本文選題：超聲檢測 + 內(nèi)毛刺��； 參考：《遼寧科技大學(xué)》2017年碩士論文

【摘要】：油氣輸送過程中要求管道抗擠毀能力強(qiáng)、成本低。在工業(yè)生產(chǎn)中,為更好的預(yù)防因焊管的質(zhì)量而帶來的潛在安全隱患,延長焊管的使用壽命,對焊縫的質(zhì)量檢測需更加嚴(yán)格。隨著技術(shù)的發(fā)展,超聲波探傷技術(shù)在無損檢測領(lǐng)域應(yīng)用越來越廣。因?yàn)閭鹘y(tǒng)的時(shí)頻分析方法時(shí)頻分辨率不高,對信號所攜帶的信息很難充分分析利用,檢測精度和可靠性也沒有明顯提高。而超聲檢測信號的時(shí)頻局部化特征更能有效的描述其信號特點(diǎn),對超聲信號的分析、識別以及檢測精度的提升和可靠性的提高更加有效。在對焊管內(nèi)毛刺缺陷的工程實(shí)際超聲檢測中,始終不能百分之百的實(shí)現(xiàn)某一缺陷的定性分類,需要不斷的在這一領(lǐng)域進(jìn)行探索、研究,實(shí)現(xiàn)對缺陷特征量的提取�；诖�,本文利用MATLAB軟件對焊管內(nèi)毛刺的超聲檢測信號進(jìn)行時(shí)頻局部化分析,并對缺陷信號進(jìn)行特征量提取,為今后內(nèi)毛刺的檢測打下堅(jiān)實(shí)的基礎(chǔ)。在內(nèi)毛刺超聲檢測信號的研究過程當(dāng)中發(fā)現(xiàn),由于EEMD方法為克服傳統(tǒng)的EMD方法中存在的模式混疊問題,在分析處理信號前,需加入大量高斯白噪聲,這大大降低了EEMD分析信號的速度,將正交小波包作為EEMD方法的預(yù)濾波單元,有效地提高了其時(shí)效性。在實(shí)際的焊管內(nèi)毛刺清除過程中,由于刮刀位置的不同和使用時(shí)間的長短通常會(huì)出現(xiàn)各種類型的毛刺,其超聲檢測結(jié)果也存在較大差異,綜合幅值特征和厚度特征可判定有無毛刺及毛刺類型。由于超聲探頭在液體中的聲束指向性差,且存在其他干擾波的影響,采用中心頻率分別為2MHz和5MHz的水浸式線聚焦探頭對外毛刺刮削干凈、但內(nèi)毛刺未經(jīng)處理的ERW焊管進(jìn)行超聲信號的樣本采集,對焊管內(nèi)毛刺超聲檢測信號的缺陷特征量的提取和智能識別研究提供真實(shí)、可靠的分析數(shù)據(jù)。通過觀察實(shí)驗(yàn)所測得的焊管內(nèi)毛刺超聲回波信號的波形,已知缺陷在波形中對應(yīng)的采樣點(diǎn)數(shù)、單個(gè)采樣點(diǎn)所用時(shí)間、超聲波在介質(zhì)中的傳播速度以及超聲探頭的入射角,就可以準(zhǔn)確確定內(nèi)毛刺所在的位置和深度。由于超聲回波信號的部分有效信息淹沒在了大量噪聲當(dāng)中,采用時(shí)頻分辨率較高的EEMD方法有效地對信號進(jìn)行多尺度分解,獲得的結(jié)果完全可以體現(xiàn)原信號的信息特征。并結(jié)合基于Lorenz混沌系統(tǒng)的Volterra級數(shù)預(yù)測模型預(yù)測多尺度IMF信號的系統(tǒng)參數(shù),通過矩陣奇異值的計(jì)算,得到系統(tǒng)的最小二乘解,提高了預(yù)測精度,且求得的奇異值幾乎不受噪聲的影響,根據(jù)求得的奇異值大小可以有效地判斷焊管是否存在毛刺,驗(yàn)證了本文中所使用的EEMD-Volterra方法對內(nèi)毛刺檢測的正確性和有效性。
[Abstract]:In the process of oil and gas transportation, it is required that the pipeline has strong ability to resist squeezing damage and low cost. In industrial production, in order to prevent the potential hidden danger caused by the quality of welded pipe, prolong the service life of welded pipe, and test the quality of weld seam more strictly. With the development of technology, ultrasonic flaw detection technology is more and more widely used in the field of nondestructive testing. Because the time-frequency resolution of the traditional time-frequency analysis method is not high, it is difficult to fully analyze and utilize the information carried by the signal, and the accuracy and reliability of the detection are not obviously improved. The time-frequency localization feature of ultrasonic detection signal can describe the signal characteristics more effectively, and it is more effective for ultrasonic signal analysis, identification, detection accuracy and reliability. In the engineering practice of ultrasonic detection of burr defects in welded pipes, the qualitative classification of certain defects can not be realized 100%. It is necessary to continuously explore and study in this field, to achieve the extraction of defect characteristics. Based on this, this paper makes use of MATLAB software to analyze the ultrasonic detection signal of welded pipe internal burr by time-frequency localization, and extracts the characteristic quantity of defect signal, which lays a solid foundation for the detection of internal burr in the future. During the study of the internal burr ultrasonic signal, it is found that in order to overcome the mode aliasing problem in the traditional Gao Si method, a large amount of white noise should be added before the signal is analyzed and processed. This greatly reduces the speed of the EEMD analysis signal. The orthogonal wavelet packet is used as the pre-filter unit of the EEMD method, and its time-efficiency is improved effectively. In the actual internal burr removal process of welded pipe, because of the different positions of scraper and the length of service time, there are usually various types of burrs, and the ultrasonic testing results are also quite different. Comprehensive amplitude and thickness characteristics can be used to determine whether or not burr and burr type exist. Due to the poor directivity of ultrasonic probe in liquid and the influence of other interference waves, the water immersion wire focusing probe with central frequency of 2 MHz and 5 MHz was used to scratch the external burr. However, the untreated ERW pipe with internal burr is used to collect ultrasonic signal samples, to extract the defect characteristic quantity of ultrasonic detection signal in welded pipe and to provide real and reliable analysis data. By observing the waveform of the burr ultrasonic echo signal in the welded pipe, the sampling points corresponding to the known defects in the waveform, the time used for a single sampling point, the velocity of ultrasonic wave propagation in the medium and the incidence angle of the ultrasonic probe are observed. The position and depth of the internal burr can be determined accurately. Because part of the effective information of ultrasonic echo signal is submerged in a large number of noises, the EEMD method with high time-frequency resolution is used to decompose the signal effectively, and the obtained results can fully reflect the information characteristics of the original signal. Combining with the Volterra series prediction model based on Lorenz chaotic system to predict the system parameters of multi-scale IMF signal, the least square solution of the system is obtained by calculating the singular value of matrix, and the prediction accuracy is improved. The obtained singular value is almost unaffected by noise. According to the obtained singular value, the existence of burr in welded pipe can be effectively determined, and the correctness and validity of EEMD-Volterra method used in this paper for internal burr detection are verified.
【學(xué)位授予單位】：遼寧科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TE973.6

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