An Improved Feature Parameter Extraction Algorithm of Composite Detection Method Based on the Fusion Theory

• Main Authors: Zhou Ying, Jin Heli, Liu Banteng, Chen Yourong
• Format: Article
• Language: English
• Published: Hindawi Limited 2021-01-01
• Series: Journal of Sensors
• Online Access: http://dx.doi.org/10.1155/2021/8898991

An improved feature parameter extraction algorithm is proposed in this study to solve the problem of quantitative detection of subsurface defects. Firstly, the common feature parameters from the differential signal of pulsed eddy current and ultrasonic are extracted in time domain and frequency domain. Then, the dispersion model and ReliefF model are established to determine the weights of each parameter. Finally, the weights from the two different algorithms are fused by the D-S evidence theory to determine feature parameters. Compared with the PCA feature parameter algorithm from the pulsed eddy current or ultrasonic, the experiment results show the feature parameters extracted by the algorithm proposed in this paper are more effective in quantitative detection of subsurface defects. It will lead to high accuracy in the subsurface defections.