| 石明江,陈 瑞,冯 林.基于磁记忆的金属管道缺陷检测方法[J].电子测量与仪器学报,2022,36(1):44-53 |
| 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 |
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| DOI： |
| 中文关键词: 磁记忆检测 磁异常梯度 平移不变量小波去噪 麻雀搜索算法 BP 神经网络 |
| 英文关键词:magnetic memory detection magnetic anomaly gradient translation invariant wavelet denoising sparrow search algorithm BP neural network |
| 基金项目:四川省科技计划项目(2019YJ0318)资助 |
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| 中文摘要: |
| 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。 |
| 英文摘要: |
| 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. |
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