Data Compression of Structural Seismic Responses via Principled Independent Component Analysis

AbstractThis paper proposes a novel lossy data compression scheme for structural seismic responses based on principled (truncated) independent component analysis (PICA). It is first shown that independent component analysis (ICA) is able to transform a multivariate data set into a sparse representation space where is optimal for coding and compression, such that both the intradependencies and interdependencies (i.e., redundant information) between the multichannel data are removed for efficient data compression. Two examples are presented to demonstrate the compression performance of PICA, using the real-measured structural seismic responses from the 1994 Northridge earthquake, of the Fire Command Control (FCC) building and the USC hospital building, respectively. It is compared with the popular wavelet transform coding technique, which is only able to handle single-channel data separately. Results show that PICA achieves dramatically higher compression ratio (CR) than the wavelet method while retaining e...

[1]  Samuel D. Stearns,et al.  Lossless compression of waveform data for efficient storage and transmission , 1993, IEEE Trans. Geosci. Remote. Sens..

[2]  Yongchao Yang,et al.  Time-Frequency Blind Source Separation Using Independent Component Analysis for Output-Only Modal Identification of Highly Damped Structures , 2013 .

[3]  Satish Nagarajaiah,et al.  Base-Isolated FCC Building: Impact Response in Northridge Earthquake , 2001 .

[4]  Billie F. Spencer,et al.  Smart sensing technology: opportunities and challenges , 2004 .

[5]  Khalid Sayood,et al.  Introduction to Data Compression , 1996 .

[6]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[7]  Sean M. O'Connor,et al.  Implementation of a compressive sampling scheme for wireless sensors to achieve energy efficiency in a structural health monitoring system , 2013, Smart Structures.

[8]  Yi-Qing Ni,et al.  Theoretical and experimental modal analysis of the Guangzhou New TV Tower , 2011 .

[9]  Yunfeng Zhang,et al.  Linear Predictor-Based Lossless Compression of Vibration Sensor Data: Systems Approach , 2007 .

[10]  I. Johnstone,et al.  Ideal spatial adaptation by wavelet shrinkage , 1994 .

[11]  Bo Hu,et al.  Blind source separation towards decentralized modal identification using compressive sampling , 2012, 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA).

[12]  Satish Nagarajaiah,et al.  Response of Base-Isolated USC Hospital Building in Northridge Earthquake , 2000 .

[13]  Richard W. Longman,et al.  Identification of linear structural systems using earthquake‐induced vibration data , 1999 .

[14]  Jerome P. Lynch,et al.  A summary review of wireless sensors and sensor networks for structural health monitoring , 2006 .

[15]  Farzad Naeim,et al.  AUTOMATED POST-EARTHQUAKE DAMAGE ASSESSMENT OF INSTRUMENTED BUILDINGS , 2006 .

[16]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[17]  James L Beck,et al.  Compressive sampling for accelerometer signals in structural health monitoring , 2011 .

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

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

[20]  Stefan Hurlebaus,et al.  Special issue on real-world applications of structural identification and health monitoring methodologies , 2013 .

[21]  J. Antoni Blind separation of vibration components: Principles and demonstrations , 2005 .

[22]  Yongchao Yang,et al.  Blind identification of damage in time-varying systems using independent component analysis with wavelet transform , 2014 .

[23]  Khalid Sayood,et al.  Introduction to data compression (2nd ed.) , 2000 .

[24]  Yi-Qing Ni,et al.  Technology innovation in developing the structural health monitoring system for Guangzhou New TV Tower , 2009 .

[25]  Charles R. Farrar,et al.  Application of Compressed Sensing to 2-D Ultrasonic Propagation Imaging System data , 2012 .

[26]  Chin-Hsiung Loh,et al.  Stochastic subspace identification for output‐only modal analysis: application to super high‐rise tower under abnormal loading condition , 2013 .

[27]  M. Celebi,et al.  Real-Time Seismic Monitoring Needs of a Building Owner—and the Solution: A Cooperative Effort , 2004 .

[28]  Pierre Comon,et al.  Handbook of Blind Source Separation: Independent Component Analysis and Applications , 2010 .

[29]  Yongchao Yang,et al.  Blind modal identification of output‐only structures in time‐domain based on complexity pursuit , 2013 .

[30]  Yunfeng Zhang,et al.  Wavelet-based vibration sensor data compression technique for civil infrastructure condition monitoring , 2006 .

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

[32]  Yongchao Yang,et al.  Output-only modal identification with limited sensors using sparse component analysis , 2013 .

[33]  K. R. Rao,et al.  Orthogonal Transforms for Digital Signal Processing , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[34]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[35]  Glen G. Langdon,et al.  Arithmetic Coding , 1979 .

[36]  Andreas Spanias,et al.  Transform methods for seismic data compression , 1991, IEEE Trans. Geosci. Remote. Sens..

[37]  J. Cardoso,et al.  Blind beamforming for non-gaussian signals , 1993 .