Cluster-based Vibration Analysis of Structures with GSP

This work describes a divide-and-conquer strategy suited for vibration monitoring applications. Based on a low-cost embedded network of Micro-ElectroMechanical (MEMS) accelerometers, the proposed architecture strives to reduce both power consumption and computational resources. Moreover, it eases the sensor deployment on large structures by exploiting a novel clustering scheme which consists of unconventional and non-overlapped sensing configurations. Signal processing techniques for inter and intra-cluster data assembly are introduced to allow for a full-scale assessment of the structural integrity. More specifically, the capability of graph signal processing is adopted for the first time in vibration-based monitoring scenarios to capture the spatial relationship between acceleration data. The experimental validation, conducted on a steel beam perturbed with additive mass, revealed high accuracy in damage detection tasks. Deviations in spectral content and mode shape envelopes were correctly revealed regardless of environmental factors and operational uncertainties. Furthermore, an additional key advantage of the implemented architecture relies on its compliance with blind modal investigations, an approach which favors the implementation of autonomous smart monitoring systems.