Machine Learning Algorithms for Failure Prediction Model and Operational Reliability of Onshore Gas Transmission Pipelines
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 failure...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Universitas Indonesia
2023-05-01
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| Series: | International Journal of Technology |
| Subjects: | |
| Online Access: | https://ijtech.eng.ui.ac.id/article/view/6287 |
| _version_ | 1797831108386095104 |
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| author | Andy Noorsaman Dea Amrializzia Habiburrahman Zulfikri Reviana Revitasari Arsene Isambert |
| author_facet | Andy Noorsaman Dea Amrializzia Habiburrahman Zulfikri Reviana Revitasari Arsene Isambert |
| author_sort | Andy Noorsaman |
| collection | DOAJ |
| description | 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
an AUC of 0.94 and a 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. |
| first_indexed | 2024-04-09T13:46:45Z |
| format | Article |
| id | doaj.art-379709f599c94409be469b251d36059b |
| institution | Directory Open Access Journal |
| issn | 2086-9614 2087-2100 |
| language | English |
| last_indexed | 2024-04-09T13:46:45Z |
| publishDate | 2023-05-01 |
| publisher | Universitas Indonesia |
| record_format | Article |
| series | International Journal of Technology |
| spelling | doaj.art-379709f599c94409be469b251d36059b2023-05-09T01:57:56ZengUniversitas IndonesiaInternational Journal of Technology2086-96142087-21002023-05-0114368068910.14716/ijtech.v14i3.62876287Machine Learning Algorithms for Failure Prediction Model and Operational Reliability of Onshore Gas Transmission PipelinesAndy Noorsaman0Dea Amrializzia1Habiburrahman Zulfikri2Reviana Revitasari3Arsene Isambert4Department of Chemical Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, IndonesiaDepartment of Process Engineering, PT. Rekayasa Engineering, Jl. Kalibata Timur II No.36, South Jakarta, Jakarat 12740, IndonesiaDepartment of Chemical Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, IndonesiaDepartment of Chemical Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, IndonesiaLaboratoire de Genie de Procedes et Materiaux, Ecole Centrale Paris, F 92295 Chatenay Malabry Cedex, FranceA 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 an AUC of 0.94 and a 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.https://ijtech.eng.ui.ac.id/article/view/6287binary logistic regressionfailure predictionmachine learningrandom foresttransmission pipeline |
| spellingShingle | Andy Noorsaman Dea Amrializzia Habiburrahman Zulfikri Reviana Revitasari Arsene Isambert Machine Learning Algorithms for Failure Prediction Model and Operational Reliability of Onshore Gas Transmission Pipelines International Journal of Technology binary logistic regression failure prediction machine learning random forest transmission pipeline |
| title | Machine Learning Algorithms for Failure Prediction Model and Operational Reliability of Onshore Gas Transmission Pipelines |
| title_full | Machine Learning Algorithms for Failure Prediction Model and Operational Reliability of Onshore Gas Transmission Pipelines |
| title_fullStr | Machine Learning Algorithms for Failure Prediction Model and Operational Reliability of Onshore Gas Transmission Pipelines |
| title_full_unstemmed | Machine Learning Algorithms for Failure Prediction Model and Operational Reliability of Onshore Gas Transmission Pipelines |
| title_short | Machine Learning Algorithms for Failure Prediction Model and Operational Reliability of Onshore Gas Transmission Pipelines |
| title_sort | machine learning algorithms for failure prediction model and operational reliability of onshore gas transmission pipelines |
| topic | binary logistic regression failure prediction machine learning random forest transmission pipeline |
| url | https://ijtech.eng.ui.ac.id/article/view/6287 |
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