Signature extraction from the dynamic responses of a bridge subjected to a moving vehicle using complete ensemble empirical mode decomposition

Feng Xiao,Gang S Chen,J Leroy Hulsey,Wael Zatar

doi:10.1177/1461348419872878

Abstract

Technology that measures bridge responses when a vehicle is crossing over it for structural health monitoring has been under development for approximately a decade. Most of the proposed methods are based on identification of the dynamic characteristics of a bridge such as the natural frequency, the mode shapes, and the damping. Specifically, many time–frequency domain approaches have been used to extract complex spectrum signatures from the complicated vibrations of a bridge due to the interactions of a vehicle with the bridge, which usually involves nonlinear, nonstationary, stochastic, and impact vibrations. In this paper, a method known as complete ensemble empirical mode decomposition with adaptive noise is applied for the first time to analyze the acceleration response of a bridge to a moving vehicle, and the purpose is to extract the spectrum signature of the vehicle–bridge response for structural health monitoring. The time–frequency Hilbert-Huang transform (HHT) spectrum of the decomposed mode from complete ensemble empirical mode decomposition with adaptive noise is presented. The results are well-correlated with finite element analysis. The advantages of the complete ensemble empirical mode decomposition with adaptive noise method are demonstrated in comparing the data from conventional methods, including power spectra, spectrograms, scalograms, and empirical mode decomposition.

Highlights

The use of vehicle–bridge interaction data for structural health monitoring (SHM) has received considerable interest in the last decade.[1,2,3,4,5,6] Compared with traditional bridge health monitoring, a vehicle–bridge interaction data-based approach allows target bridges to be monitored or assessed under operating conditions
The results show that there is more than 10% difference between the estimated specific transient frequencies from empirical mode decomposition (EMD) and complete EEMD with adaptive noise (CEEMDAN)
We demonstrated that the bridge response identified using CEEMDAN is different from the results obtained from EMD

Summary

Introduction

The use of vehicle–bridge interaction data for structural health monitoring (SHM) has received considerable interest in the last decade.[1,2,3,4,5,6] Compared with traditional bridge health monitoring, a vehicle–bridge interaction data-based approach allows target bridges to be monitored or assessed under operating conditions. Various modified bilinear time– frequency distributions including the Cohen and the affine class distributions may suppress the negative effects of cross-terms, but they will compromise time–frequency resolution and auto-term integrity.[11,12] In comparison with the Cohen and the affine class distributions of fixed kernel functions, the adaptive optimal kernel method can suppress the cross-terms more effectively with better time–frequency resolution.[13] Wigner–Ville and Choi–Williams distributions introduce many distortions due to interference terms and negative components Conventional spectrum analysis methods such as spectrograms and scalograms have low resolution for this type of complex problem due to cross-term interference, and cannot effectively reveal the Journal of Low Frequency Noise, Vibration and Active Control 40(1).

Findings

Discussion and theoretical consideration

Conclusions