Abstract Summary
The transmissibility function (TF) has been widely reported to be a damage-sensitive but excitation-insensitive damage feature. However, most TF-based novelty detection approaches fail to accommodate various uncertainties with a proper probabilistic model. Making full use of the multivariate complex-valued Gaussian ratio probabilistic model of TFs, a new damage detection method is proposed in this study by integrating the advantages of TF and hierarchical clustering. Different from hierarchical clustering for damage detection conducted on the basis of deterministic distance as a similarity metric, a multivariate Bhattacharyya distance is used to account for the uncertainty and correlation of multiple TFs. An analytical approximation of Bhattacharyya distance is efficiently derived by applying an efficient asymptotic expansion to avoid numerical integration. A case study is used to validate the performance of the proposed damage detection method. Results show that the method can avoid specifying the number of clusters and show more robust performance compared to the clustering based on deterministic distance of a univariate TF.
