Identification of modal macro-strain vector based on distributed long-gage FBG sensors under ambient vibration

Recent reports show that modal macro-strain vector (MMSV) obtained by using distributed long-gage FBG sensors is an effective indicator for damage detection. However, in previous researches, MMSV was always obtained under impulsive load such as hammer impact. In structural health monitoring of real large-scale structures, however, it is often very difficult to apply such impulsive load. This paper therefore introduces a new method to abstract MMSV under ambient excitation. Theoretical deduction reveals that MMSV can be uniquely determined by auto-spectrum of dynamic macro-strain responses under ambient excitation. Both numerical simulation and experiment were conducted to verify the proposed methods. Simulation results showed that that the identified frequencies and MMSV vectors under random excitation are in good agreement with those obtained from theoretical analysis, while experimental results showed the identified frequencies and MMSV agreed well with those obtained using point impulsive excitation.