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 testing (ECT) is an effective technique for evaluating depth of metal surface defects. However, in practice, evaluation primarily relies on the experience of an operator and is often carried out by manual inspection. In this article, we address the challenges of automatic depth evaluation of metal surface defects by virtual of state-of-the-art deep learning (DL) techniques. The main contributions are threefold. First, a highly integrated portable ECT device is developed, taking the advantage of an advanced field-programmable gate array (Zynq-7020 system on chip) and provides fast data acquisition and in-phase/quadrature demodulation. Second, a dataset, termed metal defects of different depths by ECT (MDDECT), is constructed using the ECT device by human operators and made openly available. It contains 48000 scans from 18 defects of different depths and liftoffs. Third, the depth evaluation problem is formulated as a time series classification problem, and various state-of-the-art 1-D residual convolutional neural networks are trained and evaluated on the MDDECT dataset. A 38-layer 1-D ResNeXt achieves an accuracy of 93.58% in discriminating the surface defects in a stainless steel sheet with depths from 0.3 to 2.0 mm in the resolution of 0.1 mm. In addition, the results show that the trained ResNeXt1D-38 model is immune to liftoff signals.

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.

In this paper, an electromagnetic sensor which can operate simultaneously in capacitive and inductive modalities with sensitivities to permittivity, conductivity, and permeability is developed, and a novel measurement strategy is proposed accordingly. The sensor is composed of two planar spiral coils with a track width of 4 mm, which promotes its capacitive mode. The capacitive coupling is measured in common mode, while the inductive coupling is measured in differential mode. In capacitive mode, the sensor is sensitive to changes in permittivity, i.e., the dielectric material distribution; while in inductive mode, it is sensitive to magnetically permeable material and electrically conductive material. Furthermore, it is demonstrated that the sensor can simultaneously measure dielectric and conductive materials. This novel sensing element has been designed and implemented. Experimental results verified its effectiveness in dual modality measurement.