Structural Degradation Monitoring using Covariance-driven Wavelet Packet Signature

A covariance-driven wavelet packet signature extraction method is proposed for monitoring of structural degradation under random ambient excitation. Advantageous features of this method are it only requires measuring response at one location of the structure and no structural mathematical model is needed. Assuming that the excitation forces are stationary Gaussian white noises, the covariance of measured response is calculated and wavelet packet decomposed. A damage indicator is then formulated to extract discriminate information from the constructed wavelet packet component energies. A threshold for a damage alarm is established according to the statistical properties and the one-sided confidence limit of the damage indicator from successive measurement. For demonstration, a numerical study is performed on the health monitoring of the benchmark steel-frame scale structure proposed by the IASC-ASCE Structural Health Monitoring Task Group. Four damage cases, involving different levels of damage severity at various locations, are introduced. The results show that the health condition of the frame model can be accurately monitored by the proposed method. It is also seen that the proposed method is quite insensitive to measurement noise. The monitoring results are virtually unaffected even when the response is contaminated with measurement noise of the same magnitude as that of the signal.