4.7 Article

Effective Identification and Localization of Single and Multiple Breathing Cracks in Beams under Gaussian Excitation Using Time-Domain Analysis

期刊

MATHEMATICS
卷 10, 期 11, 页码 -

出版社

MDPI
DOI: 10.3390/math10111853

关键词

breathing cracks; multiple cracks; damage localization; non-Gaussianity; random vibration; statistical methods; Shannon entropy

资金

  1. Nantong Science and technology opening cooperation project [BW2021001]
  2. Key R&D Project of Anhui Science and Technology Department [202004b11020026]
  3. Nanjing Science and Technology Project [202002014]
  4. Anhui provincial international joint research center of data diagnosis and smart maintenance on bridge structures [2021AHGHZD01]

向作者/读者索取更多资源

The paper investigates the impact of breathing cracks on the dynamic response of beam structures and proposes the use of higher-order time-domain transformations to detect and localize these non-Gaussian features.
The output response of any intact oscillatory system subjected to a Gaussian excitation is also Gaussian in nature. On the contrary, when the system contains any type of underlying nonlinearity, the output signal is definitely non-Gaussian. In beam structures, the presence of fatigue-breathing cracks significantly influences the dynamic response characteristics under Gaussian excitation. The presence of such cracks alters the response to be nonlinear, and the non-Gaussianity of the system will arise. In order to examine the non-Gaussianity features and ability for the detection and localization of fatigue cracks, several breathing crack identification scenarios in beam-like structures are presented in this paper. The effects of single and multiple breathing cracks corresponding to different boundary conditions on the responses of beams are studied. The results are analyzed based on the higher-order time-domain transformations. Higher-order transformations, namely the skewness and kurtosis coefficients in addition to the Shannon entropy, are exploited to provide dynamic details about the response, which the conventional second-order statistics cannot show. The results exhibit that the proposed methods are robust and immune to noise and can detect and localize breathing cracks with different sensitivities.

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