Prediction and Modeling of Magnetic Flux Leakage Signals Based on Machine Learning

2020年全球管道完整性管理市场规模为86.5亿美元，预计2028年将增长到112.6亿美元。漏磁检测是目前应用最广泛的管道裂纹和腐蚀检测技术。虽然大多数漏磁预测方法可以应用于简单的轮廓形状，但它们无法检测具有不规则几何形状和相互作用信号的复杂区域的缺陷。我们建议开发基于深度学习的新的漏磁预测方法。首先，我们将为多通道MFL数据开发一种具有稀疏编码的时空深度学习预测。该模型将通过将裂缝和腐蚀形成的物理学作为一个基于知识的神经网络来扩展。其次，我们将开发一种在漏磁和激光信号之间的转换方式，作为漏磁映射的传感器转换，以产生激光信号，以实现理想的性能。关于检测模式检测的预期研究结果将为下一代管道缺陷尺寸和表征算法提供信息。这将提高在线检测结果的准确性，并为管道提供更好、更便宜和更安全的维护，降低加拿大发生严重事故的风险。

The global pipeline integrity management market size was $8.65 billion US in 2020 and is projected to grow to$11.26 billion in 2028. Magnetic flux leakage (MFL) is the most widely used non-destructive testing (NDT) technique to detect cracks and corrosions in pipeline. While most MFL prediction methods can be applied to simple profile shapes, they fail to detect defects with irregular geometries and complex regions of interacting signals. We propose developing new MFL prediction approaches based on deep learning. First, we will develop a spatial temporal deep learning prediction with sparse encoding for multi-channel MFL data. This model will then be extended by incorporating physics of cracks and corrosions formation as a physic informed neural network. Second, we will develop a style transfer between MFL and laser signals as a sensor transform for MFL mapping to generated laser signal to achieve ideal performance. The anticipated research results on detecting pattern detection will provide insight to inform the next generation of pipeline defect sizing and characterization algorithms. This will improve the accuracy of inline inspection results and lead to better, cheaper, and safer maintenance for pipelines, reducing the risk of serious incidents in Canada.