Experimental system identification of the dynamics of a vibro-impact beam with a view towards structural health monitoring and damage detection

Chen, Heng; Kurt, Mehmet; Lee, Young S.; McFarland, D. Michael; Bergman, Lawrence A.; Vakakis, Alexander F.

doi:10.1016/j.ymssp.2013.12.014

Cited by 57 publications

(26 citation statements)

References 43 publications

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“…Therefore, EMD provides a multi-scale decomposition of a measured time signal into embedded oscillatory modes at different time scales of the dynamics. 32 Recently, the physics-based foundation for EMD has been provided by Lee et al 28 It has been shown that performing slow/fast partition of the extracted IMFs results in a set of slowly varying amplitude that are nearly identical to the response of the underlying analytical slow-flow mode. Therefore, by utilizing EMD, a reduced-order empirical slow-flow model of the dynamics can be obtained.…”

Section: Emd-based Nsi Methodsmentioning

confidence: 99%

“…This EMD-based NSI has been used to study dynamics of some typical nonlinear systems. 29,30 Especially, utilizing the method to identify nonlinear dynamics of a vibro-impact beam has been carried out numerically in Kurt et al 31 and experimentally in Chen et al 32 Very recently, Sadeghi and Lotfan 33 used this EMD-based NSI method to identify nonlinear parameters of a cantilever beam supported with a cubic stiffness spring at the free end.…”

Section: Introductionmentioning

confidence: 99%

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Bolt loosening detection in a jointed beam using empirical mode decomposition–based nonlinear system identification method

Huang

Zhu

2019

International Journal of Distributed Sensor Networks

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In this work, a state-of-art nonlinear system identification method based on empirical mode decomposition is utilized and extended to detect bolt loosening in a jointed beam. This nonlinear system identification method is based on identifying the multi-scale dynamics of the underlying system. Only structural dynamic response signals are needed to construct a reduced-order model to represent the system concerned. It makes the method easy to use in practice. A new bolt loosening identification procedure based on the constructed system nonlinear reduced-order model is proposed. A new damage feature to indicate bolt loosening is presented. Experimental works are carried out to validate the proposed method. The results show that the proposed damage detection method can detect bolt loosening effectively, and the proposed damage feature values increase with the increase of bolt torques. The damage feature calculated from the response solution of the reduced-order model can give robust and sensitive indication of bolt loosening.

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Section: Emd-based Nsi Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bolt loosening detection in a jointed beam using empirical mode decomposition–based nonlinear system identification method

Huang

Zhu

2019

International Journal of Distributed Sensor Networks

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“…[26][27][28]. Furthermore, several aerospace and civil structures such as rotor blades and bridges could be modelled as cantilever beams.…”

Section: Experimental Setup With a Cantilever Beammentioning

confidence: 99%

Multi-Sensor Feature Extraction and Data Fusion Using ANFIS and 2D Wavelet Transform in Structural Health Monitoring

Escamilla-Ambrosio¹,

Li²,

ManuelRamírez-Cortés³

et al. 2017

Structural Health Monitoring - Measurement Methods and Practical Applications

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In this chapter, a novel feature extraction and data fusion approach for structural damage detection and localisation is presented. This approach combines adaptive network-based fuzzy inference systems (ANFIS) and two-dimensional wavelet transform (2D-WT) technologies. Simultaneous multi-sensor feature extraction and data fusion based on 2D-WT is carried out by forming a 2D multivariate signal, which is used to analyse the structure vibration response by measuring all sensors jointly. Energy values obtained from two-level db3 wavelet decomposition are arranged in a so-called energy percentage matrix (EPM), which is taken as an input for the ANFIS. The system is further trained by defining its output as the structural condition represented by a condition index. A set of output index patterns are defined depending on the level of damage assessment performed. The proposed method was tested through experiments using a cantilever beam structure. The testing results showed that the method is successful in detecting and localising damage by vibration analysis in structural health monitoring.

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“…They are particularly useful in non-stationary and/or non-linear cases. The applicability of empirical mode decomposition for SHM in a cantilever steel beam under impulse excitation for non-linear system identification is studied by Chen et al (2014), where it is shown that distance measures between instantaneous mode shapes allow detecting and localizing vibro-impact damage. While using wavelet transformation for vibrationbased SHM, wavelet energies and entropies are often used to define DSFs (Nair and Kiremidjian, 2012;Lee et al, 2014), but wavelet transformation coefficients and scales need to be selected in advance.…”

Section: Introductionmentioning

confidence: 99%

Improved damage detectability in a wind turbine blade by optimal selection of vibration signal correlation coefficients

Hoell

Omenzetter

2016

Structural Health Monitoring

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The central message of this paper is that for robust and efficient damage detection the damage sensitive features (DSFs) should be selected optimally in a systematic way such that only these features that contribute the most to damage detectability be retained. Furthermore, suitable transformations of the original features may also enhance damage detectability. We explore these principles using data from a wind turbine blade. Several damage extent scenarios are introduced non-destructively. Partial autocorrelation coefficients (PACCs) are proposed as vibrationbased DSFs. Scores calculated with principal component analysis (PCA) of PACCs are the transformed DSFs. Statistical distances between the DSF subsets estimated from the healthy and a reference damage state are calculated with respect to a statistical threshold as a measure of optimality. The fast forward (FF) method and a genetic algorithm are used to optimize the detectability of damage by DSF selection. The comparison between the two methods points out that FF offers a comparable performance at a lower computational cost. The classifiers based on the optimal features are tested on data from several previously unseen healthy and damaged cases and across a range of statistical detection thresholds. It is demonstrated that the selected PCA scores of the PACCs are superior compared to the initial features and allow identifying small damage confidently.

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Experimental system identification of the dynamics of a vibro-impact beam with a view towards structural health monitoring and damage detection

Cited by 57 publications

References 43 publications

Bolt loosening detection in a jointed beam using empirical mode decomposition–based nonlinear system identification method

Bolt loosening detection in a jointed beam using empirical mode decomposition–based nonlinear system identification method

Multi-Sensor Feature Extraction and Data Fusion Using ANFIS and 2D Wavelet Transform in Structural Health Monitoring

Improved damage detectability in a wind turbine blade by optimal selection of vibration signal correlation coefficients

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