Structure Damage Identification Based on Information Entropy and Bayesian Fusion

Abstract

When processing signals, information entropy theory and data fusion theory have their own advantages. The former can improve the sensitivity of signals, while the latter can superimpose multisource information to correct system deviations and obtain the best identification results. Therefore, we introduce two theories into structural damage identification to improve the reliability of damage identification. First, based on the modal strain energy damage identification index, combined with information entropy and data fusion theory, a fusion entropy index (FE) and an entropy weight fusion index (EWF) are constructed. Then, the simply supported beam and truss structure model are established for damage simulation, which verified that the FE index and EWF index can accurately locate the damage. The polynomial fitting method is used to identify the damage degree of the structure, and the identification results obtained are more accurate. Finally, a simple-supported steel beam model is established in the laboratory for verification and analysis. The results show that the proposed FE index and EWF index have high damage sensitivity, noise resistance, and robustness, and relatively speaking, EWF index damage recognition ability is better. The method proposed in this paper provides an empirical method for practical engineering application.