Data-Driven Fault Diagnostics for Neutron Generator Systems in Multifunction Logging-While-Drilling Service

This paper presents a data-driven fault diagnosis method for neutron generator systems in logging-while-drilling tools. Specifically, the nuclear system’s main failure modes and associated electronic boards are first identified, and then statistical features of the selected boards are extracted based on expert knowledge. The extracted features discriminate between healthy and faulty behavior for each board. Finally, machine learning models are used to map the relationship between the extracted features and the labels of the corresponding sensor data for each board. This method is validated using data collected from actual oil well drilling operations, and the experimental results show that the method is effective. This work is part of a long-term project aiming to construct a digital fleet management system for drilling tools.