计算机科学 ›› 2020, Vol. 47 ›› Issue (2): 227-232.doi: 10.11896/jsjkx.190600147
刘云1,2,尹传环1,2,胡迪3,赵田3,梁宇3
LIU Yun1,2,YIN Chuan-huan1,2,HU Di3,ZHAO Tian3,LIANG Yu3
摘要: 随着现代航天事业的飞速发展,通信卫星的结构日益复杂,其故障也逐渐增多,通信卫星的故障检测已成为当前航天领域关注的重点问题。目前,各大航天机构对故障的检测仍以简单的上下限阈值检测为主,只能检测出少部分特定的故障。早期利用传统机器学习算法进行检测的研究也仅能检测出数量特征上的故障。针对传统的机器学习算法难以有效学习遥测数据趋势变化的问题,文中提出了基于长短时记忆(Long Short-term Memory,LSTM)网络的阈值化方法。通过LSTM预测模型来学习卫星遥测数据的趋势变化,同时以最大化相关系数与F1分数的方式为多维遥测数据的故障判定确定合适的阈值,此方式能够有效地通过卫星遥测数据的趋势变化来判断故障。实验数据采用某航天机构提供的时长为2年的24维通信卫星遥测数据,其核心模型LSTM网络在NVIDIA Corporation GP102[TITAN Xp]上训练,最终整体模型的准确率为99.34%,查准率为81.93%,查全率为94.62%。同时,与传统机器学习算法以及基于LSTM的非阈值方法进行对比,该模型的精度明显更高。实验结果表明,LSTM网络能够高效地学习到卫星遥测数据的趋势变化特征;同时,采用合适的方法选定阈值,能够有效地检测出通信卫星发生的故障,在很大程度上成功地解决航天领域中通信卫星的故障检测难题。
中图分类号:
[1]PENG X Y,PANG J Y,PENG Y,et al.Overview of anomaly detection of spacecraft telemetry data[J].Chinese Journal of Scientific Instrument,2016,37(9):1929-1945. [2]GAO Y,YANG T,XU M Q,et al.An unsupervised anomaly detection approach for spacecraft based on normal behavior clustering[C]∥2012 Fifth International Conference on Intelligent Computation Technology and Automation.IEEE,2012:478-481. [3]MUNIYANDI A P,RAJESWARI R,RAJARAM R.Network anomaly detection by cascading k-Means clustering and C4.5 decision tree algorithm[J].Procedia Engineering,2012,30:174-182. [4]MÜNZ G,LI S,CARLE G.Traffic anomaly detection using k-means clustering[C]∥GI/ITG Workshop MMBnet.2007:13-14. [5]ERFANI S M,RAJASEGARAR S,KARUNASEKERA S, et al.High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning[J].Pattern Recognition,2016,58:121-134. [6]AMER M,GOLDSTEIN M,ABDENNADHER S.Enhancing one-class support vector machines for unsupervised anomaly detection[C]∥Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description.ACM,2013:8-15. [7]GEORGE A,VIDYAPEETHAM A V.Anomaly detection based on machine learning:dimensionality reduction using PCA and classification using SVM[J].International Journal of Computer Applications,2012,47(21):5-8. [8]MALHOTRA P,VIG L,SHROFF G,et al.Long short term memory networks for anomaly detection in time series[C]∥23rd European Symposium on Artificial Neural Networks,Computational Intelligence and Machine Learning.2015:89. [9]NANDURI A,SHERRY L.Anomaly detection in aircraft data using Recurrent Neural Networks (RNN)[C]∥2016 Integrated Communications Navigation and Surveillance (ICNS).IEEE,2016. [10]SHEIKHAN M,JADIDI Z,FARROKHI A.Intrusion detection using reduced-size RNN based on feature grouping[J].Neural Computing and Applications,2012,21(6):1185-1190. [11]BONTEMPS L,MCDERMOTT J,LE-KHAC N A.Collective anomaly detection based on long short-term memory recurrent neural networks[C]∥International Conference on Future Data and Security Engineering.Cham:Springer,2016:141-152. [12]MALHOTRA P,RAMAKRISHNAN A,ANAND G,et al. LSTM-based encoder-decoder for multi-sensor anomaly detection[J].arXiv:1607.00148,2016. [13]CHENG M,XU Q,LV J,et al.MS-LSTM:A multi-scale LSTM model for BGP anomaly detection[C]∥2016 IEEE 24th International Conference on Network Protocols (ICNP).IEEE,2016:1-6. [14]HUNDMAN K,CONSTANTINOU V,LAPORTE C,et al.Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding[C]∥Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mi-ning.ACM,2018:387-395. [15]HOCHREITER S,SCHMIDHUBER J.Long short-term memory[J].Neural Computation,1997,9(8):1735-1780. [16]CHANDOLA V,BANERJEE A,KUMAR V.Anomaly detec-tion:A survey[J].ACM computing surveys (CSUR),2009,41(3):15. [17]GOLDSTEIN M,UCHIDA S.A comparative evaluation of unsupervise danomaly detection algorithms for multivariate data[J].PloS One,2016,11(4):e0152173. [18]LI Q,ZHOU X S,LIN P,et al.Anomaly detection and fault diagnosis technology of spacecraft based on telemetry-mining.[C]∥2010 3rd International Symposium on Systems and Control in Aeronautics and Astronautics.IEEE,2010:233-236. [19]ESKIN E,ARNOLD A,PRERAU M,et al.A geometric framework for unsupervised anomaly detection[M]∥Applications of data mining in computer security.Boston:Springer,MA,2002:77-101. [20]LIU F T,TING K M,ZHOU Z H.Isolation-based anomaly detection[J].ACM Transactions on Knowledge Discovery from Data,2012,6(1):1-39. [21]BAY S D,SCHWABACHER M.Mining distance-based outliers in near linear time with randomization and a simple pruning rule[C]∥Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2003:29-38. [22]IVERSON D.Data mining applications for space mission operations system health monitoring[C]∥SpaceOps 2008 Confe-rence.2008:3212. [23]KRIEGEL H P,KRÖGER P,SCHUBERT E,et al.LoOP:local outlier probabilities[C]∥Proceedings of the 18th ACM Confe-rence on Information and Knowledge Management.ACM,2009:1649-1652. [24]PAJOUH H H,JAVIDAN R,KHAYAMI R,et al.A two-layer dimension reduction and two-tier classification model for anomaly-based intrusion detection in IoT backbone networks[J].IEEE Transactions on Emerging Topics in Computing,2016,7:314-323. [25]DE VRIES T,CHAWLA S,HOULE M E.Finding local anomalies in very high dimensional space[C]∥2010 IEEE InternationalConference on Data Mining.IEEE,2010:128-137. [26]CHO K,VAN MERRIËNBOER B,BAHDANAU D,et al.On the properties of neural machine translation:Encoder-decoder approaches[J].arXiv:1409.1259,2014. [27]DUAN Y,LV Y,WANG F Y.Travel time prediction with LSTM neural network[C]∥2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).IEEE,2016:1053-1058. |
[1] | 冷典典, 杜鹏, 陈建廷, 向阳. 面向自动化集装箱码头的AGV行驶时间估计 Automated Container Terminal Oriented Travel Time Estimation of AGV 计算机科学, 2022, 49(9): 208-214. https://doi.org/10.11896/jsjkx.210700028 |
[2] | 宁晗阳, 马苗, 杨波, 刘士昌. 密码学智能化研究进展与分析 Research Progress and Analysis on Intelligent Cryptology 计算机科学, 2022, 49(9): 288-296. https://doi.org/10.11896/jsjkx.220300053 |
[3] | 李瑶, 李涛, 李埼钒, 梁家瑞, Ibegbu Nnamdi JULIAN, 陈俊杰, 郭浩. 基于多尺度的稀疏脑功能超网络构建及多特征融合分类研究 Construction and Multi-feature Fusion Classification Research Based on Multi-scale Sparse Brain Functional Hyper-network 计算机科学, 2022, 49(8): 257-266. https://doi.org/10.11896/jsjkx.210600094 |
[4] | 张光华, 高天娇, 陈振国, 于乃文. 基于N-Gram静态分析技术的恶意软件分类研究 Study on Malware Classification Based on N-Gram Static Analysis Technology 计算机科学, 2022, 49(8): 336-343. https://doi.org/10.11896/jsjkx.210900203 |
[5] | 何强, 尹震宇, 黄敏, 王兴伟, 王源田, 崔硕, 赵勇. 基于大数据的进化网络影响力分析研究综述 Survey of Influence Analysis of Evolutionary Network Based on Big Data 计算机科学, 2022, 49(8): 1-11. https://doi.org/10.11896/jsjkx.210700240 |
[6] | 金方焱, 王秀利. 融合RACNN和BiLSTM的金融领域事件隐式因果关系抽取 Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM 计算机科学, 2022, 49(7): 179-186. https://doi.org/10.11896/jsjkx.210500190 |
[7] | 陈明鑫, 张钧波, 李天瑞. 联邦学习攻防研究综述 Survey on Attacks and Defenses in Federated Learning 计算机科学, 2022, 49(7): 310-323. https://doi.org/10.11896/jsjkx.211000079 |
[8] | 肖治鸿, 韩晔彤, 邹永攀. 基于多源数据和逻辑推理的行为识别技术研究 Study on Activity Recognition Based on Multi-source Data and Logical Reasoning 计算机科学, 2022, 49(6A): 397-406. https://doi.org/10.11896/jsjkx.210300270 |
[9] | 姚烨, 朱怡安, 钱亮, 贾耀, 张黎翔, 刘瑞亮. 一种基于异质模型融合的 Android 终端恶意软件检测方法 Android Malware Detection Method Based on Heterogeneous Model Fusion 计算机科学, 2022, 49(6A): 508-515. https://doi.org/10.11896/jsjkx.210700103 |
[10] | 李亚茹, 张宇来, 王佳晨. 面向超参数估计的贝叶斯优化方法综述 Survey on Bayesian Optimization Methods for Hyper-parameter Tuning 计算机科学, 2022, 49(6A): 86-92. https://doi.org/10.11896/jsjkx.210300208 |
[11] | 赵璐, 袁立明, 郝琨. 多示例学习算法综述 Review of Multi-instance Learning Algorithms 计算机科学, 2022, 49(6A): 93-99. https://doi.org/10.11896/jsjkx.210500047 |
[12] | 王杉, 徐楚怡, 师春香, 张瑛. 基于CNN-LSTM的卫星云图云分类方法研究 Study on Cloud Classification Method of Satellite Cloud Images Based on CNN-LSTM 计算机科学, 2022, 49(6A): 675-679. https://doi.org/10.11896/jsjkx.210300177 |
[13] | 王飞, 黄涛, 杨晔. 基于Stacking多模型融合的IGBT器件寿命的机器学习预测算法研究 Study on Machine Learning Algorithms for Life Prediction of IGBT Devices Based on Stacking Multi-model Fusion 计算机科学, 2022, 49(6A): 784-789. https://doi.org/10.11896/jsjkx.210400030 |
[14] | 许杰, 祝玉坤, 邢春晓. 机器学习在金融资产定价中的应用研究综述 Application of Machine Learning in Financial Asset Pricing:A Review 计算机科学, 2022, 49(6): 276-286. https://doi.org/10.11896/jsjkx.210900127 |
[15] | 李野, 陈松灿. 基于物理信息的神经网络:最新进展与展望 Physics-informed Neural Networks:Recent Advances and Prospects 计算机科学, 2022, 49(4): 254-262. https://doi.org/10.11896/jsjkx.210500158 |
|