基于磁记忆的金属管道缺陷检测方法

Metal pipeline defect detection method based on magnetic memory

作为油气输送媒介的金属管道,其缺陷处产生应力集中将造成安全隐患,为实现金属管道缺陷的非接触式定量检测,研究了一种磁记忆检测方法。采用磁异常梯度矩阵实现对产生应力集中的缺陷进行定位;利用平移不变量小波去噪(TI)与特征提取进行信号处理;麻雀搜索算法(SSA)优化BP神经网络实现缺陷尺寸反演。实验表明,平移不变量小波去噪相比小波阈值去噪,信噪比提升1.56%,均方误差降低4.87%;SSA_BP神经网络反演均方误差比BP神经网络降低67.2%;检测方法能在提离状态下实时检测管道缺陷并反演缺陷尺寸。

As the medium of oil and gas transportation,the stress concentration in the defects of the metal pipeline will cause safety hazards.In order to realize the non-contact quantitative detection of metal pipeline defects,a magnetic memory detection method has been studied.Adopt the magnetic anomaly gradient matrix to locate the defects with stress concentration;use translation invariant wavelet denoising(TI)and feature extraction for signal processing.Sparrow search algorithm(SSA)optimizes the BP neural network to achieve defect size inversion.Experiments show that compared with wavelet threshold denoising,the translational invariant wavelet denoising can increase the signal-to-noise ratio by 1.56%and reduce the mean square error by 4.87%;the mean square error of SSA_BP neural network inversion is 67.2%lower than that of BP neural network.The detection method can detect pipeline defects in real time in the lift-off state and invert the defect size.