariate Data. In:Barnett, V., Ed. New York, 1981:21-36[2022-03-13]. https://www.scirp.org/(S(i43dyn45teexjx455qlt3d2q))/reference/ReferencesPapers.aspx?ReferenceID=1553046.
[4] Gnauck A. Interpolation and approximation of water quality time series and process identification[J]. Analytical and Bioanalytical Chemistry, 2004, 380(3):484-492. DOI:10.1007/s00216-004-2799-3.
[5] Knotters M, Heuvelink G B M. A disposition of interpolation techniques[R/OL]. Wageningen, Holland:Wettelijke Onderzoekstaken Natuur & Milieu, 2010[2022-03-13]. https://edepot.wur.nl/139504.
[6] Dutta R S C, Minocha S. On the phase interpolation problem:a brief review and some new results[J]. Sadhana, 1991, 16(3):225-239. DOI:10.1007/BF02812044.
[7] Carrizosa E, Olivares N A V, Ramírez C P. Time series interpolation via global optimization of moments fitting[J]. European Journal of Operational Research, 2013, 230(1):97-112. DOI:10.1016/j.ejor.2013.04.008.
[8] Chen Y, Kopp G A, Surry D. Interpolation of wind-induced pressure time series with an artificial neural network[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2002, 90(6):589-615. DOI:10.1016/S0167-6105(02)00155-1.
[9] Kulesh M, Holschneider M, Kurennaya K. Adaptive metrics in the nearest neighbours method[J]. Physica D:Nonlinear Phenomena, 2008, 237(3):283-291. DOI:10.1016/j.physd.2007.08.019.
[10] Lütkepohl H. New Introduction to Multiple Time Series Analysis[M]. Berlin, Heidelberg:Springer Berlin Heidelberg, 2005. DOI:10.1007/978-3-540-27752-1.
[11] 张晓琴, 王敏. 基于主成分分析的成分数据缺失值插补法[J]. 应用概率统计, 2016, 32(1):101-110. DOI:10.3969/j.issn.1001-4268.2016.01.007.
[12] Salakhutdinov R, Mnih A. Bayesian probabilistic matrix factorization using Markov chain Monte Carlo[C]//ICML'08:Proceedings of the 25th international conference on Machine learning. 2008:880-887. DOI:10.1145/1390156.1390267.
[13] Babu C N, Sure P. Partitioning and interpolation based hybrid ARIMA-ANN model for time series forecasting[J]. Sādhanā, 2016, 41(7):695-706. DOI:10.1007/s12046-016-0508-5.
[14] Hundman K, Constantinou V, Laporte C, et al. Detecting spacecraft anomalies using LSTMs and nonparametric dynamic thresholding[C]//KDD'18:Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018:387-395. DOI:10.1145/3219819.3219845.
[15] 王少影, 张宇, 孟宪红, 等. 机器学习算法对涡动相关缺失通量数据的插补研究[J]. 高原气象, 2020, 39(6):1348-1360. DOI:10.7522/j.issn.1000-0534.2019.00142.
[16] Raubitzek S, Neubauer T. A fractal interpolation approach to improve neural network predictions for difficult time series data[J]. Expert Systems With Applications, 2021, 169:114474. DOI:10.1016/j.eswa.2020.114474.
[17] Bai S J, Kolter J Z, Koltun V. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling[EB/OL]. arXiv:1803.01271v2[cs.LG]. (2018-04-19)[2022-03-13]. https://arxiv.org/abs/1803.01271.
[18] Steinmetz C J, Reiss J D. Efficient neural networks for real-time analog audio effect modeling[EB/OL]. arXiv:2102.06200v1[eess.AS]. (2021-02-11)[2022-03-13]. https://arxiv.org/abs/2102.06200.
[19] Alqahtani S, Mishra A, Diab M. Efficient convolutional neural networks for diacritic restoration[EB/OL]. 2019:arXiv:1912.06900[cs.CL]. (2019-12-14)[2022-03-13]. https://arxiv.org/abs/1912.06900.
[20] Dai W, Zhang J S, Gao Y M, et al. Formant tracking using dilated convolutional networks through dense connection with gating mechanism[C]//Interspeech 2020. ISCA:ISCA, 2020:150-154. DOI:10.21437/interspeech.2020-1804.
[21] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. June 18-23, 2018, Salt Lake City, UT, USA. IEEE, 2018:7132-7141. DOI:10.1109/CVPR.2018.00745.
[22] 何利健, 张锐, 陈文卿. 基于SE-TCN网络模型的太阳电池阵温度异常检测[J]. 上海航天(中英文), 2021, 38(5):8-16. DOI:10.19328/j.cnki.2096-8655.2021.05.002.
[23] Chen W Q, Zhang R, Liu H, et al. A novel method for solar panel temperature determination based on a wavelet neural network and Hammerstein-Wiener model[J]. Advances in Space Research, 2020, 66(8):2035-2046. DOI:10.1016/j.asr.2020.07.002.
[24] Brownlee J. Deep learning for time series forecasting:predict the future with MLPs, CNNs and LSTMs in Python[M/OL]. Machine Learning Mastery, 2018[2022-03-13]. https://machinelearningmastery.com/deep-learning-for-time-series-forecasting/.
[25] Beale M H, Hagan M T, Demuth H B. Deep learning toolboxTM reference[M/OL]. MATLAB (r) R2021a The MathWorks, Inc, 2021[2022-03-13]. https://ww2.mathworks.cn/help/pdf_doc/deeplearning/index.html.
