Deformation characterization of oil and gas pipeline by ACM technique based on SSA-BP neural network model. (15th February 2022)

• Record Type: Journal Article
• Authors: Xin, Jiaxing; Chen, Jinzhong; Li, Chunyu; Lu, Run-kun; Li, Xiaolong; Wang, Changxin; Zhu, Hongwu; He, Renyang
• Is Part Of: Measurement. Volume 189(2022)
• Publication Date: 2022-02-15
• Subjects: Deformation characterization -- ACM technology -- SSA-BP neural network -- Oil and gas pipeline -- In-line inspection; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.110654
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: This paper proposes an ACM-based non-contact, low magnetization level, and high sensitivity detection technique for the deformation of oil and gas pipelines. By analyzing the ACM waveform signals, the peak value, integrated area, first-order differential peak and trough, and peak and trough length of the first-order differential are proposed as the features. The proposed SSA-BP algorithm can characterize the critical deformation dimensions (height, length, tilt angle) within the mean relative error of 10%. Abstract: 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%.