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Wavelet transform-based feature extraction for ultrasonic flaw signal classification

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Abstract

In this paper, we present automatic classification models for ultrasonic flaw signals acquired from carbon-fiber-reinforced polymer specimens. Different state-of-the-art strategies based on wavelet transform are utilized for feature extraction. Furthermore, a wavelet packet transform-based local energy feature extraction method is proposed to solve the deficiencies of the existing methods. Artificial neural networks and support vector machines are trained to validate the effectiveness of different feature extraction methods for flaw signal classification. Experimental results show that the proposed method can extract reliable features to effectively classify the different ultrasonic flaw signals with high accuracy.

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Acknowledgments

We are grateful to the referees for their valuable comments and suggestions. This work is supported by the National Natural Science Foundation of China (No. 61073058), the Natural Science Foundation of JiangXi Province (No. 20122BAB201039), the Foundation of Key Laboratory of Nondestructive Testing (Nanchang Hangkong University), Ministry of Education (No. ZD201229003) and the Ph.D. Programs Foundation of Nanchang Hangkong University (No. EA201104193).

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  1. Key Laboratory of Nondestructive Testing, Ministry of Education, School of Information Engineering, Nanchang Hangkong University, Jiangxi, China

    Peng Yang & Qiufeng Li

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  1. Peng Yang
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  2. Qiufeng Li
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Correspondence to Peng Yang.

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Yang, P., Li, Q. Wavelet transform-based feature extraction for ultrasonic flaw signal classification. Neural Comput & Applic 24, 817–826 (2014). https://doi.org/10.1007/s00521-012-1305-7

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  • DOI: https://doi.org/10.1007/s00521-012-1305-7

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