An unsupervised statistical damage detection method for structural health monitoring (applied to detection of delamination of a composite beam)

摘要

The present paper proposes a new damage diagnosis method for structural health monitoring that does not require data on damaged-state structures. Structural health monitoring is an essential technology for aged civil structures and advanced composite structures. For damage diagnostic methods, most current structural health monitoring systems adopt parametric methods based on modeling, or non-parametric methods such as artificial neural networks. The conventional methods require FEM modeling of structure or data for training the damaged-state structure. These processes require judgment by a human, resulting in high cost. The present paper proposes a new automatic damage diagnostic method for structural health monitoring that does not require these processes by using a system identification and statistical similarity test of the identified systems using an F-test.As an example of damage diagnosis using the new method, the present study describes delamination detection of a CFRP beam. System identification among the strain data measured on the surface of a composite beam is used for damage diagnosis. The results show that the new statistical damage diagnostic method successfully diagnoses damage without the use of modeling and without learning data for damaged structures.