Nonlinear Lamb Wave for Structural Incipient Defect Detection with Sequential Probabilistic Ratio Test | Zendy

Hanxin Chen | Zendy; Mingming Liu | Zendy; Yongting Chen | Zendy; Shaoyi Li | Zendy; Yuzhuo Miao | Zendy

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Nonlinear Lamb Wave for Structural Incipient Defect Detection with Sequential Probabilistic Ratio Test

Author(s) -

Hanxin Chen,

Mingming Liu,

Yongting Chen,

Shaoyi Li,

Yuzhuo Miao

Publication year - 2022

Publication title -

security and communication networks

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.446

H-Index - 43

eISSN - 1939-0114

pISSN - 1939-0122

DOI - 10.1155/2022/9851533

Subject(s) - lamb waves , nonlinear system , acoustics , ultrasonic sensor , probabilistic logic , harmonics , computer science , sequential probability ratio test , harmonic , ultrasonic testing , signal (programming language) , materials science , structural engineering , algorithm , artificial intelligence , engineering , surface wave , physics , telecommunications , electrical engineering , quantum mechanics , voltage , programming language

The incipient defect is difficult to be identified by ultrasonic signal analysis. The nonlinear ultrasonic method based on the nonlinear Lamb wave principle is proposed by establishing a nonlinear Lamb wave ultrasonic inspection platform. The optimal Lamb wave parameters are obtained for the incipient fatigue material defects. The aluminum alloy board with 3 mm thickness under the different fatigue tensile cycles is tested. The nonlinear ultrasonic signals are analyzed to obtain second harmonic signals. The intelligent diagnosis method for incipient material degrade is proposed based on the Sequential Probability Ratio Test (SPRT). The sequential probabilistic ratio test (SPRT) algorithm is carried out to classify and identify the second harmonics of four different fatigue damages. The results show that the method about with nonlinear Lamb wave analysis with SPRT is effective and reliable for the incipient material microdefect degradation.

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