Structural damage information amplification methodology based on cluster Mahalanobis distance cumulant and IMFs

Damage identification is a key issue in structural health monitoring and safety state assessment. Damage information extraction from measurements is difficult due to environmental noise and ambient excitation, which greatly reduce the accuracy of structural damage identification. In this study, a structural damage information amplification method based on Mahalanobis distance cumulant (MDC) and intrinsic mode function (IMF) was proposed. Firstly, the measurements of the structure were decomposed by the empirical mode decomposition (EMD) technology, and the relative energy change rate of each order IMF was used to screen the damage-sensitive component. By comparing the damage identification vectors constructed by MDC values from the raw measured parameters, the damage-sensitive component can obtain more damage information making damage detecting efficient. Meanwhile, the area difference of the probability density function of MDC values was used to assess the damage information amplification to determine the appropriate level of accumulation by using the cyclic analysis procedure. The model simulations and experiments were carried out to verify the method. The results showed that the selected damage-sensitive component used for constructing MDC values can amplify the damage information and make better accuracy for damage identification.