Charles R. Farrar

TL;DR: A framework is offered that aims to provide the engineer with a qualitative approach for choosing from among a suite of candidate SHM technologies, and is demonstrated on a problem commonly encountered when developing SHM systems: selection of a damage classifier.

TL;DR: A novel approach using state-space probability models to infer the conditions underlying each time step is introduced, allowing the definition of a damage metric robust to environmental and operational variation.

TL;DR: In this article, a piezoelectric active-sensing device was used to detect the self-loosening mode in bolted joint connections, which is especially problematic when the bolted joint is in an inaccessible location, a hostile environment, or a part of a machine whose shutdown would be costly.

TL;DR: A unique combination of time series analysis, neural networks, and statistical inference techniques is developed for damage classification explicitly taking into account these natural variations of the system in order to minimize false positive indication of true system changes.

TL;DR: This work has developed a relay-based hardware that can serve as both a multiplexer and general-purpose signal router with special consideration given to piezoelectric active-sensing health monitoring approaches, and implemented this device as an expandable design that allows for easy scalability depending upon the size of the structure.