A vibration-based damage detection method for a static laminated composite shell partially filled with fluid (LCSFF) is presented and validated by experiment. The crack damage is simulated using advanced composite damage mechanics in a dynamic finite element model, in which the interaction between the fluid and the composite shell is considered. The accuracy of FE model is first validated by comparing the computed and measured structural frequency response function. Structural damage indexes are constructed and calculated based on energy variation of the structural vibration responses decomposed using wavelet package before and after the occurrence of structural damage. An artificial neural network (ANN) is trained using numerically simulated structural damage index to establish the mapping relationship between the structural damage index and damage status. The test specimen used in experiment contains a cut in its surface made by laser cutting system. Response signals of both intact and damaged specimen are measured and used to construct the corresponding damage indices. The damage status is successfully identified using ANN, indicating that the method adopted in this paper can be applied to online structural damage detection and health monitoring for static LCSFF.
- Composite shell
- Damage detection
ASJC Scopus subject areas
- Ceramics and Composites
- Civil and Structural Engineering