管道漏磁检测缺陷识别技术

针对神经网络法对管道缺陷进行识别存在所需样本数据量大、容易陷入局部极值、泛化能力没有保证等问题，提出一种新的机器学习方法，即支持向量机法.介绍了支持向量机的分析原理，以碗状缺陷为例，采用ANSYS有限元分析软件对40组不同尺寸的缺陷进行仿真，将得到的40组漏磁数据和10组实测数据作为学习样本，另外10组新数据作为验证数据，以MATLAB软件为平台进行了识别实验.实验结果表明，识别误差均在5％以下，且泛化能力强，识别方法简单，在有限样本情况下比神经网络法更具优势.

Defect recognition technology of magnetic flux leakage detection for pipeline

With performing the pipeline defect recognition using the previous neural network method,a great amount of samples are required,the local extremum is easily attained and the generalizable ability can not be ensured. Thus,a novel machine learning method,support vector machine (SVM) method,was proposed. The analysis principle of the SVM was introduced. With taking the bowl shape defect as the example,the simulation for 40 groups of defects with different dimension was conducted using ANSYS finite element anal...