Dynamic Imaging: Real-Time Detection of Local Structural Damage with Blind Separation of Low-Rank Background and Sparse Innovation

AbstractReal-time close-up imaging (filming or video surveillance) of structures is used to automate detection of local component-level damage by exploiting the spatiotemporal data structure of the multiple temporal frames of structures. Specifically, the multiple frames are decomposed into a superposition of a low-rank background component and a sparse innovation (dynamic) component by a technique called principal component pursuit (PCP, or robust principal component analysis). The low-rank component represents the irrelevant, temporally correlated background of the multiple frames, whereas the sparse innovation component indicates the salient, evolutionary damage-induced information. The sparse innovation component is then quantitatively measured for continuous alert and indication of the damage evolution. It is a data-driven and unsupervised (blind) approach that requires no parametric model or prior structural information for calibration. In addition, PCP has an overwhelming probability of success und...