Structural Damage Detection in a Truss Bridge Model Using Fuzzy Clustering and Measured FRF Data Reduced by Principal Component Projection

Yu, Lie; Zhu, Junhua; Yu, Lili

doi:10.1260/1369-4332.16.1.207

Cited by 57 publications

(24 citation statements)

References 25 publications

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“…Applications involving aging and wearing out of components, processes dealing with seismic signals, are generally time-varying where the signals evolve in real time and the process becomes too complex to be analyzed via a simple offline method (Misra et al 2002;Yu et al 2013;Gharibnezhad et al 2015). In practical SHM scenarios, data streams continuously in real time, which further necessitates that the algorithm should be amenable towards online implementation, independent of any baseline (reference) data.…”

Section: Real Time Single and Multi Channel Structural Damage Detectimentioning

confidence: 99%

Real-time unified single- and multi-channel structural damage detection using recursive singular spectrum analysis

Bhowmik

Krishnan

Hazra

et al. 2018

Structural Health Monitoring

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A novel baseline free approach for continuous online damage detection of multi degree of freedom vibrating structures using Recursive Singular Spectral Analysis (RSSA) in conjunction with Time Varying Auto-Regressive Modeling (TVAR) is proposed in this paper. The acceleration data is used to obtain recursive proper orthogonal components online using rank-one perturbation method, followed by TVAR modeling of the first transformed response, to detect the change in the dynamic behavior of the vibrating system from its original state to contiguous linear /non-linear-states indicating damage. Most work to date deal with algorithms that require windowing of the gathered data that render them ineffective for online implementation. Algorithms focussed on mathematically consistent recursive techniques in a rigorous theoretical framework of structural damage detection is missing, that motivates the development of the present framework. The response from a single channel is provided as input to the algorithm in real time. The RSSA algorithm iterates the eigenvector and eigenvalue estimates for sample covariance matrices and new data point at each successive time instants. This eliminates the need for offline post processing and facilitates online damage detection especially when applied to streaming data without requiring any baseline data. Lower order TVAR models are applied on the transformed responses to improve detectability. Numerical simulations performed on a 5-dof nonlinear system and on an SDOF system modeled using a Duffing oscillator under white noise excitation data, with different levels of nonlinearity simulating the damage scenarios, demonstrate the robustness of the proposed algorithm. The method, further validated on results obtained from experiments performed on a cantilever beam subjected to earthquake excitation; a toy cart experiment model with springs attached to either side; demonstrate the efficacy of the proposed methodology as an appropriate candidate for real time, reference free structural health monitoring.

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Section: Real Time Single and Multi Channel Structural Damage Detectimentioning

confidence: 99%

Real-time unified single- and multi-channel structural damage detection using recursive singular spectrum analysis

Bhowmik

Krishnan

Hazra

et al. 2018

Structural Health Monitoring

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“…where, ( ) = ith membership function of jth feature, = absolute value, = related mean value (midpoint) and = variance of the jth feature in ith class. In [89], the authors suggested a noteworthy methodology on the basis of fuzzy clustering combined with computed frequency response function samples minimized via principal component projection in order to investigate damage detection in truss bridge model using vibration based technique. The preliminary data utilized in this investigation are the FRFs associated with the healthy as well as the damaged structure.…”

Section: Fuzzy Logicmentioning

confidence: 99%

Recent Advances in Intelligent-Based Structural Health Monitoring of Civil Structures

Paul¹,

Jafari²

2018

Adv. sci. technol. eng. syst. j.

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This survey paper deals with the structural health monitoring systems on the basis of methodologies involving intelligent techniques. The intelligent techniques are the most popular tools for damage identification in terms of high accuracy, reliable nature and the involvement of low cost. In this critical survey, a thorough analysis of various intelligent techniques is carried out considering the cases involved in civil structures. The importance and utilization of various intelligent tools to be mention as the concept of fuzzy logic, the technique of genetic algorithm, the methodology of neural network techniques, as well as the approaches of hybrid methods for the monitoring of the structural health of civil structures are illustrated in a sequential manner.

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“…The vibration-based SDD technique has become an effective way in SDD [1,2]. Normally, SDD can be achieved by comparing the structural characteristics extracted from structural reference state and damage state respectively.…”

Section: Introductionmentioning

confidence: 99%

New damage-sensitive feature for structures with bolted joints

Luo

2017

J. Phys.: Conf. Ser.

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Abstract.Compared with riveted and welded joints, bolted joints have advantages of easily dismantled, high load carrying and load-transferring capacity. However, bolted joints are also weaker components of assembled structures. Structural damage detection (SDD) on bolted joints is much required in the field of structural health monitoring (SHM). A new SDD method is proposed for damage identification of structures with bolted joints based on residual error of AR model in time series analysis. Firstly, a new data standardization process is defined to maintain the information of damage location. Then, a new structural damage feature sensitive to structural damage is developed based on the standard deviation of AR model residual errors. To verify the proposed method, a bolted joint structure is designed and fabricated in laboratory, connection damages of structures are simulated by loosening the bolted joints. The acceleration responses of structures with bolted joints under healthy and damage cases are acquired. Finally, the SDD is performed by traditional DSF and the new DSF. The illustrated results show that the proposed method is a hybrid tool for the bolted joint damage detection with the new damage-sensitive feature. In addition, some related issues will be discussed as well. IntroductionThe vibration-based SDD technique has become an effective way in SDD [1,2]. Normally, SDD can be achieved by comparing the structural characteristics extracted from structural reference state and damage state respectively. Zhou et al [3] used the hierarchical clustering analysis and similarity measure to distinguish structural damage state from health state. Most of these methods can be divided into two categories: model based and feature based. The model based methods need to construct the structural dynamic model. Khatir et al [4] used the co-ordinate modal assurance criterion for SDD. Khatir et al [5] used the modal scale factor and natural frequencies for structural damage detection and localization. To the feature based methods, especially for those based on time series analysis, are found to be less complicated and more sensitive to local damage [6].Most of time series analysis based methods need to construct a time series model that fit for vibration response data and achieves SDD via comparing the extracted features from structural reference (healthy) and damage states. Chen and Yu

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Structural Damage Detection in a Truss Bridge Model Using Fuzzy Clustering and Measured FRF Data Reduced by Principal Component Projection

Cited by 57 publications

References 25 publications

Real-time unified single- and multi-channel structural damage detection using recursive singular spectrum analysis

Real-time unified single- and multi-channel structural damage detection using recursive singular spectrum analysis

Recent Advances in Intelligent-Based Structural Health Monitoring of Civil Structures

New damage-sensitive feature for structures with bolted joints

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