The Accuracy and Computational Efficiency of the Loewner Framework for the System Identification of Mechanical Systems

Abstract

The Loewner framework has recently been proposed for the system identification of mechanical systems, mitigating the limitations of current frequency domain fitting processes for the extraction of modal parameters. In this work, the Loewner framework computational performance, in terms of the elapsed time till identification, is assessed. This is investigated on a hybrid, numerical and experimental dataset against two well-established system identification methods (least-squares complex exponential, LSCE, and subspace state space system identification, N4SID). Good results are achieved, in terms of better accuracy than LSCE and better computational performance than N4SID.

Development of Recursive Subspace Identification for Real-Time Structural Health Monitoring under Seismic Loading

“…In addition, damage detection involves determining the changes in the dynamic characteristics of a structure, such as natural frequencies and damping ratios, and using this information to detect, localize, and quantify the structural damage. Both of them can be performed in diferent domains; for example, Civera et al recently proposed frequency-domain techniques [9,10], and Dessena et al recently developed time-domain techniques for SHM [11,12]. Most importantly, online or real-time SHM combining these approaches can evaluate the damage or deterioration of civil structures, give insight into the health and performance of a structural system, and provide valuable information for decision-making on maintenance and repair.…”