This presentation introduces a new approach for approaching structural health monitoring (SHM) applications. Starting from a general formulation of Bayes risk, we derive a global optimality criterion within a detection theory framework. The optimal design configuration is then established as the one that minimizes the total computed Bayes Risk (or loss) for the application. While the approach is suitable for many sensing/actuation SHM processes, we focus on the example of active sensing using guided ultrasonic waves by implementing an appropriate general statistical model of the wave propagation and feature extraction process. This example implements both pulse-echo and pitch-catch actuation schemes and takes into account line-of-site visibility and non-uniform damage probabilities over the monitored structure. The design space considers the optimal placement and selection of transducers. We provide a few actuator/sensor placement test problems (within the separate problems of detection and localization) and discuss the optimal solutions generated by the algorithm. Such a scheme allows for proper uncertainty quantification in the design and application process of SHM solutions.
Professor Michael Todd received his BS (1992), MS (1993), and PhD (1996) from Duke University in the Department of Mechanical Engineering and Materials Science. He was then an ASEE. postdoctoral fellow (1996), staff research engineer (1998), and Section Head (2000) at the Naval Research Laboratory in the Fiber Optic Smart Structures Section. He joined the Structural Engineering Department at the University of California San Diego in 2003, where he currently serves as Vice Chair. Among his numerous honors are the Alan Berman NRL Publication Award (1999), NRL Patent Award (2003,2004), UC San Diego Hellman Fellow (2004-2005), and the Structural Health Monitoring Person-of-the-Year Award (2005).