發(fā)布時(shí)間：2018-04-30 10:17

  本文選題：腐蝕 + 聲發(fā)射　； 參考：《大連理工大學(xué)》2015年碩士論文

【摘要】：斜拉橋是現(xiàn)代大跨度橋梁的主要類型之一,拉索是斜拉橋的重要承重構(gòu)件。由于其長(zhǎng)期暴露在外界環(huán)境之中,容易遭受氯鹽等腐蝕介質(zhì)的侵蝕。長(zhǎng)年累月,拉索結(jié)構(gòu)不可避免地會(huì)產(chǎn)生一定程度的損傷,導(dǎo)致力學(xué)性能衰減,使用壽命也會(huì)逐年縮短。在極端情況下,拉索發(fā)生斷裂導(dǎo)致橋梁坍塌,引起災(zāi)難性的事故。因此,對(duì)橋梁拉索的腐蝕損傷進(jìn)行監(jiān)測(cè)是非常有必要的。聲發(fā)射技術(shù)作為一種具有高靈敏度的無(wú)損檢測(cè)技術(shù)用于監(jiān)測(cè)橋梁拉索腐蝕損傷被證明是一種有效的方法,本文通過(guò)用聲發(fā)射技術(shù)對(duì)拉索的腐蝕損傷過(guò)程進(jìn)行實(shí)時(shí)監(jiān)測(cè),然后借助于信號(hào)處理分析技術(shù)實(shí)現(xiàn)聲發(fā)射源的識(shí)別,進(jìn)而合理評(píng)價(jià)拉索的腐蝕損傷。本文主要研究?jī)?nèi)容如下：1.通過(guò)拉索加速腐蝕實(shí)驗(yàn),獲取腐蝕全過(guò)程聲發(fā)射信號(hào)。根據(jù)聲發(fā)射撞擊數(shù)和計(jì)數(shù)與時(shí)間的相關(guān)圖,得到了拉索腐蝕損傷演化規(guī)律。為了確定拉索腐蝕過(guò)程中不同的損傷源,發(fā)展了基于主成分分析算法(PCA)提取聲發(fā)射監(jiān)測(cè)數(shù)據(jù)的特征值,應(yīng)用全局尋優(yōu)聚類分析算法對(duì)監(jiān)測(cè)獲得的大量數(shù)據(jù)進(jìn)行信息挖掘、統(tǒng)計(jì)分析歸類,找出相似損傷階段的特征規(guī)律并分析拉索在腐蝕過(guò)程中不同損傷階段的聲源特性。2.根據(jù)拉索腐蝕損傷不同聲發(fā)射源的聚類分析結(jié)果,提取不同聚類類型的聲發(fā)射監(jiān)測(cè)信號(hào)進(jìn)行波形分析。由于聲發(fā)射信號(hào)的非平穩(wěn)特性,本文通過(guò)對(duì)比現(xiàn)行的各種時(shí)頻分析技術(shù),選擇效果最好的基于經(jīng)驗(yàn)?zāi)B(tài)分解(EMD)的Hilbert-Huang變換(HHT),將其應(yīng)用于聲發(fā)射監(jiān)測(cè)技術(shù)中。通過(guò)研究HHT算法,應(yīng)用端點(diǎn)延拓方法解決EMD中的端點(diǎn)效應(yīng)；采用基于約束樣條插值的包絡(luò)擬合算法處理傳統(tǒng)EMD中應(yīng)用三次樣條函數(shù)擬合過(guò)程中產(chǎn)生的過(guò)沖和欠沖問題；針對(duì)EMD模態(tài)混迭效應(yīng),采用經(jīng)過(guò)改進(jìn)后的總體經(jīng)驗(yàn)?zāi)B(tài)分解(EEMD)應(yīng)對(duì),最后分析拉索不同腐蝕損傷階段時(shí)信號(hào)的Hilbert譜特征。得到拉索腐蝕不同損傷源下聲發(fā)射信號(hào)時(shí)域特征及其不同頻率帶上的能量分布。3.為了將拉索腐蝕聲發(fā)射源聚類分析可視化、直觀表征。創(chuàng)新地將自組織特征映像神經(jīng)網(wǎng)絡(luò)(SOFM)技術(shù)應(yīng)用到拉索腐蝕損傷的模式識(shí)別中,SOFM是基于無(wú)監(jiān)督學(xué)習(xí)方法的一種競(jìng)爭(zhēng)型神經(jīng)網(wǎng)絡(luò),通過(guò)對(duì)輸入模式進(jìn)行自組織訓(xùn)練和判斷,最終將數(shù)據(jù)分為不同的類型。通過(guò)對(duì)聲發(fā)射監(jiān)測(cè)數(shù)據(jù)的處理,自適應(yīng)得將聚類結(jié)果直觀地呈現(xiàn)在映射到二維輸出層上,實(shí)現(xiàn)聲發(fā)射不同聲源的識(shí)別。
[Abstract]:Cable-stayed bridge is one of the main types of modern long-span bridges, and cable is an important bearing member of cable-stayed bridge. Because it is exposed to external environment for a long time, it is vulnerable to corrosion by chloride and other corrosive media. Over the years, the cable structure will inevitably produce a certain degree of damage, resulting in mechanical performance attenuation, and the service life will be shortened year by year. In extreme cases, cable breaks lead to bridge collapse, causing catastrophic accidents. Therefore, it is necessary to monitor the corrosion damage of bridge cables. Acoustic emission (AE) technology, as a highly sensitive nondestructive testing technique, has been proved to be an effective method for monitoring the corrosion damage of bridge cables. In this paper, acoustic emission technology is used to monitor the corrosion damage process of cables in real time. Then the acoustic emission source identification is realized by signal processing and analysis technology, and the corrosion damage of cable is evaluated reasonably. The main contents of this paper are as follows: 1. The acoustic emission signals of the whole corrosion process were obtained by the cable accelerated corrosion experiment. According to the correlation diagram of acoustic emission impact number and counting and time, the evolution law of cable corrosion damage is obtained. In order to determine the different damage sources in the process of cable corrosion, the principal component analysis (PCA) algorithm was developed to extract the eigenvalues of acoustic emission monitoring data, and the global optimization clustering analysis algorithm was used to mine a large number of monitoring data. By statistical analysis and classification, the characteristics of similar damage stages are found out and the sound source characteristics of cable in different damage stages are analyzed. 2. According to the cluster analysis results of different acoustic emission sources of cable corrosion damage, acoustic emission monitoring signals of different clustering types were extracted for waveform analysis. Due to the non-stationary characteristics of acoustic emission signals, by comparing various time-frequency analysis techniques, the best Hilbert-Huang transform based on empirical mode decomposition (EMD) is selected and applied to acoustic emission monitoring technology. By studying the HHT algorithm, the endpoint continuation method is applied to solve the endpoint effect in EMD, and the envelope fitting algorithm based on constrained spline interpolation is used to deal with the overshoot and underimpact problems in the traditional EMD with cubic spline function fitting. In view of the EMD mode mixing effect, the improved total empirical mode decomposition (EEMD) is adopted to deal with the problem. Finally, the Hilbert spectrum characteristics of the signals at different corrosion damage stages of the cables are analyzed. The time domain characteristics of acoustic emission signals and the energy distribution in different frequency bands of cable corrosion under different damage sources are obtained. In order to visualize and visualize the cluster analysis of cable corrosion acoustic emission source. The self-organizing feature map neural network (SOFM) is applied to the pattern recognition of cable corrosion damage. SOFM is a competitive neural network based on unsupervised learning method. Finally, the data is divided into different types. By processing the acoustic emission monitoring data, the clustering results are presented intuitively on the 2-D output layer to realize the recognition of different acoustic emission sources.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U446;TP391.4

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本文編號(hào)：1824162