【摘要】：事后維修與定期維修的航空發(fā)動機(jī)維修方式過于陳舊,存在效率低下、維修費用巨大、無法有效保證飛行安全性與可靠性等諸多弊端,而且這些弊端在實際工程應(yīng)用中顯露得越來越明顯。與傳統(tǒng)維修方式相比,航空發(fā)動機(jī)預(yù)測與健康管理(EPHM)技術(shù)實現(xiàn)了事后被動維修、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)人員在特定的時間準(zhǔn)確定位發(fā)動機(jī)的潛在故障并展開主動的維修成為了可能,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)動機(jī)氣路系統(tǒng)為例,本文針對航空發(fā)動機(jī)預(yù)測與健康管理技術(shù)的一項核心問題即數(shù)據(jù)挖掘技術(shù)展開了深入研究。首先對該型號發(fā)動機(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,對反映氣路性能的狀態(tài)參數(shù)的基線進(jìn)行建模,并對模型進(jìn)行了驗證,基線精度達(dá)到了要求,為發(fā)動機(jī)氣路狀態(tài)的監(jiān)測奠定了基礎(chǔ)。之后以監(jiān)測過程中計算得到的氣路參數(shù)偏差值為基礎(chǔ),對其中蘊(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)測模型,對多個氣路參數(shù)在未來的偏差量進(jìn)行了單點趨勢預(yù)測。此外,在此預(yù)測模型的基礎(chǔ)上進(jìn)行拓展,嘗試融入模糊信息粒化理論,建立基于信息�；闹С窒蛄繖C(jī)(Granular Support Vector Machines, GrSVM)預(yù)測模型,對未來五個時間序列點進(jìn)行范圍性預(yù)測。最后通過仿真實驗對模型的預(yù)測性能進(jìn)行檢驗與分析,結(jié)果證明基于SVM的單點預(yù)測模型與基于GrSVM的范圍預(yù)測模型的精度達(dá)到要求,為EPHM體系的趨勢預(yù)測研究提供了參考。
[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】：中國民用航空飛行學(xué)院
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：V263.6

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