You Only Look Twice! for Failure Causes Identification of Drill Bits
This addresses the need for efficient root cause analysis in drilling operations to reduce operational losses and safety risks, though it appears incremental as it applies existing methods like YOLO and rule-based approaches to a specific domain.
The study tackled the problem of identifying root causes of drill bit failure by developing automated models using images of blades, achieving a macro-average F1-score of 0.94 for failure cause identification and successfully identifying 100% of 24 failure causes in tests on ten drill bits.
Efficient identification of the root causes of drill bit failure is crucial due to potential impacts such as operational losses, safety threats, and delays. Early recognition of these failures enables proactive maintenance, reducing risks and financial losses associated with unforeseen breakdowns and prolonged downtime. Thus, our study investigates various causes of drill bit failure using images of different blades. The process involves annotating cutters with their respective locations and damage types, followed by the development of two YOLO Location and Damage Cutter Detection models, as well as multi-class multi-label Decision Tree and Random Forests models to identify the causes of failure by assessing the cutters' location and damage type. Additionally, RRFCI is proposed for the classification of failure causes. Notably, the cutter location detection model achieved a high score of 0.97 mPA, and the cutter damage detection model yielded a 0.49 mPA. The rule-based approach over-performed both DT and RF in failure cause identification, achieving a macro-average F1-score of 0.94 across all damage causes. The integration of the complete automated pipeline successfully identified 100\% of the 24 failure causes when tested on independent sets of ten drill bits, showcasing its potential to efficiently assist experts in identifying the root causes of drill bit damages.