本文選題：故障樹 + 專家系統(tǒng)��； 參考：《南京理工大學(xué)》2017年碩士論文

【摘要】：本文研究的特種車是航天領(lǐng)域的重要設(shè)備,其性能的可靠性對航天任務(wù)的完成至關(guān)重要。由于頻繁使用和設(shè)備復(fù)雜化的緣故,特種車在任務(wù)的執(zhí)行過程中會經(jīng)常出現(xiàn)故障,影響任務(wù)的正常進(jìn)行。隨著特種車設(shè)備集成化、綜合化和智能化水平的提高,設(shè)備研制的風(fēng)險(xiǎn)越來越大、周期越來越長、費(fèi)用越來越高,同時(shí),對設(shè)備運(yùn)行狀態(tài)的監(jiān)測以及維修手段也提出了更高的要求。為了實(shí)現(xiàn)對特種車的綜合健康管理,本文在研究故障樹分析法、專家系統(tǒng)分析法以及BP神經(jīng)網(wǎng)絡(luò)的基礎(chǔ)之上,提出了一種新的混合故障診斷方法,即基于故障樹和規(guī)則的專家系統(tǒng),同時(shí),建立了特種車的BP神經(jīng)網(wǎng)絡(luò)預(yù)測模型,對特種車的可能故障進(jìn)行預(yù)測。論文首先在分析特種車實(shí)際需求的基礎(chǔ)之上,設(shè)計(jì)了特種車故障預(yù)測與健康管理系統(tǒng)的體系結(jié)構(gòu)和軟件框架,并通過分析特種車的原始測試數(shù)據(jù),實(shí)現(xiàn)了測試數(shù)據(jù)的導(dǎo)入和相關(guān)文件的配置;然后,在分析特種車故障模式的基礎(chǔ)之上建立了特種車的故障樹模型,并通過對故障樹節(jié)點(diǎn)屬性的配置,實(shí)現(xiàn)了基于CStatic控件的圖形繪制與顯示功能;最后,在分析特種車歷史運(yùn)行數(shù)據(jù)的基礎(chǔ)之上,建立了特種車的健康等級樣本,通過建立的樣本對特種車的BP神經(jīng)網(wǎng)絡(luò)預(yù)測模型進(jìn)行訓(xùn)練和優(yōu)化,將訓(xùn)練好的BP神經(jīng)網(wǎng)絡(luò)預(yù)測模型進(jìn)行固化并嵌入到軟件平臺。論文的創(chuàng)新點(diǎn)主要有兩個(gè)方面:一是特種車故障預(yù)測與健康管理軟件平臺的通用性,通過導(dǎo)入一系列的配置文件,如算法配置文件、規(guī)則配置文件以及故障樹配置文件等,使得軟件平臺不依賴于具體的某一種特種車,只要具備數(shù)據(jù)文件和相關(guān)的配置文件,就可以對任意系列的特種車進(jìn)行健康管理;二是基于CStatic控件的故障樹圖形顯示方法,結(jié)合故障樹的節(jié)點(diǎn)屬性和CStatic控件,實(shí)現(xiàn)了故障樹圖形的可視化和多元化,通過該方法可實(shí)現(xiàn)故障樹的全自動(dòng)繪制,而且用戶可以根據(jù)自己的需求瀏覽故障樹。通過特種車的歷史運(yùn)行數(shù)據(jù)對故障預(yù)測與健康管理系統(tǒng)進(jìn)行實(shí)際驗(yàn)證,軟件的診斷和預(yù)測結(jié)果體現(xiàn)了系統(tǒng)的通用性和實(shí)用性,符合實(shí)際需求。
[Abstract]:The special vehicle studied in this paper is an important equipment in spaceflight field. The reliability of its performance is very important to the accomplishment of space mission. Because of the frequent use and complicated equipment, the special vehicle will often break down during the task execution, which will affect the normal operation of the task. With the integration, integration and intelligent level of special vehicle equipment, the risk of equipment development is increasing, the period is getting longer and longer, the cost is getting higher and higher, at the same time, Higher requirements are also put forward for the monitoring and maintenance of equipment operation status. In order to realize the comprehensive health management of special vehicles, a new hybrid fault diagnosis method is proposed based on the research of fault tree analysis, expert system analysis and BP neural network. That is an expert system based on fault tree and rules. At the same time, the BP neural network prediction model of special vehicle is established to predict the possible faults of special vehicle. On the basis of analyzing the actual demand of special vehicle, this paper designs the system structure and software framework of the special vehicle fault prediction and health management system, and analyzes the original test data of the special vehicle. Then, on the basis of analyzing the fault mode of special vehicle, the fault tree model of special vehicle is established, and the node attribute of the fault tree is configured. The graphics drawing and displaying function based on CStatic control is realized. Finally, on the basis of analyzing the historical running data of special vehicle, the health grade sample of special vehicle is established. The BP neural network prediction model of the special vehicle is trained and optimized by the established samples, and the trained BP neural network prediction model is solidified and embedded into the software platform. There are two main innovations in this paper: first, the generality of special vehicle fault prediction and health management software platform, such as the introduction of a series of configuration files, such as algorithm configuration file, rule configuration file and fault tree configuration file, etc. So that the software platform does not depend on a specific special vehicle, as long as there are data files and related configuration files, any series of special vehicles can be managed healthily. The second is the graphical display method of fault tree based on CStatic control. Combined with the node properties of the fault tree and the CStatic control, the graph of the fault tree can be visualized and diversified. Through this method, the automatic drawing of the fault tree can be realized, and the user can browse the fault tree according to his own requirements. The fault prediction and health management system is verified by the historical running data of the special vehicle. The diagnosis and prediction results of the software reflect the generality and practicability of the system and meet the actual needs.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：V55;TP311.52

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