Feature-selective clustering for ultrasonic-based automatic defect detection in FRP structures
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- 作者：Renchun Youa ; Yuan Yaob ; yyao@mx.nthu.edu.tw" class="auth_mail" title="E-mail the corresponding author ; Jia Shia ; jshi@xmu.edu.cn" class="auth_mail" title="E-mail the corresponding author ; Kaiyi Zhengb ; Kai-Hong Wangb
- 关键词：Ultrasonic defect detection ; Feature selection ; Sparse clustering ; Fiber reinforced polymer
- 刊名：Chemometrics and Intelligent Laboratory Systems
- 出版年：2016
- 出版时间：15 October 2016
- 年：2016
- 卷：157
- 期：Complete
- 页码：35-42
- 全文大小：1156 K
文摘
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Feature-selective clustering is utilized for ultrasonic data processing.
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Effects of attenuation and surface echoes are removed by robust normalization.
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Both the locations and depths of the defects can be identified automatically.
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No historical defect data or expert experience is required.