Optimal stochastic data collection and transmission policy for self-sustaining structural health monitoring sensing architectures
Katherine A. Flanigan, Lindsay K. Graff · 2021
Data acquisition methods for structural health monitoring (SHM) historically rely on schedule-based or transmit-all data collection strategies. For sensing systems that are self-sustaining (e.g. those relying on harvested energy), these data collection methods are unable to explicitly account for the availability of energy and the value of structural response data. As a result, sensing systems often fail to capture and transmit key data to end users and require an excessive amount of time to characterize statistical parameters of response data. As structural monitoring data is increasingly incorporated into decision-making processes for asset management, there is a need for an automated data collection and transmission strategy that facilitates the characterization of the statistical parameters of structural response data with minimum variance so that bridge managers can increase the frequency with which they track structural condition without compromising accuracy. This paper presents a stochastic data collection and transmission policy that minimizes the variance of estimated component parameters of a measured process subject to constraints imposed by a sensing node’s energy and data buffer sizes, stochastic models of the incoming energy and event arrivals, the value of data, and temporal death. This work then extends the optimal data collection and transmission policy to a proposed SHM application in which a standard steel pin-and-hanger assembly is monitored to track the safety of the component with respect to its net-section yielding limit state. The proposed monitoring system utilizes sensing nodes that operate using harvested solar energy and are subject to stringent energy constraints due to the size of the solar panels and availability of incoming energy, geographic location, and battery size. Numerical results are presented and illustrate the gains achieved by implementing the optimal policy, meaning the optimal policy minimizes the variance of the estimated measured process parameter, and in turn, minimizes the variance of the measured reliability index.