【摘要】：針對(duì)機(jī)載燃油泵故障數(shù)據(jù)少、診斷效率低、維護(hù)成本高、缺乏有效診斷方法的問(wèn)題,搭建了機(jī)載燃油泵燃油轉(zhuǎn)輸系統(tǒng)實(shí)驗(yàn)平臺(tái),提出利用小波包分析進(jìn)行特征提取和基于BP_AdaBoost機(jī)載燃油泵故障診斷方法。首先測(cè)量燃油泵7種典型狀態(tài)模式所對(duì)應(yīng)的振動(dòng)信號(hào)和出口壓力信號(hào);然后在分析信號(hào)時(shí)頻特性和統(tǒng)計(jì)特性的基礎(chǔ)上,利用小波包分解提取振動(dòng)信號(hào)不同頻段能量值作為故障特征參數(shù),結(jié)合振動(dòng)信號(hào)峭度以及壓力信號(hào)均值構(gòu)造特征向量;最后利用特征向量訓(xùn)練和驗(yàn)證BP_AdaBoost分類(lèi)模型。實(shí)驗(yàn)結(jié)果不僅優(yōu)化了傳感器,而且表明BP_Adaboost算法與SVM、BP算法相比,能夠有效實(shí)現(xiàn)對(duì)機(jī)載燃油泵的故障診斷。
[Abstract]:Aiming at the problems of low fault data, low diagnostic efficiency, high maintenance cost and lack of effective diagnostic methods, an experimental platform for fuel transfer system of airborne fuel pump is built. A method of feature extraction based on wavelet packet analysis and fault diagnosis of airborne fuel pump based on BP_AdaBoost is proposed. First, the vibration signal and outlet pressure signal corresponding to 7 typical state modes of fuel pump are measured. Then on the basis of analyzing the time-frequency and statistical characteristics of the signal, the wavelet packet decomposition is used to extract the energy values of different frequency bands of the vibration signal as the fault characteristic parameter, and the eigenvector is constructed by combining the kurtosis of the vibration signal and the mean value of the pressure signal. Finally, the feature vector is used to train and verify the BP_AdaBoost classification model. The experimental results not only optimize the sensor, but also show that the BP_Adaboost algorithm can effectively realize the fault diagnosis of the airborne fuel pump compared with the SVM,BP algorithm.
【作者單位】： 空軍工程大學(xué)航空航天工程學(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;
【基金】：航空科學(xué)基金(20142896022)項(xiàng)目資助
【分類(lèi)號(hào)】：V267
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本文編號(hào)：2405590