支持向量机
涂层
小波
小波包分解
环氧树脂
网络数据包
计算机科学
人工智能
特征提取
模式识别(心理学)
材料科学
太赫兹辐射
小波变换
复合材料
光电子学
计算机网络
作者
Wanli Tu,Shuncong Zhong,Qiukun Zhang,Yi Huang
标识
DOI:10.1080/10589759.2023.2214670
摘要
ABSTRACTABSTRACTA reliable and effective diagnosis of coating structure is important for further maintenance. For the problem of slow detection and identification using terahertz non-destructive testing technology in the industrial inspection, a rapid diagnosis algorithm based on the wavelet packet energy and support vector machine method was developed for quick evaluation of the epoxy protective coating. The process mainly included time domain signal acquisition of various epoxy protective coating samples detected by a terahertz pulse imaging system, wavelet packet energy parameters extraction as the diagnosis feature vectors, classification model establishment based on the support vector machine algorithm and coating status evaluation using a three-class classifier. The influence on classification accuracy by the various feature vectors inputs with the support vector machine classifier was analysed. Satisfying results were achieved when the relative wavelet packet energy was taken as diagnostic features. A strong defective area could be quickly identified and more detail targeted analysis could be implemented as needed. The time spent was significantly reduced compared to the terahertz imaging of the whole area along with the manual judgement. The analysis indicated that the proposed method would be very useful and can be effectively employed for the coating monitoring application.KEYWORDS: Epoxy protective coatingterahertz non-destructive testing techniquewavelet packet energysupport vector machine AcknowledgmentsThis work was supported in part by the National Natural Science Foundation of China (Nos. 52101355 and 51905102) and in part by the Fujian Provincial Natural Science Foundation (No. 2019I0004).Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThe work was supported by the National Natural Science Foundation of China [51905102]; Natural Science Foundation of Fujian Province [2019I0004].
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