@article{ref1,
title="Semi-supervised learning framework for oil and gas pipeline failure detection",
journal="Scientific reports",
year="2022",
author="Alobaidi, Mohammad H. and Meguid, Mohamed A. and Zayed, Tarek",
volume="12",
number="1",
pages="e13758-e13758",
abstract="Quantifying failure events of oil and gas pipelines in real- or near-real-time facilitates a faster and more appropriate response plan. Developing a data-driven pipeline failure assessment model, however, faces a major challenge; failure history, in the form of incident reports, suffers from limited and missing information, making it difficult to incorporate a persistent input configuration to a supervised machine learning model. The literature falls short on the development of appropriate solutions to utilize incomplete databases and incident reports in the pipeline failure problem. This work proposes a semi-supervised machine learning framework which mines existing oil and gas pipeline failure databases. The proposed cluster-impute-classify (CIC) approach maps a relevant subset of the failure databases through which missing information in the incident report is reconstructed. A classifier is then trained on the fly to learn the functional relationship between the descriptors from a diverse feature set. The proposed approach, presented within an ensemble learning architecture, is easily scalable to various pipeline failure databases. The results show up to 91% detection accuracy and stable generalization ability against increased rate of missing information.  Keywords: Stationary Transportation; Pipeline Transportation <p /> <p>Language: en</p>",
language="en",
issn="2045-2322",
doi="10.1038/s41598-022-16830-y",
url="http://dx.doi.org/10.1038/s41598-022-16830-y"
}