新型大氣數(shù)據(jù)傳感系統(tǒng)故障自診斷關鍵技術研究
發(fā)布時間：2018-07-13 18:48
【摘要】：新型大氣數(shù)據(jù)傳感系統(tǒng)是一種不但可同時測量飛行器的飛行高度、速度、攻角和側(cè)滑角等多種飛行參數(shù),而且可進行自身狀態(tài)在線自確認的大氣數(shù)據(jù)系統(tǒng)。該系統(tǒng)充分繼承了嵌入式大氣數(shù)據(jù)傳感技術和自確認傳感技術的優(yōu)勢,適用于現(xiàn)代飛行器的高隱身、高機動性和高可靠性需求。本課題旨在研究故障檢測、故障定位及故障診斷等狀態(tài)自確認方法,解決新型大氣數(shù)據(jù)傳感系統(tǒng)的若干關鍵技術問題。論文的主要研究內(nèi)容如下:(1)針對新型大氣數(shù)據(jù)傳感系統(tǒng)的故障傳播問題,研究一種基于模糊概率Petri網(wǎng)的故障傳播分析方法。利用模糊概率Petri網(wǎng)的強大建模和邏輯推理性能,分析系統(tǒng)的最大概率故障傳播路徑,分別建立系統(tǒng)組件級和系統(tǒng)級的故障傳播規(guī)律模型,獲取可充分覆蓋測試樣本集的主要故障模式。試驗結(jié)果表明,壓力傳感器異常、信號采集及處理電路異常以及測壓孔堵塞是大氣數(shù)據(jù)系統(tǒng)的主要故障,與專家知識及工程經(jīng)驗得出的結(jié)論一致。(2)針對新型大氣數(shù)據(jù)傳感系統(tǒng)的故障檢測及故障源定位問題,研究一種基于小波核主元分析和故障指示向量的多故障檢測及識別方法。利用核主元分析方法分析多路測壓通道間的內(nèi)在關系,研究待測樣本在高維特征殘差空間內(nèi)投影量的變化與故障檢測的關系,驗證小波核的多分辨率分析能力在瞬時性故障檢測中的優(yōu)勢;根據(jù)測壓點布局的冗余特性,研究攻角和側(cè)滑角參數(shù)分別與垂向和縱向測壓點的內(nèi)在關系,建立故障指示向量知識庫表征測壓通道狀態(tài),驗證系統(tǒng)在低馬赫數(shù)小攻角和高馬赫數(shù)大攻角情形下,通過故障指示向量匹配實現(xiàn)故障源定位的有效性。實驗結(jié)果表明,該方法可實現(xiàn)多故障無遺漏檢測,總故障數(shù)小于3的典型故障檢測率大于90%,故障源定位率為100%。(3)針對新型大氣數(shù)據(jù)傳感系統(tǒng)的非線性故障特征提取和多故障分類問題,研究一種基于集合經(jīng)驗模態(tài)分解和多分類相關向量機的故障診斷方法。利用集合經(jīng)驗模態(tài)分解的信號自適應分解特性,分析不同類型故障輸出信號在不同本征模分量上的能量特征差異性,建立不同類型故障特征向量集,驗證集合經(jīng)驗模態(tài)分解的抗模態(tài)混疊和故障特征提取性能;利用多分類相關向量機的小樣本學習、分類結(jié)果概率形式輸出、單模型多分類等特性,分析故障診斷與分類結(jié)果不確定性的關系,研究基于交叉驗證的最優(yōu)核參數(shù)選取方法,建立不同故障模式的多分類器模型,驗證多分類相關向量機的多故障類型同時識別優(yōu)勢。與傳統(tǒng)經(jīng)驗模態(tài)分析方法相比,該方法具有明顯的抗模態(tài)混疊優(yōu)勢,對系統(tǒng)正常工作、壓力波動大、壓力跳變、壓力偏置和壓力恒值輸出等樣本識別為對應故障類型的平均分類概率分別大于86%和80%,故障分類正確率為100%。(4)為驗證研究的新型大氣數(shù)據(jù)系統(tǒng)故障檢測、故障識別及故障診斷方法的有效性,設計一種新型大氣數(shù)據(jù)系統(tǒng)仿真試驗平臺,模擬產(chǎn)生各種真實故障,對系統(tǒng)分布式壓力傳感測量進行標定和測試,獲取正常測試樣本和故障仿真數(shù)據(jù)樣本集。
[Abstract]:The new atmospheric data sensing system is a kind of flight parameters which can not only measure flight height, speed, angle of attack and sideslip angle simultaneously, but also can carry out the self confirmed air data system on line self state. This system fully inherits the advantages of embedded atmospheric data sensing technology and self recognition sensing technology. The high stealth, high mobility and high reliability of the generation of aircraft is required. This topic aims to study the state self validation methods such as fault detection, fault location and fault diagnosis to solve some key technical problems of the new atmospheric data sensing system. The main contents of this paper are as follows: (1) fault propagation for the new atmospheric data sensing system A fault propagation analysis method based on fuzzy probability Petri net is studied. Using the powerful modeling and logic reasoning performance of fuzzy probability Petri net, the maximum probability fault propagation path of the system is analyzed. The model of system component level and system level fault propagation law is established respectively, and the main reasons that can fully cover the test sample set are obtained. The test results show that the abnormal pressure sensor, the abnormal signal acquisition and processing circuit and the blockage of the pressure measurement hole are the main faults of the atmospheric data system, which are consistent with the conclusions obtained by the expert knowledge and engineering experience. (2) a new kind of fault detection and fault location based on the new atmospheric data sensing system is studied. Nuclear principal component analysis and fault indicator vector multiple fault detection and recognition method. Using kernel principal component analysis method to analyze the internal relationship between multi-channel pressure measurement channels and study the relationship between the change of the projection quantity of the sample in the high dimensional feature residual space and the fault detection, and verify the multi-resolution analysis ability of the wavelet kernel in the instantaneous fault detection. In accordance with the redundancy characteristics of the pressure point layout, the internal relationship between the angle of attack and the sideslip angle and the vertical and longitudinal pressure measurement points are studied. The fault indicator vector knowledge base is established to represent the state of the pressure measurement channel. The fault indicator vector matching is used to verify the failure of the system in the case of low Maher number and high Maher number of attack angle. The experimental results show that the method can achieve multiple failure detection, the total fault number is less than 3, the typical fault detection rate is more than 90%, the location rate of the fault source is 100%. (3) for the nonlinear fault feature extraction and multi fault classification problem of the new atmospheric data sensing system, and a research based on the set of empirical mode decomposition is studied. The fault diagnosis method of the multi classification correlation vector machine. Using the adaptive signal decomposition characteristic of the set empirical mode decomposition, the difference of energy characteristics of different types of fault output signals on different eigenmode components is analyzed, and different types of fault feature vectors are set up to verify the anti modal aliasing and fault characteristics of the integrated empirical mode decomposition. By using the small sample learning of the multi classification correlation vector machine, the probability form output of the classification result and the single model and multi classification, the relationship between the fault diagnosis and the uncertainty of the classification results is analyzed. The optimal kernel parameter selection method based on the cross validation is studied, and the multi classifier model is set up to verify the multi classification phase. Compared with the traditional empirical mode analysis, the method has obvious advantages of anti modal aliasing. The average classification probability of the sample identification for the corresponding fault types, such as the normal working of the system, the pressure fluctuation, the pressure jump, the pressure bias and the pressure constant output, is greater than 86% and 80%, respectively. The correct rate of fault classification is 100%. (4) to verify the effectiveness of the new atmospheric data system fault detection, fault identification and fault diagnosis method. A new atmospheric data system simulation test platform is designed to simulate various real faults and to calibrate and test the distributed pressure sensing measurement of the system, and to obtain the normal test sample. This and fault simulation data sample set.
【學位授予單位】：北京理工大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：TP79
本文編號：2120402
[Abstract]:The new atmospheric data sensing system is a kind of flight parameters which can not only measure flight height, speed, angle of attack and sideslip angle simultaneously, but also can carry out the self confirmed air data system on line self state. This system fully inherits the advantages of embedded atmospheric data sensing technology and self recognition sensing technology. The high stealth, high mobility and high reliability of the generation of aircraft is required. This topic aims to study the state self validation methods such as fault detection, fault location and fault diagnosis to solve some key technical problems of the new atmospheric data sensing system. The main contents of this paper are as follows: (1) fault propagation for the new atmospheric data sensing system A fault propagation analysis method based on fuzzy probability Petri net is studied. Using the powerful modeling and logic reasoning performance of fuzzy probability Petri net, the maximum probability fault propagation path of the system is analyzed. The model of system component level and system level fault propagation law is established respectively, and the main reasons that can fully cover the test sample set are obtained. The test results show that the abnormal pressure sensor, the abnormal signal acquisition and processing circuit and the blockage of the pressure measurement hole are the main faults of the atmospheric data system, which are consistent with the conclusions obtained by the expert knowledge and engineering experience. (2) a new kind of fault detection and fault location based on the new atmospheric data sensing system is studied. Nuclear principal component analysis and fault indicator vector multiple fault detection and recognition method. Using kernel principal component analysis method to analyze the internal relationship between multi-channel pressure measurement channels and study the relationship between the change of the projection quantity of the sample in the high dimensional feature residual space and the fault detection, and verify the multi-resolution analysis ability of the wavelet kernel in the instantaneous fault detection. In accordance with the redundancy characteristics of the pressure point layout, the internal relationship between the angle of attack and the sideslip angle and the vertical and longitudinal pressure measurement points are studied. The fault indicator vector knowledge base is established to represent the state of the pressure measurement channel. The fault indicator vector matching is used to verify the failure of the system in the case of low Maher number and high Maher number of attack angle. The experimental results show that the method can achieve multiple failure detection, the total fault number is less than 3, the typical fault detection rate is more than 90%, the location rate of the fault source is 100%. (3) for the nonlinear fault feature extraction and multi fault classification problem of the new atmospheric data sensing system, and a research based on the set of empirical mode decomposition is studied. The fault diagnosis method of the multi classification correlation vector machine. Using the adaptive signal decomposition characteristic of the set empirical mode decomposition, the difference of energy characteristics of different types of fault output signals on different eigenmode components is analyzed, and different types of fault feature vectors are set up to verify the anti modal aliasing and fault characteristics of the integrated empirical mode decomposition. By using the small sample learning of the multi classification correlation vector machine, the probability form output of the classification result and the single model and multi classification, the relationship between the fault diagnosis and the uncertainty of the classification results is analyzed. The optimal kernel parameter selection method based on the cross validation is studied, and the multi classifier model is set up to verify the multi classification phase. Compared with the traditional empirical mode analysis, the method has obvious advantages of anti modal aliasing. The average classification probability of the sample identification for the corresponding fault types, such as the normal working of the system, the pressure fluctuation, the pressure jump, the pressure bias and the pressure constant output, is greater than 86% and 80%, respectively. The correct rate of fault classification is 100%. (4) to verify the effectiveness of the new atmospheric data system fault detection, fault identification and fault diagnosis method. A new atmospheric data system simulation test platform is designed to simulate various real faults and to calibrate and test the distributed pressure sensing measurement of the system, and to obtain the normal test sample. This and fault simulation data sample set.
【學位授予單位】：北京理工大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：TP79
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，本文編號：2120402
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