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所属单位：自动化学院

发表刊物：OPTIK

关键字：Laser ultrasonic Quantitative recognition Wavelet packet fusion Dimension reduction SVM classification

摘要：With the development of industrialization and modern technology, laser ultrasonic technique is more and more used in aerospace, machinery and electronics, measurements, metallurgy chemical engineering, materials science, railway transportation, bridge engineering, etc. In order to maintain the excellent characteristics of new materials (such as thermal properties, mechanical properties, chemical properties and optical properties, material structure must be early diagnosed and monitored before properties change. Nondestructive Testing technology plays a great role in monitoring reliability of industry products. This paper proposes a new feature selection method based on wavelet packet algorithm, and applies SVM (support vector machine) for quantitative classification on the ultrasonic echo data generated by cracks in Laser ultrasonic experiment. By combining the nondestructive device and dimension reduction method in machine learning, this paper analyses the scatter plot of two cracks in 2d and the fitting surface in 3d and give the quantitative index for determining the performance of used methods. (C) 2017 Elsevier GmbH. All rights reserved.

ISSN号：0030-4026

是否译文：否

发表时间：2017-01-01

合写作者：Yi, Qiuji,郭瑞鹏,Li, Suyuan,姜莹莹

通讯作者：王海涛