Investigating Compressive Sensing Applications through Real Infrastructures Inertial Signals Analysis

Compressive Sensing (CS) is a sampling technique which, provided the sparsity of the arrival domain and specific properties of the reconstruction matrix, allows rebuilding a vector starting from a significantly small subset of measures. This paves the way to a plethora of applications, ranging from specialized frameworks, such as medical imaging, to more general purposes, such as data compression. Among these, Structural Health Monitoring (SHM) is a primary and current topic, focused on analyzing structures to determine their residual lifespan and their health conditions (material degradation, damage localization, disaster prevention, etc.). In this regard, CS is able to provide accurate results, at the same time limiting the amount of data needed to propagate information between different end-points. Indeed, SHM usually deals with continuous flows of information originating from heterogeneous sensors and locations, often characterized by diverse computational power and signal coverage. In this paper we apply CS to signals coming from two different structures, i.e., a laboratory model and a bridge. Results show that CS is a viable way of reconstructing the considered signals by exploiting a subset of samples while still maintaining a high degree of precision, achieving an average normalized RMSE of 0.12.