Data Analysis Challenges in Structural Health Assessment using measured Dynamic responses

The authors and their research team developed several nondestructive inspection-based structural health assessment and monitoring procedures to detect defect at the local element level, representing structures by finite elements. They are based on time domain nonlinear system identification-based concept. By tracking changes in the identified system parameters of the elements, the location and severity of defect can be assessed. To increase their implementation potential, only limited numbers of very short durations measured noise-contaminated acceleration time-histories are used in the identification algorithms. Numerous multi-disciplinary advanced data processing schemes used in developing these algorithms are briefly discussed. Presence of uncertainties in data processing and mitigation strategies are emphasized.

[1]  Achintya Haldar,et al.  Element Level System Identification with Unknown Input with Rayleigh Damping , 2004 .

[2]  Roger Ghanem,et al.  Health monitoring for strongly non‐linear systems using the Ensemble Kalman filter , 2006 .

[3]  Keith Worden,et al.  An introduction to structural health monitoring , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[4]  D. Bernal.  Load Vectors for Damage Localization , 2002 .

[5]  Ruichong Zhang,et al.  Modal parameter identification of Tsing Ma suspension bridge under Typhoon Victor : EMD-HT method , 2004 .

[6]  Richard W. Longman,et al.  Comparison Of Several System Identification Methods For Flexible Structures , 1993 .

[7]  Yl L. Xu,et al.  CHARACTERIZING NONSTATIONARY WIND SPEED USING EMPIRICAL MODE DECOMPOSITION , 2004 .

[8]  Masoud Sanayei,et al.  STRUCTURAL MODEL UPDATING USING EXPERIMENTAL STATIC MEASUREMENTS , 1997 .

[9]  Pizhong Qiao,et al.  Vibration-based Damage Identification Methods: A Review and Comparative Study , 2011 .

[10]  Achintya Haldar,et al.  Health Assessment at Local Level with Unknown Input Excitation , 2005 .

[11]  Achintya Haldar,et al.  Element-level system identification with unknown input , 1994 .

[12]  Charles R. Farrar,et al.  Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review , 1996 .

[13]  Norden E. Huang,et al.  On Instantaneous Frequency , 2009, Adv. Data Sci. Adapt. Anal..

[14]  Yu Lei,et al.  Hilbert-Huang Based Approach for Structural Damage Detection , 2004 .

[15]  T. T. Soong,et al.  STRUCTURAL CONTROL: PAST, PRESENT, AND FUTURE , 1997 .

[16]  A. M. R. Ribeiro,et al.  A review of vibration-based structural health monitoring with special emphasis on composite materials , 2006 .

[17]  O. S. Salawu.  Detection of structural damage through changes in frequency: a review , 1997 .

[18]  Jun Chen.  Application of Empirical Mode Decomposition in Structural Health Monitoring: Some Experience , 2009, Adv. Data Sci. Adapt. Anal..

[19]  Norden E. Huang,et al.  HHT-BASED BRIDGE STRUCTURAL HEALTH-MONITORING METHOD , 2005 .

[20]  Subrata Chakraborty,et al.  SENSITIVITY BASED HEALTH MONITORING OF STRUCTURES WITH STATIC RESPONSE , 2008 .

[21]  E. Peter Carden,et al.  Vibration Based Condition Monitoring: A Review , 2004 .

[22]  Xianyao Chen,et al.  HHT-Based Structural Health Monitoring , 2013 .

[23]  P. Ibáñez,et al.  Identification of dynamic parameters of linear and non-linear structural models from experimental data , 1973 .

[24]  Achintya Haldar,et al.  A novel health assessment technique with minimum information , 2008 .

[25]  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.

[26]  Duan Wang,et al.  System Identification with Limited Observations and without Input , 1997 .