Artificial neural network (ANN) is adopted to implement automatic flaw identification of pipeline girth weld.
采用人工神经网络技术实现管道环焊缝缺陷识别。
参考来源 - 超声相控阵油气管道环焊缝缺陷检测技术的研究In order to realize the flaw identification of link-thread bolt, it establised the flawidentification method which depended on the local maxima abstracted from detecting signal.
为实现螺纹区域中的缺陷检测,提出了提取局部极大值的缺陷识别方法;并根据位置的不同将缺陷分为螺杆结合部缺陷、螺纹区域缺陷以及螺纹尾部缺陷三类,对每类缺陷的信号特点进行了分析,给出了缺陷检测的判据,并通过实验验证了识别方法的可靠性;最后对螺栓头杆结合部的缺陷检测进行了实验研究,并根据信号特点建立了相应的缺陷检测方法。
参考来源 - 基于场量测量和频率扫描技术的电磁无损检测技术研究·2,447,543篇论文数据,部分数据来源于NoteExpress
The method of 2-d flaw identification using elastic wave is studied in this paper.
研究了一种利用弹性波识别二维缺陷形状的方法。
The ultrasonic testing experiments for oil pipeline flaw show that it has high identification accuracy, fine generation and easy implement on-line.
通过超声石油管线缺陷大量检测实验表明:该方法具有准确率高、推广性强、容易在线实施等优点。
The waveform of flaw signals from the rod is identified by the correlation method and Euclidean distance method. Based on these two methods, a comprehensive identification method is brought up.
采用相关系数法、欧氏距离法对这两类缺陷信号的的波形进行模式识别,以这两种方法为基础提出一种对此类缺陷信号综合识别方法。
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