本文選題：渦輪泵 + 故障檢測　； 參考：《電子科技大學(xué)》2015年碩士論文

【摘要】：渦輪泵是液體火箭發(fā)動機(jī)的核心部件,具有高昂的研制成本。渦輪泵惡劣的工作環(huán)境導(dǎo)致其具有很高故障率。因此,研究渦輪泵故障檢測技術(shù),對降低渦輪泵故障損失具有重大意義。目前,基于渦輪泵殼體振動信號的故障檢測算法是該領(lǐng)域的一個研究熱點。首先,總結(jié)并闡述了液體火箭發(fā)動機(jī)故障檢測技術(shù)的國內(nèi)外研究現(xiàn)狀和渦輪泵故障檢測算法的相關(guān)理論。在此基礎(chǔ)上,以某型號液體火箭發(fā)動機(jī)渦輪泵歷史試車的殼體振動加速度信號為研究對象,分別研究了兩種基于支持向量機(jī)的渦輪泵故障檢測算法。第一個算法是基于時域特征和快速支持向量機(jī)的渦輪泵故障檢測算法。該算法以樣本步長信號的能量和能量變化絕對值作為時域特征。同時,為解決由于訓(xùn)練樣本過多所導(dǎo)致的訓(xùn)練緩慢甚至無法訓(xùn)練的問題,引入快速支持向量機(jī)方法,從原始訓(xùn)練樣本集中篩選邊界訓(xùn)練樣本集,確保了決策分類函數(shù)的準(zhǔn)確性,而且大大縮短了訓(xùn)練時間。同時,提出了一種多指標(biāo)加權(quán)報警策略,為該算法判斷檢測步長信號中是否含有故障提供了依據(jù)。第二個算法是基于頻域特征和模糊分類支持向量機(jī)的渦輪泵故障檢測算法。該算法以樣本步長信號的頻段幅值標(biāo)準(zhǔn)差作為頻域特征,將一個樣本步長信號的頻譜分成若干頻段,計算各個頻段幅值標(biāo)準(zhǔn)差并構(gòu)造成一個向量,作為一個訓(xùn)練(檢測)樣本。同時,引入模糊分類支持向量機(jī)的方法,通過構(gòu)造故障隸屬度函數(shù),以實現(xiàn)對故障檢測樣本的故障隸屬度的計算。并且,將故障隸屬度用于剔除誤分故障檢測樣本,從而實現(xiàn)算法對虛警風(fēng)險的控制,提高算法的準(zhǔn)確性。經(jīng)實驗驗證,以上兩種渦輪泵故障檢測算法均滿足算法驗證的準(zhǔn)確性、實時性和及時性要求,對于改善液體火箭發(fā)動機(jī)渦輪泵試車的安全性,以及減少渦輪泵故障損失,具有一定的效果和積極的意義。
[Abstract]:Turbine pump is the core component of liquid rocket engine and has high development cost. Turbine pump has a high failure rate due to its poor working environment. Therefore, it is of great significance to study turbine pump fault detection technology to reduce turbine pump fault loss. At present, fault detection algorithm based on vibration signal of turbine pump shell is a research hotspot in this field. Firstly, the research status of liquid rocket engine fault detection technology at home and abroad and the relevant theory of turbine pump fault detection algorithm are summarized and expounded. On this basis, two turbine pump fault detection algorithms based on support vector machine are studied based on the vibration acceleration signal of the turbine pump of a liquid rocket engine. The first algorithm is based on time domain feature and fast support vector machine (SVM). The time domain feature of the algorithm is the absolute value of the energy and energy variation of the sample step-size signal. At the same time, in order to solve the problem that the training is slow or unable to train due to too many training samples, the fast support vector machine (FSVM) method is introduced to screen the boundary training samples from the original training samples to ensure the accuracy of the decision classification function. And greatly shortened the training time. At the same time, a multi-index weighted alarm strategy is proposed, which provides a basis for the algorithm to judge whether there are faults in the detection step size signal. The second algorithm is based on frequency domain feature and fuzzy classification support vector machine. In this algorithm, the frequency band standard deviation of the sample step size signal is taken as the frequency domain feature, the spectrum of a sample step size signal is divided into several frequency bands, the amplitude standard deviation of each frequency band is calculated and a vector is constructed as a training (detection) sample. At the same time, the method of fuzzy classification support vector machine is introduced, and the fault membership function is constructed to calculate the fault membership of fault detection samples. Furthermore, the fault membership degree is used to eliminate the fault detection samples, so that the algorithm can control the false alarm risk and improve the accuracy of the algorithm. The experimental results show that the above two turbine pump fault detection algorithms meet the accuracy, real-time and timeliness requirements of the algorithm verification, which can improve the safety of the liquid rocket engine turbine pump test operation and reduce the turbine pump failure loss. Have certain effect and positive meaning.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：V463
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本文編號：2061997