Output-only damage localization technique using time series model

AbstractIn this paper, we present a technique to detect the time instant and location of damage in civil structures using scalar time series models, by handling operational variability and measurement noise. The scalar Autoregressive (AR) and Autoregressive with exogenous inputs (ARX) models are used to obtain the time instant of damage and its spatial location. The spatial damage feature to locate the damage is obtained using a metric constructed from the probability density values of the prediction errors of AR–ARX model. The proposed method does not resort to any computationally expensive vector time series models to locate the damage and so highly preferable in smart wireless online continuous SHM schemes. Numerical simulation studies are carried out by using a simply supported beam model. The results of the studies indicate that the proposed technique is capable of identifying both the time instant and location of damage accurately using the proposed PDF based damage index. In order to validate the proposed technique with experimental results, the time-history data from the three-story bookshelf benchmark structure of EI-LANL is used. Finally, the laboratory experimental studies carried out on an RCC simply supported beam with inflicted damage are also presented. The experimental studies clearly indicate the effectiveness of the proposed damage index to detect the location of damage, by handling operational variability and measurement noise.

[1]  Guido De Roeck,et al.  One-year monitoring of the Z24-Bridge : environmental effects versus damage events , 2001 .

[2]  Vicente Lopes Junior,et al.  Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition , 2007 .

[3]  A. Rama Mohan Rao,et al.  Optimal sensor placement techniques for system identification and health monitoring of civil structures , 2008 .

[4]  K. Law,et al.  Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure , 2006 .

[5]  K. Lakshmi,et al.  A robust damage-detection technique with environmental variability combining time-series models with principal components , 2014 .

[6]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[7]  Arun Kumar Pandey,et al.  Damage detection from changes in curvature mode shapes , 1991 .

[8]  Alivia Plankis Structural health monitoring MEMS sensors using elasticity-based beam vibrations , 2012 .

[9]  Sudhakar M. Pandit,et al.  Statistical moments of autoregressive model residuals for damage localisation , 2006 .

[10]  Ling Yu,et al.  Nonlinear damage detection using higher statistical moments of structural responses , 2015 .

[11]  D. Dickey,et al.  Identifying damage locations under ambient vibrations utilizing vector autoregressive models and Mahalanobis distances , 2012 .

[12]  Ahsan Kareem,et al.  Wavelet Transforms for System Identification in Civil Engineering , 2003 .

[13]  Akira Mita,et al.  An improved substructural damage detection approach of shear structure based on ARMAX model residual , 2016 .

[14]  Biao Huang,et al.  System Identification , 2000, Control Theory for Physicists.

[15]  Zengrong Wang,et al.  Structural damage detection using autoregressive-model-incorporating multivariate exponentially weighted moving average control chart , 2009 .

[16]  Qi-Lin Zhang Statistical damage identification for bridges using ambient vibration data , 2007 .

[17]  R. Rao Damage identification technique based on time series models for LANL and ASCE benchmark structures , 2015 .

[18]  Glauco Feltrin,et al.  Damage Identification Using Modal Data: Experiences on a Prestressed Concrete Bridge , 2005 .

[19]  Charles R. Farrar,et al.  Comparative study of damage identification algorithms applied to a bridge: I. Experiment , 1998 .

[20]  Mustafa Gul,et al.  Structural health monitoring and damage assessment using a novel time series analysis methodology with sensor clustering , 2011 .

[21]  Chris Chatfield,et al.  Introduction to Statistical Time Series. , 1976 .

[22]  Maria Pina Limongelli,et al.  Frequency response function interpolation for damage detection under changing environment , 2010 .

[23]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[24]  Michael J. Brennan,et al.  Structural damage detection by fuzzy clustering , 2008 .

[25]  Daniel C. Kammer Sensor set expansion for modal vibration testing , 2005 .

[26]  M. H. Aliabadi,et al.  Optimal Sensor Placement for Structural, Damage andImpact Identification: A Review , 2013 .

[27]  M. H. Aliabadi,et al.  Optimal Sensor Placement for Structural, Damage and Impact Identification: A Review , 2014 .

[28]  K Lakshmi,et al.  Structural damage detection using ARMAX time series models and cepstral distances , 2016 .

[29]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[30]  Piotr Omenzetter,et al.  Application of time series analysis for bridge monitoring , 2006 .

[31]  J. Prawin,et al.  Nonlinear parametric identification strategy combining reverse path and hybrid dynamic quantum particle swarm optimization , 2016 .

[32]  Shien-Ming Wu,et al.  Time series and system analysis with applications , 1983 .

[33]  Spilios D Fassois,et al.  Time-series methods for fault detection and identification in vibrating structures , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[34]  Walter M. West,et al.  Illustration of the use of modal assurance criterion to detect structural changes in an Orbiter test specimen , 1986 .

[35]  A. Rotem,et al.  Determination of Reinforcement Unbonding of Composites by a Vibration Technique , 1969 .

[36]  Ahsan Kareem,et al.  Time-Frequency Analysis of Nonstationary Process Based on Multivariate Empirical Mode Decomposition , 2016 .

[37]  Akira Mita,et al.  Damage indicator defined as the distance between ARMA models for structural health monitoring , 2008 .

[38]  John E. Mottershead,et al.  Model Updating In Structural Dynamics: A Survey , 1993 .

[39]  James M. W. Brownjohn,et al.  ARMA modelled time-series classification for structural health monitoring of civil infrastructure , 2008 .

[40]  Akira Mita,et al.  A substructure approach to local damage detection of shear structure , 2012 .

[41]  Chin-Hsiung Loh,et al.  Nonlinear Identification of Dynamic Systems Using Neural Networks , 2001 .

[42]  Hoon Sohn,et al.  Damage diagnosis using time series analysis of vibration signals , 2001 .

[43]  Jiangpeng Shu,et al.  The application of a damage detection method using Artificial Neural Network and train-induced vibrations on a simplified railway bridge model , 2013 .

[44]  Peter M. A. Sloot,et al.  Time-Frequency Methods for Structural Health Monitoring , 2014, Sensors.

[45]  Mario Carpentieri,et al.  A framework for the damage evaluation of acoustic emission signals through Hilbert-Huang transform , 2016 .

[46]  Piotr Omenzetter,et al.  Damage classification and estimation in experimental structures using time series analysis and pattern recognition , 2010 .

[47]  K. Lakshmi,et al.  Damage diagnostic technique combining POD with time-frequency analysis and dynamic quantum PSO , 2015 .