Flaw Classification And Recognition Technology In Pulsed Eddy Current Nondestructive Testing

Pulsed Eddy Current(PEC) nondestructive testing(NDT) is a new rapidly developing NDT technologies. Due to its broadband spectrum Excitation, it can get more defects information in large-scale testing of complex structures. Based on the current situation of Pulsed Eddy Current NDT technique both abroad and at home, the progress in the research of Nondestructive Test System, test signal denoising and feature extraction,and recognition of different defects are presented. The research work is funded under the research contract with the Guangdong government (2006B12401001) and is important to PEC inspection not only in theory but also in practice.The main research work is described as follows:(1) System design. The paper built the entire system PEC flaw detection including data collection, storage systems, and the key modules of detection system, such as PEC signal generation module, signal conditioning module and development of detection probes. Based on DDS technology and FPGA chip, the PEC excitation signal module can reduce design costs and improve design flexibility.(2) Addressing on the situation that PEC testing signals are disturbed seriously by environment noise pollution,comparing the advantages and disadvantages of different wavelets, this paper used Morlet wavelet to denoise the detection signals, and used signal to noise ratio, root mean square error and smoothness indicators to evaluate the denoising effect. As Traditional time-domain waveform feature extraction is susceptible to noise interference, the paper chose Daubechies wavelet on layer 3 to decompesite denoised signals, calculated the energy of each level of PEC wavelet signal and set the energy value as characteristic value. Compared with traditional signal compression, the progress can significantly reduce the computation.(3)Based on the analysis of advantages and disadvantages in ant colony algorithm and genetic algorithm, combining with its application to pattern recognition, the paper innovatively presented using ant colony algorithm and genetic algorithm in PEC surface defects and sub-surface defects recognition. Compared separately using ant colony algorithm or genetic algorithm in defects classification and recognition, the paper proposed the combination of the two algorithms--ant colony - the genetic algorithm. Experiments showed that this algorithm is better than both ant colony algorithm and genetic algorithm.