对油气管道进行定期检测，准确地评估管道运行状况对于基础设施安全和经济成本等都有着重大的意义。漏磁信号识别分析是油气管道漏磁检测数据分析中的关键一环，它对于管道运行状况的准确评估有重要作用。因此，本文针对油气管道漏磁检测中的管道特征和缺陷的自动识别问题，开展了基于大数据分析的管道特征和缺陷识别方法研究。提出了基于卷积神经网络-支持向量机的管道特征识别方法。将卷积神经网络强大的特征提取能力与支持向量机作为分类器优异的泛化能力结合起来，实现管道特征部件的精确识别。通过实际的油气管道检测数据进行验证，本文方法平均准确率为99.42%，4种管道特征识别的平均灵敏度和非目标样本识别的平均特异度均大于99%，明显优于传统的基于卷积神经网络方法，说明了本文方法可以对管道特征部件完成精确识别。提出了基于支持向量机的倾斜缺陷识别方法。倾斜缺陷与非倾斜缺陷在漏磁信号特征上存在较大的区别。因此，通过建立三维有限元仿真模型，分析了缺陷的漏磁信号的轴向分量、径向分量、周向分量随倾斜角度和斜向宽度变化的特点。在此基础上，提出了基于周向分量峰值的缺陷倾斜判定方法和基于轴向分量峰值拟合和周向分量峰值拟合的缺陷倾斜角估计方法。研究并定义了6个与缺陷斜向宽度相关的特征量，联合两个估计得到的倾斜角作为支持向量机的输入特征向量，设计了基于支持向量机的倾斜缺陷识别方法。以识别准确率和灵敏度为评价指标，仿真实验结果显示，本文倾斜缺陷识别方法可以有效地完成倾斜缺陷的识别。提出了基于LightGBM（Light Gradient Boosting Machine，LightGBM）的非倾斜缺陷识别方法。该方法包括两个关键步骤：第一，提出基于非线性最小二乘估计将漏磁测量信号不同分量的多通道信号的重要特征值分别进行联合提取，并辅以连续小波变换得到缺陷信号的2个尺度因子特征，为缺陷识别提供更多有效信息；第二，设计了基于LightGBM算法的非倾斜缺陷识别方法，可以更精确地完成漏磁检测中的缺陷识别。利用识别准确率和灵敏度对算法进行评价，实验结果显示，本文识别方法在实际牵拉数据集上识别平均准确率和7种金属损失缺陷的识别灵敏度明显优于传统识别方法。证明本文方法可以有效地解决漏磁检测中的油气管道非倾斜缺陷识别问题.

Regular inspection of oil and gas pipelines and accurate assessment of pipeline operating conditions are of great significance to infrastructure safety and economic costs. MFL signal identification and analysis is a key part of MFL detection data analysis of oil and gas pipelines, and it plays an important role in the accurate assessment of pipeline operating conditions. Therefore, in this paper, aiming at the automatic identification of pipeline characteristics and defects in MFL inspection of oil and gas pipelines, a research on the method of pipeline characteristics and defect identification based on big data analysis is carried out.A pipeline characteristics recognition method based on convolutional neural network-support vector machine is proposed. The powerful feature extraction ability of convolutional neural network is combined with the excellent generalization ability of support vector machine as a classifier to achieve accurate identification of pipeline feature components. Verified by the actual oil and gas pipeline detection data, the average accuracy of the method in this paper is 99.42%, the average sensitivity of the four kinds of pipeline characteristics recognition and the average specificity of non-target sample recognition are both greater than 99%, which is obviously better than the traditional method based on convolutional neural network. The network method shows that the method in this paper can accurately identify the pipeline feature components.A method of slanted defect recognition based on support vector machine is proposed. There is a big difference between the slanted defect and the non- slanted defect in the magnetic flux leakage signal characteristics. Therefore, through the establishment of a three-dimensional finite element simulation model, the characteristics of the axial, radial and circumferential components of the defect flux leakage signal varying with the angle of inclination and the slanted width are analyzed. On this basis, a method for determining the angle of inclination of defects based on the peak value of the circumferential component and a method for estimating the slanted angle of the defect based on the peak fitting of the axial component and the peak fitting of the circumferential component are proposed. Six features related to the slanted width of defects are studied and defined. Combined with the two estimated slanted angles as input feature vectors of support vector machine, a support vector machine-based slanted defect recognition method is designed. Taking the recognition accuracy and sensitivity as the evaluation indicators, the simulation results show that the slanted defect identification method in this paper can effectively complete the identification of slanted defects.A non-slanted defect recognition method based on LightGBM (Light Gradient Boosting Machine, LightGBM) is proposed. The method includes two key steps: Firstly, it is proposed to jointly extract the important eigenvalues of the multi-channel signals of different components of the flux leakage measurement signal based on nonlinear least squares estimation, and supplemented by continuous wavelet transform to obtain 2 scale factors of the defect signal, which provides more effective information for defect identification; secondly, a defect identification method based on LightGBM algorithm is designed, which can more accurately complete defect identification in magnetic flux leakage detection. The algorithm is evaluated by the recognition accuracy and sensitivity. The experimental results show that the recognition method in this paper is significantly better than the traditional recognition method in terms of the average accuracy and the recognition sensitivity of 7 kinds of metal loss defects on the actual pulling data set. It is proved that the method in this paper can effectively solve the problem of non-slanted defect identification of oil and gas pipelines in magnetic flux leakage detection.