Data-Driven Remaining Useful Life Estimation Approach for Neutron Generators in Multifunction Logging-While-Drilling Service
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Abstract
This paper introduces a data-driven approach for estimating the remaining useful life of the neutron generator component in logging-while-drilling tools. The approach builds on identification of the incipient failure modes of the neutron generator and constructing a health indicator that serves as a statistical representation of the component’s deterioration over time. Afterwards, a K-nearest neighbors algorithm is trained to establish the relationship between the extracted health indicator values and the corresponding remaining useful life. The effectiveness of the presented approach is verified through the utilization of real-world data gathered from oil well drilling operations. The study is part of a long term project aimed at developing a digital fleet management system for drilling tools.
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Prognostics, Remaining Useful Life Estimation, Health State Estimation, Oil and Gas, Nuclear Subsystems
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