A Method for Detecting Damage in Drill Tool Threads Based on APMR-GBM

Drill pipe threads are susceptible to fatigue cracking under complex downhole loads, posing significant risks to drilling safety. Although metal magnetic memory (MMM) testing enables efficient nondestructive evaluation, its practical utility is often compromised by interference from material magnetization and lift-off distance. To overcome this limitation, we introduce a novel damage assessment method based on the area peak-to-mean ratio (APMR) of magnetic signals. This feature is specifically designed to suppress external disturbances while retaining sensitivity to stress-induced magnetic anomalies. Finite element analysis was performed to elucidate the magneto-mechanical coupling behavior at defective thread roots. Subsequently, MMM signals were acquired from drill pipe threads under varying inspection conditions using a custom-built scanning system equipped with 16 tunneling magnetoresistance (TMR) sensors. Multiple features, including APMR, were extracted and evaluated across various machine learning classifiers. The gradient boosting machine (GBM) achieved superior performance, yielding an accuracy of 0.9861 and a recall of 1.0000 on the test set—outperforming all other models. This work presents an effective automated approach for drill pipe thread damage evaluation, contributing to enhanced reliability and safety in drilling operations.