Embedded subspace-based modal analysis and uncertainty quantification on wireless sensor platform PEGASE

Embedded subspace-based modal analysis and uncertainty quantification on wireless sensor platform PEGASE
Vincent Le Cam, Michael Döhler, Mathieu Le Pen, Ivan Guéguen, Laurent Mevel. Embedded subspace-based modal analysis and uncertainty quantification on wireless sensor platform PEGASE. EWSHM - 8th European Workshop on Structural Health Monitoring, Jul 2016, Bilbao, Spain. <hal-01344213>

Abstract : Operational modal analysis is an important step in many methods for vibration-based structural health monitoring. These methods provide the modal parameters (frequencies, damping ratios and mode shapes) of the structure and can be used for monitoring over time. For a continuous monitoring the excitation of a structure is usually ambient, thus unknown and assumed to be noise. Hence, all estimates from the vibration measurements are realizations of random variables with inherent uncertainty due to unknown excitation, measurement noise and finite data length. Estimating the standard deviation of the modal parameters on the same dataset offers significant information on the accuracy and reliability of the modal parameter estimates. However, computational and memory usage of such algorithms are heavy even on standard PC systems in Matlab, where reasonable computational power is provided. In this paper, we examine an implementation of the covariance-driven stochastic subspace identification on the wireless sensor platform PEGASE, where computational power and memory are limited. Special care is taken for computational efficiency and low memory usage for an on-board implementation, where all numerical operations are optimized. The approach is validated from an engineering point of view in all its steps, using simulations and field data from a highway road sign structure.