摘要：A magnetic flux leakage (MFL) tool may identify most pipeline corrosion defects. Therefore, analyzing the MFL signals to obtain helpful information for pipeline safety analysis is significant. However, existing methods need many steps to get pipeline reliability results via MFL detection, resulting in a substantial time investment. This study proposes a reliability prediction method based on Residual Neural Networks (ResNet) that can directly map the MFL inspection data to the pipeline's reliability. Pipeline defect effective area model, rather than those based on just depth, is effectively integrated with deep learning models. Due to the limited practical data sources, the finite element (FE) method is used to simulate a large amount of data for ResNet training. It is found that the ResNet family can improve both the model's performance and training efficiency. Compared to traditional methods, the proposed model's accuracy is more than 20% higher, and the computational efficiency has been increased by 200 times. Case studies of FE simulations and industrial applications illustrate that the suggested approach is capable of assessing the reliability of corroded pipes in a more timely and accurate manner than traditional methods. The proposed method is also helpful for pipeline operators to understand the pipeline risk condition and obtain suggestions for optimizing costs and re-assessment intervals, providing a foundation for pipeline digital integrity management. 漏 2024 The Institution of Chemical Engineers

ISSN号：0957-5820

卷、期、页：卷184：1131-1142

发表日期：2024-04-01

影响因子：0.000000

期刊分区（SCI为中科院分区）：二区

收录情况：SCI（科学引文索引印刷版）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：Process Safety and Environmental Protection

参与作者：陈一诺,田志刚

通讯作者：魏昊天

第一作者：董绍华

论文类型：期刊论文

论文概要：陈一诺,田志刚,魏昊天,董绍华，Reliability analysis of corroded pipes using MFL signals and Residual Neural Networks，Process Safety and Environmental Protection，2024，卷184：1131-1142

论文题目：Reliability analysis of corroded pipes using MFL signals and Residual Neural Networks