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. Figure 7. Magnitude versus frequency for sensor 10 (17 runs for each case): (a) baseline, (b) damage level 1, (c) damage level 2, and (d) damage level 3. 8 A. KUNWAR ET AL.

Dynamic Monitoring and Vibration Analysis of Ancient Bridges by Ground-Based Microwave Interferometry and the ESMD Method

“…Furthermore, compared with the projected displacement curves of Mode 1 for point Rbin46_N and point Rbin64_N, more sudden variations appear on the projected displacement curves of Mode 1 for point Rbin46_L and point Rbin64_L. These results indicate that the projected displacement curve of Mode 1 is the main projected displacement variation related to the bridge, which is the same as HHT [2,21]. Moreover, in order to estimate the efficiency of the instantaneous frequency of Mode 1 from noisy data to evaluate the instantaneous dynamic response of Zhaozhou Bridge, the instantaneous frequencies F1 to F4 with a mean (MN) greater than 0.1 Hz, corresponding to the decomposed Mode 1 to Mode 4 for each set of time series displacement, were selected to make a comparison.…”

A State of the Art Review of Modal-Based Damage Detection in Bridges: Development, Challenges, and Solutions

“…Additionally, many SHM applications of the HHT have been conducted on data obtained from numerical models [128] and laboratory-based experiments [129]. However, recent research by Moughty & Casas [130] has focused on the potential of using the HHT to detect early stage damage in bridges subjected to operational loading.…”

Identification of sudden stiffness changes in the acceleration response of a bridge to moving loads using ensemble empirical mode decomposition

“…The latter is a popular way to identify, locate and quantify deterioration based on the principle that damage affecting the mechanical properties of the structure will change the dynamic properties of the structure and it will allow the bridge operator to take adequate action. These vibration-based methods typically require many sensors and long records to distinguish between true damage and deviations from the expected 'healthy' results that do not necessarily imply damage (i.e., due to forced vibration and environmental conditions) [1][2][3].…”

“…The first question that arises is how to characterize a non-linear response. This has been addressed in a wide range of mechanical and civil engineering applications via methods such as the Continuous Wavelet Transform (CWT) [4,5], Unscented Kalman Filter (UKF) [6,7], Hilbert-Huang Transform (HHT) [2,[8][9][10] and others. The HHT utilises Empirical Mode Decomposition (EMD) to estimate the Instantaneous Frequency (IF) and the Instantaneous Phase (IP).…”

“…Applications of the EMD method to nonlinear and non-stationary signals for wide range of signals from speech characterisation to ocean wave data have shown its versatility and robustness in detecting inconsistencies. Several authors have attempted to identify nonlinearity by applying EMD and HT to a nonlinear damaged structures [2,[11][12][13]. However, detractors of EMD argue its lack of physical meaning due to mode mixing in the IMFs.…”