Automatic Damage Detection of Fasteners in Overhaul Processes

Commercial aircraft engines have a maintenance process that includes overhauling approximately every six years. Hundreds of different components must be disassembled, checked, repaired (if necessary), and then reassembled. This includes undoing fasteners, cleaning, checking, refitting, and tightening them. Prior to refitting the fasteners, they must be checked for damages. In this paper, we propose an automatic damage inspection of the fasteners, using computer vision and machine learning. We built a setup to automatically record and preprocess the data and compared multiple supervised and unsupervised machine learning models for detecting damages of 12 different fasteners. Using our automatic approach, we can determine the type of fastener, its status (damaged or intact) and visualize the anomalies to aid the understanding of the decisions of the automatic detection. This can be the first step towards a fully automated fastener damage detection in overhaul processes.