Data to decisions: Real-time structural assessment from sparse measurements affected by uncertainty

Abstract

This paper proposes a data-to-decisions framework—a methodology and a computational strategy—to assist real-time decisions associated with structural monitoring and informed by incomplete, noisy measurements. The data-to-decision structural assessment problem is described in terms of sensor data measurements (such as strain components) and system capabilities (such as failure indices). A MultiStep Reduced-Order Modeling (MultiStep-ROM) strategy tackles the time-critical problem of estimating capabilities from measured data. The methodology relies on an offline-online decomposition of tasks, and combines reduced-order modeling, surrogate modeling, and clustering techniques. The performance of the approach is studied for the case of uncertain measurements arising from spatially distributed sensors over a wing panel. Both sensor noise and sensor spatial sparsity affect the quality of the information available online. The discussion is supported by three investigations that explore the efficiency of the online procedure for multiple combinations of quantity and quality of sensed data. The method is demonstrated for an unmanned aerial vehicle composite wing panel undergoing local degradation of its structural properties. Keywords: data-driven reduced-order modeling; data-driven structural assessment; data-to-decisions; sparse and uncertain measurements; real-time capability assessment; self-aware vehicle.