【摘要】：聲發(fā)射檢測(cè)技術(shù)作為近些年來發(fā)展起來的一種無損檢測(cè)技術(shù)，正以其特有的優(yōu)勢(shì)受到了廣泛的關(guān)注。本文從不同方面研究了聲發(fā)射檢測(cè)技術(shù)在故障診斷領(lǐng)域中的應(yīng)用，主要內(nèi)容如下： 對(duì)聲發(fā)射及聲發(fā)射技術(shù)相關(guān)理論的基本知識(shí)、概念和特點(diǎn)及聲發(fā)射信號(hào)的相關(guān)處理方法進(jìn)行了介紹。闡述了常用聲發(fā)射信號(hào)定位方法的原理，，并根據(jù)四傳感器陣列平面定位原理結(jié)合MATLAB程序，實(shí)現(xiàn)了平面聲發(fā)射源定位。通過實(shí)驗(yàn)驗(yàn)證了定位程序的有效性，同時(shí)分析了影響定位精度的因素。 研究了軸承聲發(fā)射信號(hào)的不同處理與分析方法，實(shí)現(xiàn)了滾動(dòng)軸承的故障診斷。主要利用時(shí)域相關(guān)特征參數(shù)分析了軸承在不同狀態(tài)下所產(chǎn)生聲發(fā)射信號(hào)的特征；通過傳統(tǒng)傅里葉變換的方法，在頻域處理與分析了軸承聲發(fā)射信號(hào)；針對(duì)小波分析在時(shí)頻分析中的優(yōu)勢(shì)，采用小波分析對(duì)軸承聲發(fā)射信號(hào)進(jìn)行分析。重點(diǎn)研究了基于BP神經(jīng)網(wǎng)絡(luò)的智能診斷方法，提出了一種基于主成分分析與粗糙集相結(jié)合的屬性約簡(jiǎn)方法，并具體介紹了該方法的實(shí)現(xiàn)過程，最后利用約簡(jiǎn)后的參數(shù)輸入到BP神經(jīng)網(wǎng)絡(luò)進(jìn)行故障類型識(shí)別。同時(shí)，通過對(duì)實(shí)驗(yàn)數(shù)據(jù)進(jìn)行分析，驗(yàn)證了該方法能夠有效提高神經(jīng)網(wǎng)絡(luò)收斂速度和識(shí)別精度。 闡述了聲發(fā)射技術(shù)分別在疲勞裂紋檢測(cè)、壓力容器檢測(cè)和變壓器局部放電檢測(cè)中的應(yīng)用原理、特點(diǎn)與方法。結(jié)合實(shí)例詳細(xì)介紹了聲發(fā)射技術(shù)在三種情況下的檢測(cè)過程，從實(shí)際的角度證明了聲發(fā)射技術(shù)在這幾方面應(yīng)用的有效性。
[Abstract]:As a kind of nondestructive testing technology developed in recent years, acoustic emission (AE) detection technology has attracted wide attention due to its unique advantages. In this paper, the application of acoustic emission detection technology in fault diagnosis is studied from different aspects. The main contents are as follows: the basic knowledge of acoustic emission and acoustic emission technology related theory, The concept and characteristics of acoustic emission signal are introduced. The principle of commonly used acoustic emission (AE) signal localization method is described. According to the four sensor array plane positioning principle and MATLAB program, the planar acoustic emission source location is realized. The validity of the program is verified by experiments, and the factors influencing the positioning accuracy are analyzed. The different processing and analysis methods of bearing acoustic emission signal are studied, and the fault diagnosis of rolling bearing is realized. The characteristics of acoustic emission signals produced by bearings in different states are analyzed by using time-domain correlation characteristic parameters, and the acoustic emission signals of bearings are processed and analyzed in frequency domain by the traditional Fourier transform method. Aiming at the advantage of wavelet analysis in time frequency analysis, wavelet analysis is used to analyze the acoustic emission signal of bearing. This paper focuses on the intelligent diagnosis method based on BP neural network, proposes a attribute reduction method based on principal component analysis and rough set, and introduces the realization process of this method in detail. Finally, the reduced parameters are input into the BP neural network for fault type identification. At the same time, through the analysis of the experimental data, it is proved that this method can effectively improve the convergence speed and recognition accuracy of the neural network. The application principle, characteristics and methods of acoustic emission technology in fatigue crack detection, pressure vessel detection and transformer partial discharge detection are described. The detection process of acoustic emission technology in three cases is introduced in detail with an example, and the effectiveness of acoustic emission technology in these aspects is proved from the practical point of view.
【學(xué)位授予單位】：北京化工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TH165.3

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