Damage detection in an experimental bridge model using Hilbert-Huang transform of transient vibrations

Abstract

This paper presents the health monitoring of an experimental bridge model using Hilbert–Huang transform of transient vibration data. The Hilbert–Huang transform involves decomposition of vibration data into ‘intrinsic mode functions’ through the process of empirical mode decomposition. The Hilbert transform of intrinsic mode functions yields magnitude and frequency of oscillations as a function of time, which is called the Hilbert spectrum. Marginal Hilbert spectrum is obtained by integration of the Hilbert spectrum over time. A 4 × 1 m single span-bridge instrumented with 10 wireless sensor nodes is used for the study. Three levels of damage are introduced by removing bolts connecting the midspan floor beam with one of the girders. Bridge vertical accelerations are measured as a wheel and axle is gently rolled across its length (called one ‘run’) to simulate passing vehicles. The Hilbert spectrum for multiple runs, joint time–frequency analysis for individual runs, marginal Hilbert spectrum, and instantaneous phase were examined for the baseline (healthy) bridge and the three damage cases. The results demonstrate that the method can detect and locate damage under transient vibration loads. Copyright © 2011 John Wiley & Sons, Ltd.