Optimal sampling in damage detection of flexural beams by continuous wavelet transform

11th International Conference on Damage Assessment of Structures

物理学;材料科学

Basu, B.^1 ; Broderick, B.M.^1 ; Montanari, L.^2 ; Spagnoli, A.^2

Dept. Civil, Structural and Environmental Engineering, Trinity College, Dublin 2, Ireland^1

DICATeA, University of Parma, Parco Area delle Scienze 181/A, Parma, Italy^2

关键词: Continuous Wavelet Transform;    Continuous wavelet transforms;    Damage Identification;    Measurement techniques;    Simple supported beams;    Spatial displacement;    Structural damages;    Vibration based damage identifications;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/628/1/012032/pdf
DOI  :  10.1088/1742-6596/628/1/012032

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

Modern measurement techniques are improving in capability to capture spatial displacement fields occurring in deformed structures with high precision and in a quasi-continuous manner. This in turn has made the use of vibration-based damage identification methods more effective and reliable for real applications. However, practical measurement and data processing issues still present barriers to the application of these methods in identifying several types of structural damage. This paper deals with spatial Continuous Wavelet Transform (CWT) damage identification methods in beam structures with the aim of addressing the following key questions: (i) can the cost of damage detection be reduced by down-sampling? (ii) what is the minimum number of sampling intervals required for optimal damage detection ? The first three free vibration modes of a cantilever and a simple supported beam with an edge open crack are numerically simulated. A thorough parametric study is carried out by taking into account the key parameters governing the problem, including level of noise, crack depth and location, mechanical and geometrical parameters of the beam. The results are employed to assess the optimal number of sampling intervals for effective damage detection.