Pulsed eddy current (PEC) testing is a non-destructive testing (NDT) method, which is highly suitable for thickness measurement, and sub-surface defect detection. In this paper, PEC testing is employed to simultaneously detect defect and sample thickness. Firstly, Finite-element method (FEM) is conducted to reveal the signal characteristics of different defect sizes and depths on the aluminum plate, as well as its thickness. Experimental verification is conducted to verify the simulation results. It reveals that the signal variations associated with defect size and depth are relatively smaller compared to the impact of thickness changes. Both logarithmic function and discrete Fourier transform (DFT) are utilized as signal processing method to improve signal differentiation between defect characteristics. Notably, the processed signal through DFT produces both amplitude and phase spectra, with a focus on the enriched features within the high-frequency portion of the signals post-DFT. Lastly, all the raw and processed data are fed into deep learning (DL) models. The optimized Res2net18 network with post-DFT data demonstrates superior performance. It achieves classification accuracies of 98.0%, 100%, and 96.0% for defect size, thickness of the sample, and defect depth, respectively. The results show the proposed DL network has faster convergence and stability in comparison to the Res2Net network and DenseNet121, and there are 1.5% and 2.0%, 5.4% and 9.7% classification accuracy improvement in defect size and depth. The predict values based on Optimized Res2Net Network also present consistent results with validation and test results.

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.

Lift-off effect, resulting in detection error, which is a significant challenge that needs to be addressed in defect detection using eddy current testing (ECT). A novel array eddy current sensor, with each unit composing of 4 excitation coils and a receiving coil, is proposed to implement fast and large-area defect scanning for metal sheet without lift-off interference. In addition, theoretical analysis based on Biot-Savart law is applied to illustrate the feasibility of the proposed sensor in eliminating lift-off effect. However, the proposed array sensor has also an angular sensitivity which means there are different signal responses for different defect orientations, but the feature is not conducive to evaluate the defect depths without knowing the angle difference as a prior, which can be addressed by deep learning (DL) algorithm. Both simulation and experiment are implemented to verify the reliability and feasibility of proposed array sensor. The proposed preprocessed method is utilized to attain significant signal feature differences for various defect depths. In addition, the improved ResNet18 network is proposed to train acquired experimental data, and its superior performance is demonstrated. The results show that the improved ResNet18 network with preprocessed data achieves fastest training speed compared to other methods in this paper. At same time, the proposed DL network achieves high accuracy of 98.1% and 95.4% for defects depth for aluminum sheet and stainless-steel sheet, respectively. Additionally, when considering 16 labels combinations of lift-off distances and angle differences during the process of inspection, the accuracy of lift-off classification for two materials are 97.2% and 96.9%, respectively.

Eddy-current testing technique has been extensively explored for estimating the electromagnetic property of steel plates in various industrial applications. In this article, a physics-guided deep learning (DL) method is proposed to estimate the permeability of plate in high thickness with probe liftoff. A simplified analytical model is derived, which realises the single to multiple frequency inductance transformation and calculates the related physical properties of the measurement configuration. A constant is found, which is a fundamental coefficient describing the first-order nature of the sensor response to a plate and it is insensitive to plate properties and probe dimensions. The nonlinear mapping from physical information, derived from the simplified analytical model, to plate permeability is constructed by the DL model based on the modified ResNet18-1D. Numerical simulations and experiments have been performed to evaluate the proposed method for permeability estimation with various plate materials and probe liftoff. The method achieves real-time accurate estimation of plate permeability with a relative error lower than 3%.