超声波管道内检测腐蚀缺陷分类识别研究

Research of Classification and Recognition of Pipeline Corrosion Defects on Ultrasonic In-line Inspection

超声波检测是输油管道在线内检测的重要方法之一,管道腐蚀使管道壁产生大量的突变界面,使超声检测回波信号复杂,其分类是一个高维分类问题。利用支持向量机在解决小样本、非线性、高维模式识别中特有的优势,直接采用表征超声回波形态的A扫描数据作为特征向量,将特征提取与模式分类统一进行,建立了管道腐蚀检测信号分类的二次规划优化模型,并利用优化函数求得该问题的最优解,根据最优解建立了用于回波信号分类判断的分类决策函数,实现管道腐蚀缺陷识别。实验表明基于支持向量机的管道腐蚀超声内检测信号分类识别方法可以分类识别管道腐蚀产生的突变界面和腐蚀裂纹。

The ultrasonic detection is one of the important ways to inspect the wall-loss defects in-line for the oil pipeline. Pipeline corrosion defects make the rough surfaces of pipelines into break interfaces. The interfaces make the ultrasonic echoes complicated. The recognition of the echoes is a high-dimensional classification problem. To solve the problem, a further analysis of ultrasonic echo signals was carried out, showing that the method of measuring pipeline wall thickness with the time interval of the proceeding two ultrasonic echo signals brings about high fall-out and omission ratios. An effective method based on Support Vector Machine （SVM） what is suitable for small-sample, non-linear and high-dimensional recognition for classification and recognition of pipeline corrosion defects was put forward. The ultrasonic A-scan time-series were considered characteristic vectors. With unified considering the characteristic extraction and corrosion defects recognition the quadratic programming problem model of ultrasonic echo signals classification was established. The answer of this problem was got by using the optimization function. The classified decision function of ultrasonic echo signals was established. Experiments show that the classified result of break interfaces of pipelines and corrosive crack is accurate and clear and the method is suitable for in-line detection of pipeline corrosion defects.