[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125581-en":3,"doc-seo-125581-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125581,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Machine Learning Algorithms for Failure Prediction Model and Operational Reliability of Onshore Gas Transmission Pipelines","Onshore gas transmission pipelines provide the safest and most effective long-distance transport for natural gas, yet inefficient maintenance can lead to failures with catastrophic consequences. The study builds accurate failure-prediction and operational reliability models to support optimal pipeline replacement timing. Two machine learning methods—random forest and binary logistic regression—are trained and tested using a decade of unstructured historical failure data. Random forest and logistic regression achieve strong AUC and prediction accuracy, and the final model is used to forecast future break locations and estimated break proportions over 2025–2035.","Machine Learning Algorithms for Failure Prediction Model and Operational Reliability of Onshore Gas Transmission Pipelines  \nAndy Noorsaman 1*, Dea Amrializzia2, Habiburrahman Zulfikri 1, Reviana Revitasari 1, Arsene Isambert3  \n1Department of Chemical Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia  \n2Department of Process Engineering, PT. Rekayasa Engineering, Jl. Kalibata Timur II No.36, South Jakarta, Jakarat 12740, Indonesia  \n3Laboratoire de Genie de Procedes et Materiaux, Ecole Centrale Paris, F 92295 Chatenay Malabry Cedex, France  \nAbstract. A transmission pipeline is the safest and most effective way of transporting large volumes of natural gas over long distances. However, if not maintained efficiently, failures of gas transmission pipelines can occur and cause catastrophic events. Therefore, an accurate prediction of pipe failures and operational reliability is required to determine the optimal pipe replacement timing such that the incidence of pipe failures can be prevented. Nowadays, computer-assisted technology helps businesses make better decisions, and machine learning is among the excellent techniques that can be utilized in predicting failures. In this study, two machine learning algorithms, i.e., random forest and binary logistic regression, are developed, and their prediction abilities are compared. The model is developed based on a decade of unstructured and complex historical failure data of the onshore gas transmission pipelines released by the United States Department of Transportation. The modeling process begins with data pre-processing followed by model training, model testing, performance measuring, and failure predicting. Both algorithms have demonstrated excellent results. The random forest model achieved an AUC of 0.89 and a predictive accuracy of 0.913, while the binary logistic regression model outperformed with anAUC of 0.94 anda prediction accuracy of 0.949. The trained model is further employed to predict future failures on a 11900-mile natural gas pipeline spanning from Louisiana to the northeast section of the United States. We show the location of the pipes that will be broken in the interval of five years and estimate that 29%/63%/83% of the pipes will break by 2025/2030/2035 .  \nKeywords: Binary logistic regression; Failure prediction; Machine learning; Random forest; Transmission pipeline  \n1. Introduction  \nNatural gas as a petroleum substitute offers many economic, technological, and environmental benefits and increases efficiency because it is quickly developed (Lee et al., 2012) as cited in Bawono and Kusrini, 2017) . It is a versatile energy source because it can be stored and transported in trucks or tankers as liquified natural gas, medium-conditioned liquified gas, or compressed natural gas (Ríos-Mercado and Borraz-Sánchez, 2015) as cited in (Farizal, Dachyar, and Prasetya, 2021) . However, Mikolajková-Alifov et al. (2019) study  \n* Corresponding author’s email: [andy.noorsaman@ui.ac.id](andy.noorsaman@ui.ac.id), Tel.: +62-21-7863515; Fax: +62-21-7863515 doi: 10.14716/ijtech.v14i3 .6287  \nconveys that transporting large amounts of natural gas via pipelines, one of which is through onshore gas transmission pipelines, is more cost-effective (Farizal, Dachyar, and Prasetya, 2021). A submarine pipeline in a submerged floating tunnel (SFT) is proposed asan alternative solution to pipeline-related ecological issues (Budiman, Raka, and Wahyuni, 2017) . However, SFT is not covered by the scope of this study, which is concerned with a natural gas pipeline that runs from Louisiana to the northeast United States.  \nAlthough more efficient than trucks or tankers, onshore gas transmission pipelines face serious challenges. Its failures are disastrous, causing financial losses, environmental damage, and even death. Gas pipeline failures are caused by several factors, including pipe/weld material failure, excavation damage, corrosion, equipment failure, soi","cbCainu8xx1Pt9qb","https://ap.wps.com/l/cbCainu8xx1Pt9qb","pdf",639769,1,10,"English","en",105,"# Introduction\n## Natural gas and pipeline reliability challenges\n## Predictive maintenance and IIoT context\n## Machine learning for failure prediction\n# Model development and evaluation\n## Data preprocessing and training\n## Testing and performance measurement\n## Failure prediction and forecasting","[{\"question\":\"为什么需要对陆上天然气输送管道进行故障预测与可靠性评估？\",\"answer\":\"因若维护不高效，管道故障可能导致灾难性事件并造成巨大的财务损失、环境破坏甚至人员伤亡，因此需要准确预测故障以确定最佳更换时机。\"},{\"question\":\"研究对比了哪两种机器学习算法？\",\"answer\":\"研究开发并对比了两种算法：随机森林（random forest）与二元逻辑回归（binary logistic regression），用于建立故障预测模型。\"},{\"question\":\"模型的未来预测结果如何用于评估管道失效风险？\",\"answer\":\"训练完成的模型用于预测未来故障，在指定管道区间内给出未来五年内可能损坏的位置，并估计管道在2025、2030和2035年前的失效比例。\"}]","Machine Learning Algorithms for Failure Prediction Model and Operational Reliability of Onshore Gas Transmission Pipelines | PDF",1785900029,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-algorithms-for-failure-prediction-model-and-operational-reliability-of-onshore-gas-transmission-pipelines","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-algorithms-for-failure-prediction-model-and-operational-reliability-of-onshore-gas-transmission-pipelines/125581/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"为什么需要对陆上天然气输送管道进行故障预测与可靠性评估？","Question",{"text":75,"@type":76},"因若维护不高效，管道故障可能导致灾难性事件并造成巨大的财务损失、环境破坏甚至人员伤亡，因此需要准确预测故障以确定最佳更换时机。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"研究对比了哪两种机器学习算法？",{"text":80,"@type":76},"研究开发并对比了两种算法：随机森林（random forest）与二元逻辑回归（binary logistic regression），用于建立故障预测模型。",{"name":82,"@type":73,"acceptedAnswer":83},"模型的未来预测结果如何用于评估管道失效风险？",{"text":84,"@type":76},"训练完成的模型用于预测未来故障，在指定管道区间内给出未来五年内可能损坏的位置，并估计管道在2025、2030和2035年前的失效比例。","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]