Compressed change detection for structural health monitoring

The problem of detection of a sparse number of damages in a structure is considered. The idea relies on the newly developed framework for compressed change detection [1], which leverages the unique covering property of identifying codes to detect statistical changes in stochastic phenomena. Since only a small number of damage scenarios can occur simultaneously, change detection is applied to responses of pairs of sensors that form an identifying code over a learned damage-sensing graph. An asymptotic analysis of the detection delay and the probability of detection of the proposed approach is provided when the number of damage scenarios is large.