Structural health monitoring techniques have gained an increasing interest in many areas, especially in aerospace engineering. This is motivated by the benefits of enhancing safety, reducing maintenance costs and lightweight design. One important topic for structural health monitoring methods is to find a balance in the number of sensors, the accuracy in damage detection and the requirement of a high-fidelity finite element model. This dissertation proposes a vibration-based SHM method to deal with the problem. The main work is presented in the following. First, the vibration amplitudes at nodal points, also referred as node displacement or NODIS, are studied in this work. The NODIS is an efficient structural damage indicator which can achieve real-time damage identification with a relatively small number of sensors. The sensitivity curves of each NODIS under different damage parameters are investigated for both beam and plate structures. Secondly, the Bayesian probabilistic approach is adopted to increase the accuracy in damage detection and solve the ill-conditioned problem due to limited measurements. After that, a perturbation-based surrogate model is derived for both beam and plate structures to replace the computationally expensive model in the Bayesian framework. Then, the effectiveness of the NODIS is validated with the decision-tree-based damage detection method. Furthermore, the performance of the NODIS-based Bayesian framework with the perturbation method is evaluated and compared with finite element results. In addition, the architecture of the proposed method is illustrated. At last, the proposed method is applied to a sailplane supporting beam under different environmental temperatures and a composite sandwich plate with different grinding depths.weiterlesen