Fatigue cracking in riveted lap joints on aircraft could cause severe safety consequences in flight. The prior inspection for the integrity of the second layer of aircraft structure with rivet removed is necessary due to the priority of safety. Methods such as radiographic and ultrasonic testing are widely applied but require relatively expensive equipment, mechanical scanning or professional operators. Eddy current method can be adopted for non-destructive testing (NDT) inspection, which is safe and low-cost. In this paper, a hand-held eddy current (EC) sensor array was optimised based on sensitivity map analysis. The EC sensor array was used with a multi-channel EM instrument, which was capable of inspecting defects around the fastener locations covered by the head of rivets. A simulated sample of riveted joint with defects was examined with the eddy current sensing system and the collected data was processed by Hough Circle Transformation (HCT) and texture analysis. The analysis results could detect and locate fatigue cracks on the sample. The presented eddy current sensing system in this paper achieved an automatic, real-time, portable, convenient and accurate inspection method for defects in riveted joints.

Eddy current (EC) testing has become one of the most common techniques for measuring metallic planar structures in various industrial scenarios such as infrastructures, automotive, manufacturing, and chemical engineering. There has been significant progress in measuring the geometry, electromagnetic properties, and defects of metallic planar structures based on electromagnetic principles. In this review, we summarize recent developments in EC computational models, systems, algorithms, and measurement approaches for planar structures. First, the computational models including analytical models, numerical methods, and plate property estimation algorithms are introduced. Subsequently, the impedance measurement system and probes are presented. In plate measurements, sensor signals are sensitive to probe lift-off, and various algorithms for reducing the lift-off effect are reviewed. These approaches can be used for measureing thickness and electromagnetic properties. Furthermore, defect detection for metallic plates is also discussed.

The detection of lift-off variation presents a significant challenge in many eddy current testing (ECT) applications, where it is often considered an undesirable signal. To address this issue, the paper proposes a novel 4-coil excitation sensor, which can effectively suppress the lift-off effect when detecting defects of varying depths in planar structures. The proposed sensor design leverages opposite excitation directions between any two arbitrary adjacent excitation coils, which can effectively cancel out the lift-off signal due to a defect-free plate. However, this 4-coil excitation design has an angular sensitivity – i.e., different signal response for different angles between the same defect and the sensor. This angular sensitivity will cause trouble to identify the depth of defects without knowing the angle in prior. To overcome this challenge, the current study introduces deep learning (DL) models as a potential solution. Signals representing different defect depths and angles between the sensor and the defect are acquired using an experimental platform in our lab. The real and imaginary parts of the induced voltage on the receiver coil labelled with depths and angles are utilized to train a 1-D convolutional neural network (1D CNN) model, in which different convolution kernel sizes are used to extract more information and speed up convergence. The high performance of the proposed structure of 1D CNN were compared with convolutional long-short term memory (LSTM) model, recurrent neural network (RNN) model, LSTM+RNN model and ResNet18 model. The proposed 1D CNN model not only achieves good performances with a higher accuracy of 99.8% but also requires a smaller model size and less computational time.