基于历史数据的测控系统指标预测方法探索
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摘要
目前对航天测控系统进行故障预测与健康管理成为提高系统安全和可靠性的新发展方向。但已有的方式,如监控系统、自动化测试等,只能对系统进行实时监测或者事后分析。如何让测控系统在实时监测自身状态的基础上,能提前预估系统未来指标状态,从而更好地预防系统可能发生的故障,成为一条新的测控系统发展思路。在经典测控系统的故障识别和预测技术基础之上,针对测控系统关键指标预测的问题,探索基于历史数据的指标预测方法,具体分析基于SVR的单步和多步指标预测方法,并使用实测数据进行试验和分析。与Ridge、Lasso等传统线性回归方法相比,使用SVR进行指标预测更为准确。
At present Prognostic and Health Management(PHM) becomes the new development direction to improve the safety and reliability of space TT&C system. But the existing means, such as monitoring system and automated testing, can only monitor the system timely. How to predict the future state of the TT&C system based on the real-time monitoring, so as to prevent the occurrence of system failure, becomes a new idea for TT&C system development. On the basis of the classical fault identification and prediction technology of TT&C system, this paper studies the index forecasting methods based on the historical data for the key index prediction of TT&C system. The single step and multi-step prediction methods based on SVR are analyzed in detail and they are verified by the measurement data of TT&C system. Compared with the current Ridge, Lasso and other traditional linear regression methods, the prediction methods based on SVR are more accurate for the index forecasting of the TT&C system.
At present Prognostic and Health Management(PHM) becomes the new development direction to improve the safety and reliability of space TT&C system. But the existing means, such as monitoring system and automated testing, can only monitor the system timely. How to predict the future state of the TT&C system based on the real-time monitoring, so as to prevent the occurrence of system failure, becomes a new idea for TT&C system development. On the basis of the classical fault identification and prediction technology of TT&C system, this paper studies the index forecasting methods based on the historical data for the key index prediction of TT&C system. The single step and multi-step prediction methods based on SVR are analyzed in detail and they are verified by the measurement data of TT&C system. Compared with the current Ridge, Lasso and other traditional linear regression methods, the prediction methods based on SVR are more accurate for the index forecasting of the TT&C system.
引文
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[2]韩国泰.航空电子的故障预测与健康管理技术[J].航空电子技术,2009,40(1):30–38.Han Guotai.Prognostics and Health Management of Avionics[J].Avionics Technology,2009,40(1):30–38.
[3]周俊杰,王德功,常硕.浅析基于模型的航空电子装备故障预测[J].装备制造技术,2010,(5):34–35.Zhou Junjie,Wang Degong,Chang Shuo.A Preliminary Analysis for Error Prediction of Aviation Electronic Armament Based on Model[J].Equipment Manufacturing Technology,2010,(5):34–35.
[4]赵晖.支持向量机分类方法及其在文本分类中的应用研究[D].大连:大连理工大学,2005.Zhao Hui.Study on Support Vector Machines Classification Methods and Their Application in Text Categorization[D].Dalian:Dalian University of Technology,2005.
[5]陈晓峰,王士同,曹苏群,等.新的稀疏支持向量回归机算法及实验研究[J].计算机工程与应用,2008,44(36):24–28.Chen Xiaofeng,Wang Shitong,Cao Suqun,et al.Novel Sparse Support Vector Regression and Its Experimental Study[J].Computer Engineering and Applications,2008,44(36):24–28.
[6]陈家伟.支持向量机在人体健康状态预测中的研究与应用[D].合肥:中国科学技术大学,2014.Chen Jiawei.The Research and Application in Health Prediction by SVM[D].Hefei:University of Science and Technology of China,2014.
[7]罗军.3D连续手势识别研究[D].成都:电子科技大学,2014.Luo Jun.3D Continuous Hand Gesture Recognition Research[D].Chengdu:University of Electronic Science and Technology of China,2014.
[8]陈志刚.基于度量学习和聚类的一致性预测器算法研究[D].厦门:厦门大学,2014.Chen Zhigang.Research of Conformal Predictor Based on Distance Metric Learning and Clustering[D].Xiamen:Xiamen University,2014.