主成分分析
支持向量机
铝
信号(编程语言)
超声波传感器
非线性系统
材料科学
模式识别(心理学)
人工智能
结构工程
计算机科学
声学
复合材料
工程类
物理
量子力学
程序设计语言
出处
期刊:Journal of physics
[IOP Publishing]
日期:2021-11-01
卷期号:2066 (1): 012109-012109
被引量:1
标识
DOI:10.1088/1742-6596/2066/1/012109
摘要
Abstract In order to study the application of nonlinear ultrasonic in the quantitative identification of defective aluminum plate, different depth cracks are machined on the aluminum alloy plate with a thickness of 10 mm by wire cutting to simulate the defects in the plate. The normal and defective aluminum plates are selected to establish the experimental model, and the continuous wavelet transform (CWT) is used to extract the characteristic parameters of the aluminum plate nonlinear ultrasonic signal. The dimensions of the data are reduced by principal component analysis (PCA), and the principal component with the top three contribution rate are selected as the characteristic value. Finally, the support vector machine (SVM) algorithm is used to analyze the aluminum alloy plate state and classify the defect signal. The experimental results show that the feasibility of nonlinear ultrasonic signal recognition of aluminum plate defects is verified by combining principal component analysis and support vector machine model.
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