This paper presents a method for constructing a health indicator to detect neutron-generator faults in a multifunction logging-while-drilling (LWD) service and predict maintenance requirements due to wear. The method is based on extracting fetures from selected channels that hold information about the subsystem degradation with time. These features are used to build a decision-tree model which estimates the tool condition from the recorded data. The model demonstrates excellent value for both maintenance and field engineers due to the fact that in just a few minutes the physical condition of the neutron generator can be determined with high confidence. This work is part of a long-term project with the aim to construct a digital fleet management for drilling tools.
How to Cite
Signal Processing, Machine Learning, Prognostics and Health Management, Data-driven Fault detection, Health indicator construction
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.