Deformation characterization of oil and gas pipeline by ACM technique based on SSA-BP neural network model

Accurate and quantitative characterization of deformation in oil and gas pipelines is essential. This paper proposed a novel ACM (alternating current magnetization) based technique to detect the deformation of oil and gas pipelines. Numerical simulations and experiments reveal the relationships between the deformation factors (height, length, tilt angle) and the detected waveform signals. Meanwhile, the peak value, integral area, first order differential peak and valley value, peak and valley length of the waveform signals are selected as the features. In addition, a BP neural network model optimized by SSA (sparrow search algorithm) was introduced to identify the deformation of the pipelines. The results show that the waveform signals corresponding to the deformation due to external stress and corrosion are distributed in the mountain peak and basin shape, respectively. With features as input, the proposed SSA-BP algorithm can efficiently characterize the deformation within the mean relative error of 10%.

Automated summary

Accurate and quantitative characterization of deformation in oil and gas pipelines is essential for pipeline integrity management. This paper proposed a novel ACM-based technique to detect the deformation, revealing relationships between deformation factors and waveform signals. By using features and a SSA-BP algorithm, the proposed method efficiently characterizes pipeline deformation within a mean relative error of 10%.