摘要：Natural gas pipeline leakage can cause serious financial losses to natural gas transportation and pose accidents to the environmental safety. Currently-used supervised learning methods heavily rely on sufficient pipeline failure historical data for their training. Therefore, we propose a novel detection approach based on unsupervised learning and stress perception for determining the leakage situation in pipelines. In this study, pipeline stress signals are first acquired based on residual magnetic effect. The relationship between residual magnetic and stress is built using improved sparrow search algorithm (ISSA) and extreme learning machine (ELM). Then, the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is deployed to learn suitable features from the stress signals under the pipeline normal condition, generating high-quality stress data features. Finally, the generated stress features are supplied to the Bayesian Gaussian mixture model (BGMM). And the weighted logarithm probability (WLP) is used as the health indicator for examining pipeline status. The results demonstrate that the relative error of residual magnetic stress model is controlled within 3 %, and the WLP value of fault samples is smaller than ? 100, so that the proposed method can discriminate the normal and leak conditions as well as the risk and severity of leakage. This study provides a theoretical basis and new perspective for pipeline leakage detection. ? 2022 The Institution of Chemical Engineers

ISSN号：0957-5820

卷、期、页：v 170,p76-88

发表日期：2023-02-01

影响因子：0.000000

期刊分区（SCI为中科院分区）：二区

收录情况：EI（工程索引）

发表期刊名称：Process Safety and Environmental Protection

通讯作者：香朝元,香朝元

第一作者：赵弘

论文类型：期刊论文

论文概要：香朝元,赵弘,香朝元，Leakage detection in natural gas pipeline based on unsupervised learning and stress perception，Process Safety and Environmental Protection，2023，v 170,p76-88

论文题目：Leakage detection in natural gas pipeline based on unsupervised learning and stress perception