Innovative method of empirical mode decomposition as spatial tool for structural damage identification

Reddy, D. Mallikarjuna; Krishna, I. R. Praveen; Sathesa,

doi:10.1002/stc.1676

Cited by 11 publications

(7 citation statements)

References 16 publications

(16 reference statements)

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“…However, HHT is being developed and recently applied in the field of SHM during last years. [34][35][36][37] The main feature of HHT is its capability to deal with non-linear and non-stationary vibrations. The HHT method involves two distinct steps: firstly, the decomposition of signals into IMF, where its components are obtained from the original signal through an empirical procedure.…”

Section: Hilbert-huang Transformmentioning

confidence: 99%

Bridge damage detection via improved completed ensemble empirical mode decomposition with adaptive noise and machine learning algorithms

Delgadillo

Casas

2022

Structural Contr & Hlth

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Structural health monitoring field is growing in the use of more modern techniques and tools in order to identify damages in civil structures. The improvements in signal processing techniques and data mining have, recently, been employed due to their powerful computational ability to detect damage in bridges. Despite the majority of researchers have been studying laboratory-scale implementations and theoretical developments, the limited data to identify structural faults in real bridges are still a problem. The current study presents a novel approach for damage identification by using two improved methods such as decomposition techniques and machine learning algorithms. Since the data obtained from the traffic vibration in real bridges are non-linear and time varying, the Hilbert-Huang transform is used to process the vibration data.

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Section: Hilbert-huang Transformmentioning

confidence: 99%

Bridge damage detection via improved completed ensemble empirical mode decomposition with adaptive noise and machine learning algorithms

Delgadillo

Casas

2022

Structural Contr & Hlth

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“…proposed by Huang (1998). Using this method, a complex signal can be decomposedinto a collection of intrinsic mode functions (IMFs) and a residue.Since the decomposition of the signal is mainly dependent on the information contained in the signal, the IMF number is always limited and its components gained via decomposition are steady (Reddy et al 2015;Rezaei and Taheri 2010;Yang and Tavner 2009). The IMFs generally satisfy two conditions: 1) in the entire signal sequence, the number of extreme points and crossing through zero points should be the same or the difference no more than 1; 2) at any time point, the averaged value of the envelop constructed by the local maximum and minimum points is 0.…”

Section: Wavelet Packettransform(wpt) Feature Extractionmentioning

confidence: 99%

Condition Assessment of Timber Utility Poles Based on a Hierarchical Data Fusion Model

Dackermann

Liu

et al. 2016

J. Comput. Civ. Eng.

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This paper proposes a novel hierarchical data fusion technique for the non-destructive testing (NDT) and condition assessment of timber utility poles. The new method analyses stress wave data from multi-sensor multi-excitation guided wave testing using a hierarchical data fusion model consisting of feature extraction, data compression, pattern recognition and decision fusion algorithms. The proposed technique is validated on guided waved at a of in-situ timber poles. The actual health states of these poles are known from autopsies conducted after the testing, forming a ground-truth for supervised classification. In the proposed method, a data fusion level extracts the main features from the sampled stress wave signals using power spectrum density (PSD) estimation, wavelet packet transform (WPT) and empirical mode decomposition (EMD). These features are then compiled to a feature vector via real-number encoding and sent to the next level for further processing. Principal component analysis (PCA) is also adopted for feature compression and to minimise information redundancy and noise interference. In the feature fusion level, two classifiers based on support vector machine (SVM) are applied to sensor separated data of the two excitation types and the pole condition is identified. In the decision making fusion level, the D-S evidence theory is employed to integrate the results from the individual sensors obtaining a final decision. The results of the in-situ timber pole testing show that the proposed hierarchical data fusion model was able to distinguish between healthy and faulty poles demonstrating the effectiveness of the new method.

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“…It is worth to mention that CWT and HHT have been widely used on the traverse phase response for damage detection and finding singularities, which do not exist in a healthy bridge and only appear in the response of the damaged structure. [19][20][21][22][23][24][25] However, those singularities, abrupt changes, or signal discontinuities typically appear in a higherfrequency range (i.e., well above 100 Hz), whereas a low frequency (i.e., below 10 Hz) is the target frequency range in this paper, since for the majority of the railways VBI systems, the fundamental frequency of the bridge as well as the vehicle frequencies are distributed in the target low-frequency range.…”

Section: Introductionmentioning

confidence: 99%

Extracting the time‐dependent resonances of a vehicle–bridge interacting system by wavelet synchrosqueezed transform

Mostafa

Maio

Loendersloot

et al. 2021

Struct Control Health Monit

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The dynamic response of a vehicle-bridge interaction (VBI) system is a noisy, nonstationary, and multicomponent response with closely spaced spectral components. Considering the ultimate goal as utilizing the VBI system instantaneous frequencies (IFs) for bridge condition monitoring, this paper carries out a systematic study to investigate the performance of four advanced timefrequency analysis techniques on a series of VBI responses: Hilbert-Huang transformation (HHT), continuous wavelet transform (CWT), robust local mean decomposition (robust-LMD), and wavelet synchrosqueezed transform (WSST). The novel contribution of this study is the application of the two latter techniques. The synthesized tests demonstrate that the WSST is the best method to precisely separate and localize in time the closely spaced resonances. Therefore, the WSST is applied to field measurements obtained from the Boyne viaduct in Ireland, as well. The results verify the capacity of this method to deal with time-varying vibrations adequately.

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Innovative method of empirical mode decomposition as spatial tool for structural damage identification

Cited by 11 publications

References 16 publications

Bridge damage detection via improved completed ensemble empirical mode decomposition with adaptive noise and machine learning algorithms

Bridge damage detection via improved completed ensemble empirical mode decomposition with adaptive noise and machine learning algorithms

Condition Assessment of Timber Utility Poles Based on a Hierarchical Data Fusion Model

Extracting the time‐dependent resonances of a vehicle–bridge interacting system by wavelet synchrosqueezed transform

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