A novel health monitoring scheme for smart structures

This paper proposes a multiclass nonlinear relevance vector machine (MNRVM) model for health monitoring of smart structures equipped with magnetorheological (MR) dampers. The proposed model will be used to classify the damage statuses of the integrated structure-control systems subjected to ambient excitations. A numerical model of a three-story building equipped with an MR damper is studied to demonstrate the effectiveness of the proposed health monitoring schemes. Dynamic responses of the smart structures subjected to random excitations are measured. Discrete wavelet transform is applied to the obtained data to compress and filter noises of the measured data. As a next step, the compressed and de-noised signals are used for developing autoregressive (AR) models. Then the MNRVM is applied to the AR-coefficient data to classify them with respect to the damage statuses. As a baseline, the support vector machine (SVM) algorithm is considered. It is demonstrated that the proposed MNRVM framework is effective in classifying various damage statuses of the nonlinear smart structures subjected to ambient excitations. Simulation results also show that the MNRVM performs similar to the SVM with faster computation time.