SOURCE-LINKED INTELLIGENCE

Fast-varying Natural Frequencies and Damping Ratio Identification for Linear Time-Varying System

arXiv · AI, language, vision and robotics · article · Sep 17, 2026 · UTC

This work proposes a physics-enhanced machine learning approach for the system identification of Linear Time-Varying (LTV) systems under time-varying operating conditions in terms of fast-varying natural frequencies and damping ratios by combining a long short-term memory network with an Extended Kalman Filter (EKF). The proposed approach uses vibration data (displacement and velocity measurements), domain knowledge of modal damping ratios, and a physics-based model that can yield an approximate natural frequencies time-dependency model. The approach is validated using synthetic data generated

Read original source ↗ Open in workspace

recordType

paper

region

Global