A frequency response based structural damage localization method using independent component analysis

Vibration-based structural damage detection has been the focus of attention by many researchers over the last few decades. However, most methods proposed for this purpose utilize extracted modal parameters or some indices constructed on the basis these parameters. Our literature review revealed that few papers have employed frequency response functions (FRFs) for detecting structural damage. In this paper, a technique is presented for damage detection which is based on measured FRFs. Independent component analysis (ICA) has been implemented on the spatiotemporal responses in each frequency in order to reduce the dimension of the data. This is based on the concept that the forced harmonic response of a linear vibrating system can be fully captured utilizing a single ICA mode. A different approach is also presented in which ICA is applied to the frequency domain data. Operational deflection shapes (ODSs) have been decomposed using ICA to localize the damage. The efficiency of both methods is demonstrated through some numerical and experimental case studies.

[1]  Zhongyuan Su,et al.  Gear fault identification and classification of singular value decomposition based on Hilbert-Huang transform , 2011 .

[2]  Cecilia Surace,et al.  Structural Damage Detection Based on Proper Orthogonal Decomposition: Experimental Verification , 2008 .

[3]  Haiqi Zheng,et al.  Hilbert-Huang transform and marginal spectrum for detection and diagnosis of localized defects in roller bearings , 2009 .

[4]  R. Liu,et al.  AMUSE: a new blind identification algorithm , 1990, IEEE International Symposium on Circuits and Systems.

[5]  Robin Sibson,et al.  What is projection pursuit , 1987 .

[6]  John W. Sammon,et al.  A Nonlinear Mapping for Data Structure Analysis , 1969, IEEE Transactions on Computers.

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

[8]  E. Oja,et al.  Independent Component Analysis , 2013 .

[9]  Boca Raton,et al.  Clustering for Data Mining , 2005 .

[10]  Ricardo Perera,et al.  A multistage FE updating procedure for damage identification in large-scale structures based on multiobjective evolutionary optimization , 2008 .

[11]  Jeanny Hérault,et al.  Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets , 1997, IEEE Trans. Neural Networks.

[12]  Aapo Hyvärinen,et al.  Icasso: software for investigating the reliability of ICA estimates by clustering and visualization , 2003, 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718).

[13]  Nuno M. M. Maia,et al.  Localization of Damage Using Curvature of the Frequency-response-functions , 1997 .

[14]  Ugo Galvanetto,et al.  Numerical investigation of a new damage detection method based on proper orthogonal decomposition , 2007 .

[15]  Jean-Claude Golinval,et al.  Physical interpretation of independent component analysis in structural dynamics , 2007 .

[16]  Nicholas A J Lieven,et al.  Frequency response function shape-based methods for structural damage localisation , 2009 .

[17]  Nuno M. M. Maia,et al.  DAMAGE DETECTION USING THE FREQUENCY-RESPONSE-FUNCTION CURVATURE METHOD , 1999 .

[18]  Robert D. Adams,et al.  The location of defects in structures from measurements of natural frequencies , 1979 .

[19]  Jean-Franois Cardoso High-Order Contrasts for Independent Component Analysis , 1999, Neural Computation.

[20]  Jianhong Wu,et al.  Data clustering - theory, algorithms, and applications , 2007 .

[21]  Hoon Sohn,et al.  A review of structural health monitoring literature 1996-2001 , 2002 .

[22]  Wei-Xin Ren,et al.  Damage detection by finite element model updating using modal flexibility residual , 2006 .

[23]  David J. Ewins,et al.  Modal Testing: Theory, Practice, And Application , 2000 .

[24]  Jin Chen,et al.  Spectral kurtosis based on AR model for fault diagnosis and condition monitoring of rolling bearing , 2012 .

[25]  Eric Moulines,et al.  A blind source separation technique using second-order statistics , 1997, IEEE Trans. Signal Process..

[26]  Michael Kirby,et al.  Geometric Data Analysis: An Empirical Approach to Dimensionality Reduction and the Study of Patterns , 2000 .

[27]  Michael I. Friswell,et al.  Structural Damage Detection using Independent Component Analysis , 2004 .

[28]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

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

[30]  Qiuhai Lu,et al.  MULTIPLE DAMAGE LOCATION WITH FLEXIBILITY CURVATURE AND RELATIVE FREQUENCY CHANGE FOR BEAM STRUCTURES , 2002 .

[31]  Soo-Young Lee Blind Source Separation and Independent Component Analysis: A Review , 2005 .