https://orcid.org/0000-0002-5564-6576
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ABSTRACT
A 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.
Acknowledgments
This 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 statement
No potential conflict of interest was reported by the authors.