【摘要】：衛(wèi)星在軌運(yùn)行期間會向地面?zhèn)魉痛罅窟b測數(shù)據(jù),這些數(shù)據(jù)真實地反應(yīng)了衛(wèi)星的有效載荷與運(yùn)行狀態(tài),通過挖掘高維遙測數(shù)據(jù)低維特征信息,可以有效提高衛(wèi)星異常狀態(tài)檢測能力和可靠性水平。本文針對“天巡一號”衛(wèi)星姿態(tài)控制系統(tǒng)遙測數(shù)據(jù),開展了基于局部線性潛入流形學(xué)習(xí)法的高維數(shù)據(jù)特征提取與故障檢測方法研究,并建立了衛(wèi)星姿態(tài)控制系統(tǒng)故障檢測快速半物理仿真系統(tǒng),驗證故障檢測方法可行性與適用性。在總結(jié)分析衛(wèi)星遙測數(shù)據(jù)的一般特征與分類基礎(chǔ)上,深入研究衛(wèi)星姿態(tài)控制系統(tǒng)遙測數(shù)據(jù)特性,設(shè)計基于主元分析的衛(wèi)星姿態(tài)控制系統(tǒng)故障檢測方案。從在軌衛(wèi)星的星上工作模式與遙測參量影響因素兩方面,介紹衛(wèi)星姿態(tài)控制系統(tǒng)遙測數(shù)據(jù)基本特征;針對遙測數(shù)據(jù)高維特性,通過主元分析方法,開展高維數(shù)據(jù)降維與特征提取方法研究,同時利用統(tǒng)計量實現(xiàn)對低維特征信息的異常檢測。針對一般線性特征提取方法無法挖掘非線性高維遙測數(shù)據(jù)特征信息問題,本文研究了基于局部線性嵌入流形學(xué)習(xí)法的數(shù)據(jù)特征提取與故障檢測方法。非線性高維遙測數(shù)據(jù)的低維嵌入幾何結(jié)構(gòu)難以通過一般線性特征提取方法獲得,采用局部線性嵌入流形法,設(shè)計高維遙測數(shù)據(jù)降維與特征提取方案;針對獲得的低維特征信息,結(jié)合統(tǒng)計量SPE和2T設(shè)計故障檢測方法;通過“天巡一號”遙測數(shù)據(jù)驗證所設(shè)計的特征提取與故障檢測方案的有效性。針對在線樣本數(shù)據(jù)不斷更新,傳統(tǒng)批處理工作模式的局部線性嵌入難以更新完善數(shù)據(jù)庫等問題,研究了基于增量式局部線性嵌入法的數(shù)據(jù)特征提取與故障檢測方法。通過在線樣本更新權(quán)值矩陣進(jìn)而完善數(shù)據(jù)庫,在此基礎(chǔ)上設(shè)計了在線樣本特征提取與故障檢測方案,通過“天巡一號”遙測數(shù)據(jù)驗證方法的有效性。針對地面獲取遙測數(shù)據(jù)不完整、衛(wèi)星在軌故障模擬困難等問題,設(shè)計衛(wèi)星姿態(tài)控制系統(tǒng)故障檢測快速仿真平臺。采用PC104和AD7011-EVA單板機(jī)分別模擬星載控制計算機(jī)、模型計算機(jī),采用激勵源端信號注入方式實現(xiàn)故障模擬與故障注入,將多配置飛輪接入閉環(huán)回路,通過對飛輪系統(tǒng)的故障模擬與故障注入,模擬飛行、獲取仿真數(shù)據(jù)基礎(chǔ)上驗證上述故障檢測方法的可行性以及檢測結(jié)論的一致性。
[Abstract]:A large number of telemetry data will be transmitted to the ground during the orbit operation. These data truly reflect the payload and operation state of the satellite. By mining the low-dimensional characteristic information of the high-dimensional telemetry data, It can effectively improve the ability and reliability of satellite abnormal state detection. In this paper, the feature extraction and fault detection method of high-dimensional data based on the local linear submersible manifold learning method is developed for the telemetering data of the satellite attitude control system of "Tianxuan-1" satellite. A fast semi-physical simulation system for fault detection of satellite attitude control system is established to verify the feasibility and applicability of the fault detection method. On the basis of summarizing and analyzing the general characteristics and classification of satellite telemetering data, the characteristics of satellite attitude control system telemetry data are deeply studied, and the scheme of satellite attitude control system fault detection based on principal component analysis is designed. This paper introduces the basic characteristics of satellite attitude control system telemetry data from the two aspects of onboard working mode and influencing factors of telemetry parameters, aiming at the high dimensional characteristics of telemetering data, the method of principal component analysis is used to analyze the characteristics of satellite attitude control system. The methods of dimensionality reduction and feature extraction of high-dimensional data are studied. At the same time, anomaly detection of low-dimensional feature information is realized by using statistics. Aiming at the problem that the general linear feature extraction method can not mine the feature information of the nonlinear high-dimensional telemetry data, this paper studies the data feature extraction and fault detection method based on the local linear embedded manifold learning method. The low dimensional embedded geometric structure of nonlinear high dimensional telemetry data is difficult to be obtained by general linear feature extraction method. The method of local linear embedding manifold is used to design the dimensionality reduction and feature extraction scheme of high dimensional telemetry data. Combined with statistic SPE and 2T, the fault detection method is designed, and the effectiveness of the designed feature extraction and fault detection scheme is verified by "Tianyun-1" telemetry data. Aiming at the problem that the online sample data is constantly updated and the local linear embedding of the traditional batch processing mode is difficult to update and perfect the database, the data feature extraction and fault detection method based on incremental local linear embedding method is studied. By updating the weight matrix of online samples to perfect the database, an online sample feature extraction and fault detection scheme is designed, and the validity of the method is verified by the remote sensing data of "Tianyun-1". Aiming at the problems such as incomplete acquisition of remote sensing data on the ground and difficulty in simulation of satellite fault in orbit, a fast simulation platform for fault detection of satellite attitude control system is designed. The PC104 and AD7011-EVA single board computer are used to simulate the spaceborne control computer and the model computer respectively. The fault simulation and fault injection are realized by the excitation source signal injection method. The multi-configuration flywheel is connected to the closed-loop. Through the fault simulation and fault injection of the flywheel system, the simulation flight is simulated, and the feasibility of the above fault detection method and the consistency of the detection conclusion are verified on the basis of obtaining the simulation data.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：V448.22;V467

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本文編號：2205838