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Both the software and hardware of a pulsed eddy current (PEC) real-time testing device are designed to pre-process the noise from continuous casting slabs (CCS) during in situ defect detection. An algorithm based on a sliding window with a fixed length is developed in the process of denoising. The root mean square error (RMSE) and the signal-to- noise ratio (SNR) of the denoised PEC response are given with the combination of Daubechies4, Symlet4 and three threshold denoising algorithms. The results of the simulation and experiment indicate that the best real-time denoising condition is with the wavelet transform of three or four layers. Herein, the SNR obtained is above 19 dB and the average throughput rate is 51 kHz when the moving speed of the casting slabs is about 2 m/s. Our system highlights real-time processing, stability, high accuracy and strong anti-interference, so it is suitable for automatic testing in a production situation. [ABSTRACT FROM AUTHOR]

The defect classification is investigated by using features-based giant-magnetoresistive pulsed eddy current (GMR-PEC) sensor. The power spectrum density of the intrinsic mode functions (IMFs) is extracted as the classification feature, considering the disadvantage of selecting a wavelet base determined in previous work on spectral analysis combined with wavelet-decomposition. The IMFs are derived through empirical mode decomposition (EMD) and ensemble EMD. Principal component analysis, linear discriminant analysis, and Bayesian classifier are employed for defect classification algorithm. The proposed approach is validated by experiments, and results indicate that the cracks and cavities in the surface and subsurface can be classified satisfactorily. [ABSTRACT FROM AUTHOR]

Highlights: [•] A new PEC feature (spectrum analysis after wavelet decomposition) is investigated. [•] The spectrum of the detail decomposited exceeding 1.7kHz is more effective. [•] The high-frequency components are dominant among the surface and sub-surface defects. [•] The PCA-LDA and Bayesian classifier can correctly classify all kinds of defects. [ABSTRACT FROM AUTHOR]