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 fro...
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| Principais autores: | , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Nature Portfolio
2022-08-01
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| coleção: | Scientific Reports |
| Acesso em linha: | https://doi.org/10.1038/s41598-022-16830-y |
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