A Compressed Sensing Approach in Structural Damage Identification

Accurate detection of time and location of damage in its early stages has motivated the researchers to develop several damage diagnosis methods in recent decades. With current improvements in monitoring hardware, many of the previously developed Structural Health Monitoring (SHM) methods encounter the BIG DATA problem in processing the data collected through dense sensor networks. Therefore, it is vital to improve the efficiency and scalability of today’s SHM procedures parallel to data acquisition enhancements. Toward this end, this paper presents a data-driven damage detection methodology based on compressed sensing techniques. The objective of the paper is accurate damage localization in a structural component instrumented with a dense sensor network, by processing data only from a subset of sensors. In this method, first a set of sensors from the network are randomly sampled. Measurements from these sampled sensors are processed to extract damage sensitive features. These features undergo statistical change point analysis to establish a new boundary for a local search of damage location. As the local search proceeds, probability of the damage location is estimated through a Bayesian procedure with a bivariate Gaussian likelihood model. The decision boundary and the posterior probability of the damage location are updated as new sensors are added to processing subset and more information about location of damage becomes available. This procedure is continued until enough evidence is collected to infer about damage location. Performance of this method is evaluated using a finite element model of a cracked gusset plate connection. Preand post-damage strain distributions in the plate are used for damage diagnosis.