Eddy current testing (ECT), is a non-destructive method widely applied in detecting defects of metal plates based on the principle of electromagnetic induction. The flexible eddy current testing (FECT) has been developed from ECT. What's more, it's also an effective method that is given a higher priority in defect detection of metal with complex surface structure due to its flexibility and small size. An arrayed differential flexible butterfly-shape eddy current sensor is proposed to detect the surface defect of iron screw thread in this paper, which has one butterfly-shape coil, four sets of differential receiver (RX) coils and one top RX coil, and the proposed sensor is designed as butterfly-shape with a certain folding angle to match with the curvature of the internal and external screw thread, which can solve the lift-off problem of screw thread defect detection at the bottom. The theoretical analysis illustrates the design principle for the proposed sensors. The simulation based on COMSOL Multiphysics is applied to illustrate the probability of the proposed sensor, and the experimental testing system is adopted to verify the high performance of the proposed sensors. The results of simulation and experiment are identical, which show the flexible differential butterfly-shape array eddy current sensor can not only improve the sensitivity and reduce the error rate, but also orientate the defect on the surface of the screw thread.

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.

In this work, multiple floral eddy current probes (FECP) are proposed. FECP consists of a detection coil and a plurality of symmetric excitation coils, and due to the number of excitation coils, it could be classified into 4-coils, 6-coils, 8-coils, or 10-coils FECP. Due to the innovation of its configuration that transmitter (TX) coils are successively connected in reverse order and distributed symmetrically around receiver (RX) coil, the proposed new probe is not only less sensitive to the change of the lift-off distance but also has high accuracy and sensitivity of detection and could resist vibration interference. In this paper, the theoretical model of FECP is established, and simulation and experiment verification are carried out respectively. Firstly, the lift-off effect is studied. Moreover, an aluminum tested plate with artificial flaws is used to evaluate the performance of differenttype of flexible FECP, which shows higher sensitivity in flaws than the conventional single coil.

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.