發(fā)布時間：2018-04-25 15:30

  本文選題：空間碎片 + 超高速撞擊��； 參考：《航空學(xué)報》2017年05期

【摘要】：為通過聲發(fā)射技術(shù)識別鋁合金蜂窩板超高速撞擊(HVI)的損傷狀態(tài),提出一種基于神經(jīng)網(wǎng)絡(luò)的損傷模式識別方法。通過超高速撞擊實驗獲取聲發(fā)射信號,結(jié)合精確源定位技術(shù)、時頻分析技術(shù)、小波分析技術(shù)及模態(tài)聲發(fā)射技術(shù),提出了10個與損傷相關(guān)的特征參數(shù),通過非參數(shù)檢驗分析其與損傷的關(guān)系,設(shè)計了一種基于貝葉斯正則化BP神經(jīng)網(wǎng)絡(luò)的超高速撞擊損傷模式識別方法。建立最優(yōu)網(wǎng)絡(luò)模型,通過不同參數(shù)組合識別能力分析,優(yōu)選出2種特征參數(shù)組合,通過非同源樣本對其損傷模式識別能力進(jìn)行驗證。結(jié)果表明:傳播距離與損傷模式無關(guān),卻是識別損傷模式的重要參數(shù);125~250kHz頻域的自動加窗小波能量比會降低損傷模式的識別能力;采用貝葉斯正則化的BP神經(jīng)網(wǎng)絡(luò)可以較好地識別蜂窩板超高速撞擊損傷模式,參數(shù)組合為傳播距離、上升時間、持續(xù)時間、截止頻率、4個自動加窗小波能量比及小波能量熵,共9個參數(shù),對任意選取非同源樣本識別錯分率僅為9.38%。
[Abstract]:In order to identify the damage state of the hypervelocity impact (HVI) of aluminum alloy honeycomb panel by acoustic emission technology, a method of damage pattern recognition based on neural network is proposed. The acoustic emission signals are obtained by ultra high speed impact test, combined with the precise source location technology, time frequency analysis, small wave analysis and modal acoustic emission technology, 10 of which are presented. The relationship between damage and damage is analyzed by nonparametric test. A model identification method for hypervelocity impact damage based on Bayesian regularization BP neural network is designed. The optimal network model is established. Through the analysis of different parameters combination recognition ability, 2 combination of characteristic parameters is optimized and the non homologous sample is used. The results show that the propagation distance is independent of the damage mode, but it is an important parameter to identify the damage mode, and the automatic adding window wavelet energy ratio in the 125~250kHz frequency domain can reduce the recognition ability of the damage mode, and the BP neural network with Bayesian regularization can identify the hypervelocity impact damage of the honeycomb plate. The parameter combination is the propagation distance, the rising time, the duration, the cut-off frequency, the 4 automatic window wavelet energy ratio and the wavelet energy entropy, which are 9 parameters, and the error rate is only 9.38%. for the arbitrary selection of non homologous samples.

【作者單位】： 哈爾濱工業(yè)大學(xué)航天學(xué)院;
【基金】：國家“十二五”空間碎片專項(K0203210) 中央高�；究蒲袠I(yè)務(wù)費專項資金(HIT.NSRIF.2015029)~~
【分類號】：V528

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本文編號：1801916