NLP-Based Fault Detection Method for Multifunction Logging-While-Drilling Services
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
This paper presents a Natural Language Processing (NLP) method aimed at detecting faults within field failure reports of drilling tools. It builds on the definition of entities specifically matched to our unique requirements. These entities have been annotated within the dataset under the guidance of a Subject Matter Expert (SME), laying a foundation for our NLP method. By utilizing a model based on bidirectional encoder representations from transformers, the method achieves an F1-score of 88\% in identifying entities and consequently detecting faults within field failure reports. This work is part of a long-term project aiming to construct a failure analysis and resolution system for drilling tools.
How to Cite
##plugins.themes.bootstrap3.article.details##
Failure Investigation Process, Logging While Drilling Services
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.