A novel unsupervised deep learning model for global and local health condition assessment of structures

摘要

A methodology is described for global and local health condition assessment of structural systems using ambient vibration response of the structure collected by sensors. The model incorporates synchrosqueezed wavelet transform, Fast Fourier Transform, and unsupervised deep Boltzmann machine to extract features from the frequency domain of the recorded signals. A probability density function is used to create a structural health index (SHI). This index can be used to assess both the global and local health conditions of the structure. A beauty of the proposed model is that it does not require costly experimental results to be obtained from a scaled version of the structure to simulate different damage states of the structure. Only ambient vibrations of the healthy structure are needed. In the absence of ambient vibrations, they can be simulated stochastically using structural properties and the probability theory. The effectiveness of the proposed model is illustrated employing experimental data obtained on a shake table in Hong Kong.