基于小波与灰色方法的半球谐振陀螺寿命预测
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摘要
半球谐振陀螺具有成本高、批量小的特点,为了在不进行1:1实验的情况下评估其性能和寿命,提出基于小波分析与灰色关联度的残差修正GM(1,1)寿命预测方法。将小波变换引入半球谐振陀螺寿命预测中,利用2种紧支撑标准正交小波对半球谐振陀螺的漂移数据降噪处理以削弱序列的随机性,使用残差修正GM(1,1)模型对4个型号不同的半球谐振陀螺进行多周期数据预测,结合灰色关联分析方法得到半球谐振陀螺的预测寿命。实验结果显示,残差修正GM(1,1)对半球谐振陀螺预处理后漂移数据的预测精度高于GM(1,1)预测方法,表明该预测方法的正确性和有效性。
Hemispherical Resonator Gyroscope(HRG) has characteristics of high cost and low volume production. In order to evaluate its lifetime and reliability without whole life test, a method of combining residual modified GM(1,1), wavelet analysis and grey correlation is proposed in the paper. In this method, two compactly supported orthonormal wavelets are brought in to smooth the randomness in drift data.And residual modified GM(1,1) model is used to predict 4 different HRGs' several period data sequences with preprocessed data. With grey correlation, lifetimes of 4 HRGs are predicted out. Experimental results show that residual modified GM(1,1) has higher prediction accuracy than GM(1,1) and also demonstrate the method's validity in HRGs' lifetime prediction.
Hemispherical Resonator Gyroscope(HRG) has characteristics of high cost and low volume production. In order to evaluate its lifetime and reliability without whole life test, a method of combining residual modified GM(1,1), wavelet analysis and grey correlation is proposed in the paper. In this method, two compactly supported orthonormal wavelets are brought in to smooth the randomness in drift data.And residual modified GM(1,1) model is used to predict 4 different HRGs' several period data sequences with preprocessed data. With grey correlation, lifetimes of 4 HRGs are predicted out. Experimental results show that residual modified GM(1,1) has higher prediction accuracy than GM(1,1) and also demonstrate the method's validity in HRGs' lifetime prediction.
引文
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[10]Soltani S.On the Use of the Wavelet Decomposition for Time Series Prediction[J].Neurocomputing,2002,48(1/4):267-277.
[11]Mabrouk A B,Abdallah N B,Dhifaoui Z.Wavelet Decomposition and Autoregressive Model for Time Series Prediction[J].Applied Mathematics and Computation,2008,199(1):334-340.
[2]吕志清.半球谐振陀螺(HRG)信号处理技术[J].中国惯性技术学报,2000,20(4):58-61.
[3]冯培德,张保京,富铁.可靠性工程技术在航空惯性导航系统研制中的应用[J].中国惯性技术学报,1996,4(4):56-65.
[4]Li Boran,Wu Yiran,Wang Changhong.The Identification and Compensation of Temperature Model for Hemispherical Resonator Gyro Signal[C]//Proc.of the 3rd International Symposium on Systems and Control in Aeronautics and Astronautics.Harbin,China:[s.n.],2010:398-401.
[5]Yang Qian,Yi Guoxing,Wang Henian,et al.Kinetic Model Analysis and Testing of HRG[C]//Proc.of the 1st International Symposium on Systems and Control in Aerospace and Astronautics.Harbin,China:[s.n.],2006:105-109.
[6]Peng Hui,Fang Zhen,Lin Ke,et al.Error Analysis of Hemispherical Resonator Gyro Drift Data[C]//Proc.of the 2nd International Symposium on Systems and Control in Aerospace and Astronautics.Shenzhen,China:[s.n.],2008:1-4.
[7]邓聚龙.灰色论基础[M].武汉:华中科技大学出版社,2002.
[8]Hsu C C,Chen C Y.Applications of Improved Grey Prediction Model for Power Demand Forecasting[J].Energy Conversion and Management,2003,44(14):2241-2249.
[9]Albert W L,Chi S C,Chen J H.An Improved Grey-based Approach for Electricity Demand Forecasting[J].Electric Power Systems Research,2003,67(3):217-224.
[10]Soltani S.On the Use of the Wavelet Decomposition for Time Series Prediction[J].Neurocomputing,2002,48(1/4):267-277.
[11]Mabrouk A B,Abdallah N B,Dhifaoui Z.Wavelet Decomposition and Autoregressive Model for Time Series Prediction[J].Applied Mathematics and Computation,2008,199(1):334-340.