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This paper presents a novel data-driven approach for modeling degradation of the neutron generator component in logging-while-drilling tools. The study begins by identifying the incipient failure modes of the neutron generator and constructing a health indicator (HI) that serves as a quantitative measure of the component’s health status. The resulting HI can be used for additional analysis and decision-making. Then, a random forest classifier is trained to establish the relationship between the extracted HI values and the corresponding degradation level labels. The proposed method is validated using actual data collected from oil well drilling operations. The experimental results demonstrate its effectiveness in accurately classifying the health state of the neutron generator component. The study is part of a long-term project aimed at developing a digital fleet management system for drilling tools.