Auto-Detection of Hidden Corrosion in an Aircraft Structure by Electromagnetic Testing: A Machine-Learning Approach

Abstract

An aircraft is a multilayer structure that is assembled by rivets. Under extreme working conditions, corrosion appears and quickly propagates at the rivet sites of the layers; thus, it threads the integrity and safety of the aircraft. Corrosion usually occurs at the hidden layer around the rivet, making it difficult to detect. This paper proposes a machine learning approach incorporating an electromagnetic testing system to detect the hidden corrosion at the riveting site effectively. Several machine learning methods will be investigated for the detection of different sizes and locations of corrosion. The training strategy of the machine-learning models on the small numbers of data will also be investigated. The result shows that the proposed approach could effectively detect 89.48% of the hidden corrosion having from 2.8 to 195.4 mm3 with only 20% of training data and could be increased to 99.0% with 60–80% of the training data.

Keywords

• electromagnetic testing
• rivet corrosion
• aircraft intake
• hall sensor array
• machine learning