Investigation of Ensemble Empirical Mode Decomposition Applied for Composite Multiscale Cross-Sample Entropy Analysis

Entropy is an approach for computing uncertainty. Over recent decades, entropy has been developed to measure complexity in biological signals. With the development of entropy, the diversity of its application has increased. In civil engineering, entropy has been combined with structural health monitoring (SHM) for damage detection. Moreover, as algorithms for entropy have continued to be developed, multiscale entropy (MSE), composite multiscale entropy (CMSE), and composite multiscale cross-sample entropy (CMSCE) have been proposed in succession. The aim of this study was to optimize CMSCE to enhance SHM performance. To reduce the influence of ambient noise, ensemble empirical mode decomposition (EEMD) can be used to filter structural dynamic signals. Therefore, the first mode of structure was extracted by using EEMD for entropy analysis and evaluation of damage assessment performance. A numerical simulation of a seven-story steel structure was run to verify the efficacy of EEMD and calculate damage indices for detection of damaged locations. Through this simulation, signals with and without EEMD were compared. As the result, it can be observed that the performance of damage identification was improved in low floors by CMSCE with EEMD.