Semi-supervised learning framework for oil and gas pipeline failure detection.

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 fa

SUBMITTER: Alobaidi MH 

PROVIDER: S-EPMC9374783 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature