• Title/Summary/Keyword: vibration-based damage detection

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A statistical framework with stiffness proportional damage sensitive features for structural health monitoring

  • Balsamo, Luciana;Mukhopadhyay, Suparno;Betti, Raimondo
    • Smart Structures and Systems
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    • v.15 no.3
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    • pp.699-715
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    • 2015
  • A modal parameter based damage sensitive feature (DSF) is defined to mimic the relative change in any diagonal element of the stiffness matrix of a model of a structure. The damage assessment is performed in a statistical pattern recognition framework using empirical complementary cumulative distribution functions (ECCDFs) of the DSFs extracted from measured operational vibration response data. Methods are discussed to perform probabilistic structural health assessment with respect to the following questions: (a) "Is there a change in the current state of the structure compared to the baseline state?", (b) "Does the change indicate a localized stiffness reduction or increase?", with the latter representing a situation of retrofitting operations, and (c) "What is the severity of the change in a probabilistic sense?". To identify a range of normal structural variations due to environmental and operational conditions, lower and upper bound ECCDFs are used to define the baseline structural state. Such an approach attempts to decouple "non-damage" related variations from damage induced changes, and account for the unknown environmental/operational conditions of the current state. The damage assessment procedure is discussed using numerical simulations of ambient vibration testing of a bridge deck system, as well as shake table experimental data from a 4-story steel frame.

A vibration-based approach for detecting arch dam damage using RBF neural networks and Jaya algorithms

  • Ali Zar;Zahoor Hussain;Muhammad Akbar;Bassam A. Tayeh;Zhibin Lin
    • Smart Structures and Systems
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    • v.32 no.5
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    • pp.319-338
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    • 2023
  • The study presents a new hybrid data-driven method by combining radial basis functions neural networks (RBF-NN) with the Jaya algorithm (JA) to provide effective structural health monitoring of arch dams. The novelty of this approach lies in that only one user-defined parameter is required and thus can increase its effectiveness and efficiency, as compared to other machine learning techniques that often require processing a large amount of training and testing model parameters and hyper-parameters, with high time-consuming. This approach seeks rapid damage detection in arch dams under dynamic conditions, to prevent potential disasters, by utilizing the RBF-NNN to seamlessly integrate the dynamic elastic modulus (DEM) and modal parameters (such as natural frequency and mode shape) as damage indicators. To determine the dynamic characteristics of the arch dam, the JA sequentially optimizes an objective function rooted in vibration-based data sets. Two case studies of hyperbolic concrete arch dams were carefully designed using finite element simulation to demonstrate the effectiveness of the RBF-NN model, in conjunction with the Jaya algorithm. The testing results demonstrated that the proposed methods could exhibit significant computational time-savings, while effectively detecting damage in arch dam structures with complex nonlinearities. Furthermore, despite training data contaminated with a high level of noise, the RBF-NN and JA fusion remained the robustness, with high accuracy.

Damage Detection Using Finite Element Model Updating (유한요소 모델 개선기법을 이용한 손상추정)

  • Min, Cheon-Hong;Choi, Jong-Su;Hong, Sup;Kim, Hyung-Woo;Yeu, Tae-Kyeong
    • Journal of Ocean Engineering and Technology
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    • v.26 no.5
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    • pp.11-17
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    • 2012
  • In this study, a damage detection method that uses sensitivity-based finite (FE) element model updating with the natural frequency and zero frequency was proposed. The stiffness matrix for a structure was modified using the sensitivity-based FE model updating method. A sensitivity analysis was used to update the FE model, and the natural frequencies and zero frequencies were considered as target parameters to supplement the information on the vibration characteristics. The locations and values of the damages were estimated from the modified stiffness matrix. Several numerical examples were considered to verify the performance of the proposed method.

Improvedd Vibration-Based Damage Identification Method (개선된 진동기초 손상검색방법의 유도와 검증)

  • 김정태;노리스스텁스
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.12 no.3
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    • pp.331-343
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    • 1999
  • 본 논문에서는 새로이 유도된 진동기초 손상검색방법을 제시하고, 제한적인 모드특성치가 측정된 구조물을 대상으로 이 알고리즘의 적합성과 손상예측의 정확도를 검증하고자 하였다. 먼저, 기존의 Kim과 Stubbs에 의해 발표된 손상발견 알고리즘들을 검토하였으며, 이 알고리즘들의 적용한계와 오류적 가정을 극복할 수 있는 손상검색 알고리즘을 새로이 유도하였다. 다음으로, 손상발생 전후에 소수의 진동모드 특성치가 측정된 2경간 연속보를 대상으로 손상예측실험을 수행하여, 이들 손상검색 알고리즘의 손상예측 정확도를 분석하였다. 기존의 손상검색 알고리즘에 비하여 새로이 유도된 손상검색 알고리즘의 손상예측 정확도가 향상된 것으로 분석되었다.

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Damage and Failure Detection of CFRP Using Optical Fiber Vibration Sensor (광섬유 진동센서를 이용한 탄소섬유강화 복합재료의 손상 및 파손검출)

  • 양유창;한경섭
    • Proceedings of the Korean Society For Composite Materials Conference
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    • 2001.05a
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    • pp.254-257
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    • 2001
  • An intensity-based optical fiber vibration sensor is applied to detect and evaluate damages and fiber failure of composites. The optical fiber vibration sensor is constructed by placing two cleaved fiber end, one of which is cantilevered in a hollow glass tube. The movement of the cantilevered section lags behind the rest of the sensor in response to an applied vibration and the amount of light coupled between the two fibers is thereby modulated. Vibration characteristics of the optical fiber vibration sensor are investigated. Surface mounted optical fiber vibration sensor is used in tensile and indentation test. Experimental results show that the optical fiber sensor can detect damages and fiber failure of composites correctly.

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Sensor clustering technique for practical structural monitoring and maintenance

  • Celik, Ozan;Terrell, Thomas;Gul, Mustafa;Catbas, F. Necati
    • Structural Monitoring and Maintenance
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    • v.5 no.2
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    • pp.273-295
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    • 2018
  • In this study, an investigation of a damage detection methodology for global condition assessment is presented. A particular emphasis is put on the utilization of wireless sensors for more practical, less time consuming, less expensive and safer monitoring and eventually maintenance purposes. Wireless sensors are deployed with a sensor roving technique to maintain a dense sensor field yet requiring fewer sensors. The time series analysis method called ARX models (Auto-Regressive models with eXogeneous input) for different sensor clusters is implemented for the exploration of artificially induced damage and their locations. The performance of the technique is verified by making use of the data sets acquired from a 4-span bridge-type steel structure in a controlled laboratory environment. In that, the free response vibration data of the structure for a specific sensor cluster is measured by both wired and wireless sensors and the acceleration output of each sensor is used as an input to ARX model to estimate the response of the reference channel of that cluster. Using both data types, the ARX based time series analysis method is shown to be effective for damage detection and localization along with the interpretations and conclusions.

Application of smart piezoelectric transducers to structural health monitoring (구조물 건전성 감시를 위한 스마트 PZT센서의 적용성 연구)

  • Park, Seung-Hee;Yi, Jin-Hak;Lee, Jong-Jae;Yun, Chung-Bang;Noh, Yong-Rae
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 2003.10a
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    • pp.549-555
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    • 2003
  • The objective of かis study is to investigate the feasibility of piezoelectric transducers as a damage detection system for civil infrastructures. There have been considerable amount of efforts by the modal analysis community to localize damage and evaluate its severity without looking at a reliable way to excite the structure. The detection of damages by modal analysis and similar vibration techniques depends upon the knowledge and estimation of various modal parameters. In addition to the associated difficulties, such low-frequency dynamic response based techniques fail to detect incipient damages. Smart piezoelectric ceramic (PZT) transducers which act as both actuators and sensors in a self-analyzing manner are emerging to be effective in non-parametric health monitoring of structural systems. In this paper, we present the results of an experimental study for the detection of damages using smart PZT transducers on the steel plate. The method of extracting the impedance characteristics of the PZT transducer, which is electro-mechanically coupled to the host structure, is adopted for damage detection. Two damages are simulated and assessed by the bonded PZT transducers for characterization. The experimental results verified the efficacy of the proposed approach and provided a demonstration of good robustness at the realistic steel structures, emphasizing the great potential for developing an automated in situ structural health monitoring system for application to large civil infrastructures without the need to blow the modal parameters.

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Damage and Failure Detection of Composites Using Optical Fiber Vibration Sensor (광섬유 진동센서를 이용한 복합재료의 손상 및 파손검출)

  • Yang, Y.C.;Han, K.S.
    • Proceedings of the KSME Conference
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    • 2001.06a
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    • pp.547-552
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    • 2001
  • An intensity-based optical fiber vibration sensor is applied to detect and evaluate damages and fiber failure of composites. The optical fiber vibration sensor is constructed by placing two cleaved fiber end, one of which is cantilevered in a hollow glass tube. The movement of the cantilevered section lags behind the rest of the sensor in response to an applied vibration and the amount of light coupled between the two fibers is thereby modulated. Vibration characteristics of the optical fiber vibration sensor are investigated. Surface mounted optical fiber vibration sensor is used in tensile and indentation test. Experimental results show that the optical fiber sensor can detect damages and fiber failure of composites correctly.

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Damage detection of a cable-stayed bridge based on the variation of stay cable forces eliminating environmental temperature effects

  • Chen, Chien-Chou;Wu, Wen-Hwa;Liu, Chun-Yan;Lai, Gwolong
    • Smart Structures and Systems
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    • v.17 no.6
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    • pp.859-880
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    • 2016
  • This study aims to establish an effective methodology for the detection of instant damages occurred in cable-stayed bridges with the measurements of cable vibration and structural temperatures. A transfer coefficient for the daily temperature variation and another for the long-term temperature variation are firstly determined to eliminate the environmental temperature effects from the cable force variation. Several thresholds corresponding to different levels of exceedance probability are then obtained to decide four upper criteria and four lower criteria for damage detection. With these criteria, the monitoring data for three stay cables of Ai-Lan Bridge are analyzed and compared to verify the proposed damage detection methodology. The simulated results to consider various damage scenarios unambiguously indicate that the damages with cable force changes larger than ${\pm}1%$ can be confidently detected. As for the required time to detect damage, it is found that the cases with ${\pm}2%$ of cable force change can be discovered in no more than 6 hours and those with ${\pm}1.5%$ of cable force change can be identified in at most 9 hours. This methodology is also investigated for more lightly monitored cases where only the air temperature measurement is available. Under such circumstances, the damages with cable force changes larger than ${\pm}1.5%$ can be detected within 12 hours. Even though not exhaustively reflecting the environmental temperature effects on the cable force variation, both the effective temperature and the air temperature can be considered as valid indices to eliminate these effects at high and low monitoring costs.

Bearing fault detection through multiscale wavelet scalogram-based SPC

  • Jung, Uk;Koh, Bong-Hwan
    • Smart Structures and Systems
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    • v.14 no.3
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    • pp.377-395
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    • 2014
  • Vibration-based fault detection and condition monitoring of rotating machinery, using statistical process control (SPC) combined with statistical pattern recognition methodology, has been widely investigated by many researchers. In particular, the discrete wavelet transform (DWT) is considered as a powerful tool for feature extraction in detecting fault on rotating machinery. Although DWT significantly reduces the dimensionality of the data, the number of retained wavelet features can still be significantly large. Then, the use of standard multivariate SPC techniques is not advised, because the sample covariance matrix is likely to be singular, so that the common multivariate statistics cannot be calculated. Even though many feature-based SPC methods have been introduced to tackle this deficiency, most methods require a parametric distributional assumption that restricts their feasibility to specific problems of process control, and thus limit their application. This study proposes a nonparametric multivariate control chart method, based on multiscale wavelet scalogram (MWS) features, that overcomes the limitation posed by the parametric assumption in existing SPC methods. The presented approach takes advantage of multi-resolution analysis using DWT, and obtains MWS features with significantly low dimensionality. We calculate Hotelling's $T^2$-type monitoring statistic using MWS, which has enough damage-discrimination ability. A bootstrap approach is used to determine the upper control limit of the monitoring statistic, without any distributional assumption. Numerical simulations demonstrate the performance of the proposed control charting method, under various damage-level scenarios for a bearing system.