Optimal Actuator and Sensor Placement for Active Sensing

In active sensing, actuators are used to impart user-defined energy into a structure from which resulting sensor responses may be mined for useful information regarding the structure’s health. Optimal actuator-sensor placement works to maximize the observability of this information given two primary constraints: the number of actuators and sensors available for the network and the power available for interrogation. We present a generalized detection theory-based methodology for optimal actuator and/or sensor placement. Placements are chosen to maximize one of three presented performance measures, one based on classical probability and two on Bayesian. Generalization is achieved by assuming the critical detection region is a subset of the space spanned by an arbitrary set of extracted SHM features. The methodology is applied to the particular case of active sensing by applying the statistics of the application-specific feature set. In this study, we considered two sensing schemes: pitch-catch, where the actuator and sensor are distinct, and pulse-echo, where the actuator and sensor are one in the same. The method has the capability of optimizing about non-uniform damage probability distributions, concave structure geometry, and potential sensor failure. The optimization space was searched using a genetic algorithm specifically tailored for sensor placement. We provide five example actuatorsensor placement scenarios and the