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Proceedings Paper

Fatigue crack detection by nonlinear spectral correlation with a wideband input
Author(s): Peipei Liu; Hoon Sohn
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Paper Abstract

Due to crack-induced nonlinearity, ultrasonic wave can distort, create accompanying harmonics, multiply waves of different frequencies, and, under resonance conditions, change resonance frequencies as a function of driving amplitude. All these nonlinear ultrasonic features have been widely studied and proved capable of detecting fatigue crack at its very early stage. However, in noisy environment, the nonlinear features might be drown in the noise, therefore it is difficult to extract those features using a conventional spectral density function. In this study, nonlinear spectral correlation is defined as a new nonlinear feature, which considers not only nonlinear modulations in ultrasonic waves but also spectral correlation between the nonlinear modulations. The proposed nonlinear feature is associated with the following two advantages: (1) stationary noise in the ultrasonic waves has little effect on nonlinear spectral correlation; and (2) the contrast of nonlinear spectral correlation between damage and intact conditions can be enhanced simply by using a wideband input. To validate the proposed nonlinear feature, micro fatigue cracks are introduced to aluminum plates by repeated tensile loading, and the experiment is conducted using surface-mounted piezoelectric transducers for ultrasonic wave generation and measurement. The experimental results confirm that the nonlinear spectral correlation can successfully detect fatigue crack with a higher sensitivity than the classical nonlinear coefficient.

Paper Details

Date Published: 5 April 2017
PDF: 11 pages
Proc. SPIE 10170, Health Monitoring of Structural and Biological Systems 2017, 101701Y (5 April 2017); doi: 10.1117/12.2258746
Show Author Affiliations
Peipei Liu, KAIST (Korea, Republic of)
Hoon Sohn, KAIST (Korea, Republic of)

Published in SPIE Proceedings Vol. 10170:
Health Monitoring of Structural and Biological Systems 2017
Tribikram Kundu, Editor(s)

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