發(fā)布時(shí)間：2018-08-02 12:54

【摘要】：事后維修與定期維修的航空發(fā)動(dòng)機(jī)維修方式過(guò)于陳舊,存在效率低下、維修費(fèi)用巨大、無(wú)法有效保證飛行安全性與可靠性等諸多弊端,而且這些弊端在實(shí)際工程應(yīng)用中顯露得越來(lái)越明顯。與傳統(tǒng)維修方式相比,航空發(fā)動(dòng)機(jī)預(yù)測(cè)與健康管理(EPHM)技術(shù)實(shí)現(xiàn)了事后被動(dòng)維修、定期維修向基于智能系統(tǒng)的視情維修轉(zhuǎn)變,使工程技術(shù)人員在特定的時(shí)間準(zhǔn)確定位發(fā)動(dòng)機(jī)的潛在故障并展開主動(dòng)的維修成為了可能,從而提高飛機(jī)維修效率、飛行安全性和飛機(jī)可靠性,降低維修成本。 以Rolls-Royce公司研制的Trent700發(fā)動(dòng)機(jī)氣路系統(tǒng)為例,本文針對(duì)航空發(fā)動(dòng)機(jī)預(yù)測(cè)與健康管理技術(shù)的一項(xiàng)核心問(wèn)題即數(shù)據(jù)挖掘技術(shù)展開了深入研究。首先對(duì)該型號(hào)發(fā)動(dòng)機(jī)氣路系統(tǒng)狀態(tài)參數(shù)的相關(guān)數(shù)據(jù)進(jìn)行信息挖掘。以渦輪燃?xì)鉁囟萒GT為例,對(duì)反映氣路性能的狀態(tài)參數(shù)的基線進(jìn)行建模,并對(duì)模型進(jìn)行了驗(yàn)證,基線精度達(dá)到了要求,為發(fā)動(dòng)機(jī)氣路狀態(tài)的監(jiān)測(cè)奠定了基礎(chǔ)。之后以監(jiān)測(cè)過(guò)程中計(jì)算得到的氣路參數(shù)偏差值為基礎(chǔ),對(duì)其中蘊(yùn)含的性能衰退信息進(jìn)行挖掘,建立支持向量機(jī)(Support Vector Machines,SVM)算法的回歸預(yù)測(cè)模型,對(duì)多個(gè)氣路參數(shù)在未來(lái)的偏差量進(jìn)行了單點(diǎn)趨勢(shì)預(yù)測(cè)。此外,在此預(yù)測(cè)模型的基礎(chǔ)上進(jìn)行拓展,嘗試融入模糊信息�；碚�,建立基于信息�；闹С窒蛄繖C(jī)(Granular Support Vector Machines, GrSVM)預(yù)測(cè)模型,對(duì)未來(lái)五個(gè)時(shí)間序列點(diǎn)進(jìn)行范圍性預(yù)測(cè)。最后通過(guò)仿真實(shí)驗(yàn)對(duì)模型的預(yù)測(cè)性能進(jìn)行檢驗(yàn)與分析,結(jié)果證明基于SVM的單點(diǎn)預(yù)測(cè)模型與基于GrSVM的范圍預(yù)測(cè)模型的精度達(dá)到要求,為EPHM體系的趨勢(shì)預(yù)測(cè)研究提供了參考。
[Abstract]:After maintenance and regular maintenance of aero-engine maintenance mode is too old, there are many shortcomings such as low efficiency, huge maintenance costs, can not effectively ensure flight safety and reliability, and so on. And these malpractices are more and more obvious in practical engineering application. Compared with the traditional maintenance method, the aero-engine prediction and health management (EPHM) technology realizes the passive maintenance after the event, and the periodic maintenance changes to the intelligent system-based maintenance. It is possible for engineers and technicians to accurately locate the potential faults of the engine and carry out active maintenance at a specific time, thus improving the aircraft maintenance efficiency, flight safety and aircraft reliability, and reducing the maintenance cost. Taking the gas path system of Trent700 engine developed by Rolls-Royce Company as an example, this paper makes a deep research on the data mining technology, which is one of the core problems of aero-engine prediction and health management technology. Firstly, the relevant data of the gas path system state parameters of the engine are mined. Taking the turbine gas temperature TGT as an example, the baseline of the state parameters reflecting the performance of the gas path is modeled, and the model is verified. The baseline accuracy meets the requirements, which lays a foundation for the monitoring of the gas path state of the engine. Then, based on the deviation values of gas path parameters calculated in the monitoring process, the performance degradation information contained therein is mined, and the regression prediction model of support vector machine (Support Vector machines) algorithm is established. A single point trend prediction is made for the deviation of multiple gas path parameters in the future. In addition, on the basis of this prediction model, we try to incorporate the fuzzy information granulation theory, establish the support vector machine (Granular Support Vector Machines, GrSVM) prediction model based on information granulation, and predict the range of the next five time series points. Finally, the prediction performance of the model is tested and analyzed by simulation experiments. The results show that the precision of the single point prediction model based on SVM and the range prediction model based on GrSVM meets the requirements, which provides a reference for the trend prediction research of EPHM system.
【學(xué)位授予單位】：中國(guó)民用航空飛行學(xué)院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：V263.6

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 劉楊;任德奎;;基于灰色理論的間斷性需求備件預(yù)測(cè)方法[J];四川兵工學(xué)報(bào);2011年04期

2 李愛(ài)軍;章衛(wèi)國(guó);譚鍵;;飛行器健康管理技術(shù)綜述[J];電光與控制;2007年03期

3 張叔農(nóng);康銳;;數(shù)據(jù)挖掘技術(shù)在航空發(fā)動(dòng)機(jī)PHM中的應(yīng)用[J];彈箭與制導(dǎo)學(xué)報(bào);2008年01期

4 馬碩;焦現(xiàn)煒;田柯文;呂世樂(lè);趙陽(yáng);鄭善軍;;故障預(yù)測(cè)技術(shù)發(fā)展與分類[J];四川兵工學(xué)報(bào);2013年02期

5 宋云雪;張科星;史永勝;;基于多元線性回歸的發(fā)動(dòng)機(jī)性能參數(shù)預(yù)測(cè)[J];航空動(dòng)力學(xué)報(bào);2009年02期

6 曹霞;黃圣國(guó);;ACMS的飛機(jī)狀態(tài)監(jiān)控新概念[J];江蘇航空;2000年03期

7 張寶珍;;預(yù)測(cè)與健康管理技術(shù)的發(fā)展及應(yīng)用[J];測(cè)控技術(shù);2008年02期

8 鐘詩(shī)勝,周志波,張永,康力平;基于三次回歸分析的試車臺(tái)基線庫(kù)的建立[J];計(jì)算機(jī)集成制造系統(tǒng);2005年02期

9 鐘詩(shī)勝;崔智全;付旭云;;Rolls-Royce發(fā)動(dòng)機(jī)基線挖掘方法[J];計(jì)算機(jī)集成制造系統(tǒng);2010年10期

10 尉詢楷;朱紀(jì)洪;陳良峰;馮悅;楊立;;航空發(fā)動(dòng)機(jī)PHM中的數(shù)據(jù)挖掘機(jī)遇與挑戰(zhàn)[J];計(jì)算機(jī)工程與科學(xué);2012年04期

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