本文選題：鋼管 + 超聲檢測　； 參考：《鋼鐵研究總院》2014年碩士論文

【摘要】：在利用超聲波在對鋼管進行檢測時,其中影響超聲檢測效果的一個主要因素就是鋼管的表面狀況。如果鋼管材料的表面狀況很差,粗糙度高,超聲波會在材料的檢測表面發(fā)生反射、折射作用,這對超聲波造成很大的衰減并使得超聲回波信號伴隨有大量噪聲信號,降低了超聲檢測的靈敏度和信噪比,嚴重影響后續(xù)對超聲檢測缺陷信號的判別以及定性、定量分析,影響超聲探傷結果的準確性。 本文結合課題的研究目的和方法,利用現有的實驗設備,自行搭建一套完整的超聲檢測信號處理系統,并在Windows操作系統下,通過美國國家儀器(NI)提供的LabVIEW開發(fā)軟件設計出了基于虛擬儀器的超聲檢測信號處理平臺。該系統可以激勵超聲探頭發(fā)射超聲波信號,利用數據采集卡完成超聲檢測回波信號的模／數轉換,并通過USB串口總線把所采集的數據上傳到計算機內。在計算機上開發(fā)的基于虛擬儀器的超聲檢測信號處理平臺可以對數據采集卡進行狀態(tài)控制和參數設置,并對所讀取的超聲檢測回波信號做相應的處理工作。 利用自行搭建的基于虛擬儀器超聲檢測信號處理系統,對表面粗糙鋼管的超聲檢測信號進行了相關的處理工作。對表面粗糙鋼管的信號處理工作主要包括基于傳統傅立葉變換的信號分析處理工作和基于小波變換的信號分析處理工作�；趥鹘y傅立葉變換的信號分析處理工作主要有：①對表面粗糙鋼管的超聲檢測信號進行快速傅立葉變換(FFT),完成表面粗糙鋼管超聲檢測信號的頻譜分析；②在頻譜分析的基礎上,對表面粗糙鋼管的超聲檢測信號做有限長單位沖激響應(FIR)加窗濾波處理,并對所加不同窗函數的濾波效果進行比較分析；③利用短時傅立葉變換(STFT)對表面粗糙鋼管的超聲檢測信號進行時頻分析.基于小波變換的信號分析處理工作主要有：①利用小波變換(WT)對表面粗糙鋼管的超聲檢測信號進行時頻分析,并與短時傅立葉變換的時頻分析結果相比較；②利用小波閾值去噪法對表面粗糙鋼管的超聲檢測信號進行去噪處理,并對軟、硬閾值法去噪的結果進行比較分析。 研究結果表明,在利用超聲檢測技術對表面狀況達不到標準規(guī)定要求的鋼管材料進行檢測時,如果不進行任何降噪處理,由于檢測信噪比較低,很難實現缺陷的可靠檢出(工業(yè)探傷的信噪比一般應達到6dB～8dB以上)。而通過對檢測信號進行適當的濾波或去噪處理后,檢測信噪比得到了明顯的改善。但是傳統的基于傅立葉變換的FIR波技術,在消除噪聲的同時會把大量的有用信號也濾除掉,導致波形的失真；而基于小波變換的信號去噪處理,幾乎不會造成有用信號的損失,而且可以有效保持缺陷信號的特征形貌,達到更為理想的效果。這為今后表面粗糙鋼管的超聲檢測信號處理技術提供了很好的解決途徑。
[Abstract]:The surface condition of the steel tube is one of the main factors that affect the effect of ultrasonic testing. If the surface condition of steel tube is very poor and the roughness is high, the ultrasonic wave will reflect and refraction on the testing surface of the material, which will cause great attenuation to the ultrasonic wave and make the ultrasonic echo signal accompanied by a large number of noise signals. It reduces the sensitivity and signal-to-noise ratio of ultrasonic detection, seriously affects the discrimination, qualitative and quantitative analysis of ultrasonic flaw signals, and affects the accuracy of ultrasonic flaw detection results. Using the existing experimental equipment, a complete ultrasonic detection signal processing system is built by ourselves, and under Windows operating system, The ultrasonic signal processing platform based on virtual instrument is designed by LabVIEW software provided by the National instrument of USA. The system can excite the ultrasonic probe to transmit the ultrasonic signal, use the data acquisition card to complete the A / D conversion of the ultrasonic detection echo signal, and upload the collected data to the computer through the USB serial port bus. The ultrasonic signal processing platform based on virtual instrument developed on the computer can control the state and set the parameters of the data acquisition card. The ultrasonic signal processing system based on virtual instrument is used to process the ultrasonic signal of rough steel pipe. The signal processing of rough surface steel pipe mainly includes the signal analysis and processing based on the traditional Fourier transform and the signal analysis and processing based on the wavelet transform. The main work of signal analysis and processing based on traditional Fourier transform is to perform fast Fourier transform (FFTT) on ultrasonic detection signal of rough surface steel pipe with 1: 1, and to complete the spectrum analysis of ultrasonic detection signal of rough surface steel pipe. (2) on the basis of spectrum analysis, the ultrasonic detection signal of rough surface steel pipe is filtered by finite length unit impulse response (FIR), and the filtering effect of different window function is compared and analyzed. (3) the short time Fourier transform (STFT) was used to analyze the ultrasonic signal of rough steel pipe. The work of signal analysis and processing based on wavelet transform mainly includes: (1) using wavelet transform (WTT) to analyze the ultrasonic detection signal of rough steel pipe, and comparing the results with that of STFT. 2De-noising the ultrasonic signal of rough surface steel pipe by wavelet threshold de-noising method, and comparing and analyzing the results of soft and hard threshold de-noising. When ultrasonic testing technology is used to detect steel tube material whose surface condition is not up to the standard, if no noise reduction is done, the signal-to-noise ratio (SNR) of the detection is low. It is difficult to detect defects reliably. (the SNR of industrial flaw detection should be above 6 dB or more than 8 dB. The signal-to-noise ratio (SNR) of the detected signal is obviously improved after proper filtering or de-noising of the detected signal. However, the traditional Fir technology based on Fourier transform can eliminate the noise and filter out a large number of useful signals, which results in the distortion of the waveform, while the signal de-noising based on wavelet transform can hardly cause the loss of useful signals. Moreover, it can effectively maintain the feature and morphology of the defect signal, and achieve a more ideal effect. This provides a good solution for ultrasonic signal processing technology of surface rough steel pipe in the future.
【學位授予單位】：鋼鐵研究總院
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TB553;TN911.7

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