【摘要】：復(fù)合材料具有質(zhì)量輕、強度高、耐腐蝕等優(yōu)點,已經(jīng)廣泛應(yīng)用于軍事、航天、交通、電子電氣等領(lǐng)域。由于復(fù)合材料很容易遭受外來破壞而產(chǎn)生損傷,所以對復(fù)合材料進行損傷檢測就顯得很重要,目前用光纖光柵傳感器對復(fù)合材料進行損傷檢測是研究熱點之一,本文將開展光纖光柵傳感器采集信號,結(jié)合小波分析和神經(jīng)網(wǎng)絡(luò)的信號處理方法,實現(xiàn)復(fù)合材料的損傷檢測。本文先介紹了復(fù)合材料的研究背景和意義,然后分析了關(guān)于復(fù)合材料損傷檢測國內(nèi)外的研究現(xiàn)狀,同時分析了小波分析和神經(jīng)網(wǎng)絡(luò)在損傷檢測方面的應(yīng)用。接著介紹了小波變換的基本理論,研究了一種新的Lab VIEW和MATLAB混合編程的信號解調(diào)技術(shù),實現(xiàn)信號實時處理。結(jié)合復(fù)合材料板沖擊響應(yīng)信號的特點,研究了信號的小波包能量譜分析方法,能夠提取到信號不同頻率上的特征信息,為提取損傷特征向量提供理論了依據(jù)。隨后進行復(fù)合材料的沖擊實驗,研究了傳感器布設(shè)與響應(yīng)信號之間的關(guān)系,結(jié)果表明當傳感器的軸線與沖擊點和傳感器中心連線垂直且距離沖擊點較近布設(shè)時傳感效果最好,之后在復(fù)合材料板上模擬損傷得到不同損傷狀況時的沖擊響應(yīng)信號,通過小波包能量譜分析得到了信號的損傷特征向量。最后,結(jié)合從四個傳感信號提取的損傷特征值和復(fù)合材料板不同的損傷工況,組成BP神經(jīng)網(wǎng)的學習樣本,提出了通過小波包分析得到信號能量譜提取損傷特征向量,輸入BP神經(jīng)網(wǎng)絡(luò)進行損傷識別的方法,構(gòu)建了一套結(jié)合小波分析和神經(jīng)網(wǎng)絡(luò)的復(fù)合材料損傷檢測系統(tǒng),進行復(fù)合材料的損傷檢測。
[Abstract]:Because of its advantages of light weight, high strength and corrosion resistance, composite material has been widely used in military, aerospace, transportation, electronic and electrical fields. The damage detection of composite material is very important because it is easy to be damaged by external damage. At present, it is one of the research hotspots to use fiber Bragg grating sensor to detect the damage of composite material. In this paper, the fiber Bragg grating sensor will be used to detect the damage of composite materials by combining wavelet analysis and neural network signal processing. This paper first introduces the research background and significance of composite materials, then analyzes the research status of composite damage detection at home and abroad, at the same time, analyzes the application of wavelet analysis and neural network in damage detection. Then, the basic theory of wavelet transform is introduced, and a new signal demodulation technique of mixed programming of Lab VIEW and MATLAB is studied to realize the real-time signal processing. According to the characteristics of composite plate impulse response signal, the wavelet packet energy spectrum analysis method of the signal is studied, which can extract the characteristic information of the signal at different frequencies and provide the theoretical basis for extracting the damage characteristic vector. The relationship between sensor placement and response signal is studied. The results show that the sensor is the best when the axis of the sensor is perpendicular to the impact point and the center of the sensor is connected and the sensor is close to the impact point. Then the shock response signals of different damage conditions are obtained by simulating the damage on the composite plate, and the damage characteristic vectors of the signals are obtained by wavelet packet energy spectrum analysis. Finally, according to the damage characteristic values extracted from four sensing signals and the different damage conditions of composite plates, a BP neural network learning sample is formed, and the signal energy spectrum is proposed to extract the damage feature vector by wavelet packet analysis. The method of BP neural network is inputted to identify the damage, and a composite damage detection system combining wavelet analysis and neural network is constructed to detect the damage of composite material.
【學位授予單位】：南京航空航天大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TB33;TP18

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