Different applications of pulsed eddy current (PEC) testing have been reported in safety detection due to its characteristics of high sensitivity, non-contact and rich information. In the determination of metal thickness by PEC testing, the lift-off effect commonly gives rise to inaccurate measurement. As a desirable signal feature immune to lift-off effect, lift-off point of intersection (LOI) is a common point where PEC signals intersect when only lift-off distance changes. However, for ferromagnetic samples, a LOI point could not be obtained directly from PEC signals when lift-off distance changes. This paper investigated spectral PEC signals of ferromagnetic metal plate by numerical simulations and experiments. Excitingly, a frequency LOI was firstly observed in spectral PEC signals for ferromagnetic samples and the behavior of LOI was further analyzed when sample conductivity, permeability and thickness change. This paper concludes that the LOI in spectral PEC signals is affected by sample conductivity, permeability and thickness. In addition, the frequency LOI allows for accurate evaluation of ferromagnetic samples with the variation of lift-off distance.

The liftoff point of intersection (LOI) is a desirable signal feature in the pulsed eddy current (PEC) testing. It is immune to liftoff effect caused by the change of liftoff distance between a probe and a sample. However, adjusting LOI remains an open problem for enhancement of PEC evaluation or extends the application of LOI to thicker samples. In this paper, LOI point could be adjusted by varying the rising time of pulse excitation to extend the measurement range or increase sensitivity for enhancement of the PEC evaluation. The simulations and experiments demonstrate that the difference PEC signal increases and decreases significantly slowly when the rising time of pulse excitation increases. When the rising time of pulse excitation increases, the measurement range of LOI amplitude increases, and the measurement range of LOI time decreases, whereas the sensitivity of LOI amplitude decreases, and the sensitivity of LOI time increases. This investigation helps us to optimize the rising time for the improvement of specific PEC evaluation.

Lift-off invariant strategies play an important role in pulsed eddy current (PEC) testing. As an effective signal feature immune to lift-off effect, lift-off point of intersection (LOI) has been applied to determine thickness or evaluate defects. Typically, an LOI feature is extracted from time domain PEC signals. In this article, we observed a novel LOI feature in real and imaginary part of frequency domain PEC responses via simulation and experiment. An analytical model is formulated to investigate the novel LOI feature. It is found that the proposed LOI feature varies with sample thickness increasing, which indicates that the LOI feature from spectral PEC signals could be used for thickness measurement.

This paper proposes a structural damage detection method based on one-dimensional convolutional neural network (CNN). The method can automatically extract features from data to detect structural damage. First, a three-layer framework model was designed. Second, the displacement data of each node was collected under the environmental excitation. Then, the data was transformed into the interlayer displacement to form a damage dataset. Third, in order to verify the feasibility of the proposed method, the damage datasets were divided into three categories: single damage dataset, multiple damage dataset, and damage degree dataset. The three types of damage dataset can be classified by the convolutional neural network. The results showed that the recognition accuracy is above 0.9274. Thereafter, a visualization tool called “t-SNE” was employed to visualize the raw data and the output data of the convolutional neural network. The results showed that the feature extraction ability of CNN is excellent. However, there are many hidden layers in a CNN. The outputs of these hidden layers are invisible. In the last section, the outputs of hidden layers are visualized to understand how the convolutional neural networks work.

Lift-off point of intersection (LOI), as a desirable signal feature independently of the lift-off effect, is expected to be applied in pulsed eddy current (PEC) testing of ferromagnetic samples. The time-domain LOI in PEC signals is easily recorded and easier to be extracted for evaluating ferromagnetic samples, compared with the frequency domain LOI in spectral PEC signals. However, the time-domain LOI cannot be obtained in the PEC signals of ferromagnetic samples. In this article, a twice subtraction method was proposed to obtain an LOI feature in PEC signals of ferromagnetic samples for evaluating the sample thickness, which is verified through simulations and experiments. In the twice subtraction method, the first subtraction is performed between PEC signals from the sample and air to obtain a difference in the PEC signal. The secondary subtraction is implemented between the difference in the PEC signal and its standard deviation. As a result, an LOI can be extracted from the twice subtraction signals when only lift-off distances are introduced. The results demonstrate that the twice subtraction method is a feasible program to obtain an LOI in twice subtraction signals of ferromagnetic samples, and the obtained LOI has the potential to be used to measure the thickness of ferromagnetic samples without lift-off effect.