Structural assessment and damage identification algorithms using binary data

One of the challenges in structural health monitoring (SHM) is the power required for sensors to collect and communicate data. Self-powered sensors are able to harvest power by from their environment, i.e., strain and vibration of the host structure. However, the harvested power with current technology is still limited and improving the system's efficiency requires reducing the power budget. A way to minimize the communication power demand is to transmit the minimum amount of information, namely one bit. The binary signal can be generated at a sensor node according to a local rule based on physical measurements, but interpretation at the global level requires dealing with discrete binary (1 or 0) data, which implies system information with reduced resolution. This study presents an investigation on approaches for the interpretation of such kind of binary data for use in structural assessment and damage identification. Pattern recognition (PR) methods based on image data analysis were adapted for the study. The methods used were classifiers based on deviation of patterns with respect to each other, twodimensional principal component analysis, and two-dimensional linear discriminant analysis. The PR methods and the performance of the interpretation algorithms were evaluated by using virtual data from finite element simulations on an aluminum plate. The ability for each of the PR methods to identify service demands, load variations and localized material degradation was evaluated. Results indicate that PR methods can be used as damage identification algorithms for binary data sets in novel wireless self-powered sensor networks.