摘要：The magnetic flux leakage (MFL) inspection is the most common method for detecting defects in corroded pipelines and assessing the reliability of pipelines. However, in traditional ways, many steps are required to obtain the pipeline’s reliability from MFL detection. This study establishes a finite element (FE) model to simulate the MFL signal. Then, a novel method is proposed to use a convolutional neural network (CNN) to directly map the MFL image to the array representing the pipeline’s reliability in the future 30 years. The case study demonstrates that the proposed method can estimate corroded pipe reliability more quickly and precisely by eliminating several image processing and calculation procedures. The results indicate that the proposed method is beneficial for assisting pipeline operators in efficiently identifying the reliability status and providing a basis for pipeline integrity management. ? RQD 2023. All rights reserved.All right reserved.

ISSN号：9798986576121

卷、期、页：28th ISSAT International Conference on Reliability and Quality in Design, RQD 2023, Pages 56-60,

发表日期：2023-08-03

期刊分区（SCI为中科院分区）：无

收录情况：EI（工程索引）

发表期刊名称：28th ISSAT International Conference on Reliability and Quality in Design, RQD 2023

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：会议论文

论文概要：陈一诺,田志刚,魏昊天,董绍华，Reliability assessment for corroded pipes based on MFL inspection，28th ISSAT International Conference on Reliability and Quality in Design, RQD 2023，2023，28th ISSAT International Conference on Reliability and Quality in Design, RQD 2023, Pages 56-60,

论文题目：Reliability assessment for corroded pipes based on MFL inspection