本文選題：燃油泵 + 樣本分位數(shù)�。� 參考：《航空學(xué)報(bào)》2016年09期

【摘要】：機(jī)載燃油泵的健康狀態(tài)關(guān)系著飛行任務(wù)的完成和飛行安全,對(duì)機(jī)載燃油泵的故障狀態(tài)特征提取及診斷成為亟需解決的問(wèn)題。通過(guò)對(duì)機(jī)載燃油實(shí)驗(yàn)系統(tǒng)的振動(dòng)與壓力信號(hào)進(jìn)行綜合分析,提出了一種基于樣本分位數(shù)的故障狀態(tài)特征提取方法。首先,根據(jù)樣本分位數(shù)的漸近分布定理,討論了樣本分位數(shù)的統(tǒng)計(jì)特性,分析了故障狀態(tài)與樣本分位數(shù)的對(duì)應(yīng)關(guān)系,從理論上保證了該方法的可行性,在實(shí)測(cè)數(shù)據(jù)統(tǒng)計(jì)分析的基礎(chǔ)上,討論了樣本容量對(duì)樣本分位數(shù)穩(wěn)定性的影響;其次,根據(jù)樣本分位數(shù)漸近分布定理計(jì)算各故障狀態(tài)的置信區(qū)間,并與Bootstrap方法得到的置信區(qū)間進(jìn)行對(duì)比,結(jié)果顯示,依據(jù)樣本分位數(shù)漸近分布定理得到的置信區(qū)間真實(shí)可靠,為在線故障診斷提供了依據(jù);然后,以各故障狀態(tài)下提取的樣本分位數(shù)為特征向量構(gòu)建貝葉斯判別函數(shù),進(jìn)行故障診斷;最后依據(jù)故障診斷的正確率對(duì)傳感器進(jìn)行優(yōu)化,結(jié)果表明,同時(shí)安裝振動(dòng)傳感器與壓力傳感器可以提高故障診斷的正確率,并且只安裝1個(gè)壓力傳感器與1個(gè)特定方向的振動(dòng)傳感器即可對(duì)機(jī)載燃油泵的故障狀態(tài)進(jìn)行完全識(shí)別。為快速準(zhǔn)確的在線判斷機(jī)載燃油泵的狀態(tài)提供了理論支撐,并且可以降低工程應(yīng)用中機(jī)載燃油泵監(jiān)測(cè)系統(tǒng)的體積、功耗及復(fù)雜性。
[Abstract]:The health state of the airborne fuel pump is related to the completion of the flight mission and flight safety. It is urgent to solve the problem of extracting and diagnosing the fault state characteristics of the airborne fuel pump. A fault state feature extraction method based on sample quantiles is proposed by synthetically analyzing the vibration and pressure signals of the airborne fuel oil experimental system. Firstly, according to the asymptotic distribution theorem of sample quantiles, the statistical characteristics of sample quantiles are discussed, and the corresponding relationship between fault state and sample quantiles is analyzed. The feasibility of this method is guaranteed theoretically. Based on the statistical analysis of measured data, the influence of sample size on the stability of sample quantiles is discussed. Secondly, the confidence intervals of each fault state are calculated according to the asymptotic distribution theorem of sample quantiles. Compared with the confidence interval obtained by Bootstrap method, the results show that the confidence interval obtained by the asymptotic distribution theorem of sample quantiles is true and reliable, which provides the basis for on-line fault diagnosis. The Bayesian discriminant function is constructed with the sample quantiles extracted from each fault state as the eigenvector to carry out fault diagnosis. Finally, the sensor is optimized according to the correct rate of fault diagnosis, and the results show that, The accuracy of fault diagnosis can be improved by installing vibration sensor and pressure sensor at the same time, and only one pressure sensor and one vibration sensor in specific direction can fully identify the fault state of airborne fuel pump. It provides a theoretical support for fast and accurate on-line judging of the status of airborne fuel pump, and can reduce the volume, power consumption and complexity of the monitoring system of airborne fuel pump in engineering application.
【作者單位】： 空軍工程大學(xué)航空航天工程學(xué)院;魯東大學(xué)數(shù)學(xué)與統(tǒng)計(jì)科學(xué)學(xué)院;中航工業(yè)金城南京機(jī)電液壓工程研究中心;航空機(jī)電系統(tǒng)綜合航空科技重點(diǎn)實(shí)驗(yàn)室;
【分類號(hào)】：V263.6

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本文編號(hào)：1793226