A Comparative Study on Structural Damage Detection Using Derivatives of Laser-Measured Flexural and Longitudinal Vibration Shapes
Source abstract
Abstract
Structural damage detection methods relying on laser-measured vibration shapes have become a research focus in the past decade. For damage in a beam/bar with reduced cross-sectional dimensions, such as a notch, it causes changes in both its bending stiffness and axial stiffness in the damage region. Such stiffness changes can induce discontinuities in derivatives of flexural and longitudinal vibration shapes, whereby the damage can be detected and located. Derivatives of flexural vibration shapes have been widely used for structural damage detection, whereas derivatives of longitudinal vibration shapes were recently proposed for structural damage detection and have attracted much less attention. Although it is difficult to excite and measure longitudinal vibration, it can be useful for detecting damage in such a structure as a cable, since flexural vibration of the cable is mainly governed by its tension and not its bending stiffness. In this study, capabilities of derivatives of laser-measured flexural and longitudinal vibration shapes in structural damage detection are comprehensively compared. In particular, to overcome their common deficiency of being susceptible to environmental noise interference, the multiscale analysis based on wavelet transform is integrated into derivatives of vibration shapes to enhance their robustness against noise interference. Analytical and experimental validation shows that compared with commonly used derivatives of flexural vibration shapes, derivatives of longitudinal vibration shapes have the same capability in detecting damage in beam/bar-type structural components.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Structural Health Monitoring Techniques · Optical measurement and interference techniques · Ultrasonics and Acoustic Wave Propagation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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