Abstract
Over the last decade, Blind Source Separation (BSS) has evolved as a powerful tool for system identification of flexible structures. Several numerical and experimental studies have been proposed to demonstrate its effectiveness in dealing with noisy full-scale data. The author has recently developed a suite of methods that enhance the capabilities of BSS in addressing issues associated with decentralized implementation, autonomous processing, nonstationary excitations, and the presence of narrowband excitations in ambient vibration measurements. The basic idea of the algorithms proposed by the author is to cast the problem of identification within the framework of underdetermined BSS invoking sparsifying transforms. The resulting partial mode shape coefficients are combined to yield complete modal information. The transformations are undertaken using Stationary Wavelet Packet Transform (SWPT), yielding a sparse representation in the wavelet domain. Both numerical and experimental studies demonstrate the potential of these methods. The speaker will introduce this suite of methods and some examples where these methods have successfully been applied.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Antoni J (2005) Blind separation of vibration components: principles and demonstrations. Mech Syst Signal Process 19(6):1166–1180
Poncelet F, Kerschen G, Golinval JC, Verhelst D (2007) Output-only modal analysis using blind source separation technique. Mech Syst Signal Process 21:2335–2358
Hazra B, Sadhu A, Lourenco R, Narasimhan S (2010) Retuning tuned mass dampers using ambient vibration response. Smart Mater Struct 19(11):115002
Hazra B, Sadhu A, Roffel AJ, Paquet PE, Narasimhan S (2012) Underdetermined blind identification of structure by using the modified cross-correlation method. J Eng Mech 138(4):327–337
Hazra B, Sadhu A, Roffel AJ, Narasimhan S (2012) Hybrid time-frequency blind source separation towards ambient system identification of structures. Comput Aided Civ Infrastruct Eng 27(5):314–332
Sadhu A (2013) Decentralized ambient modal identification of structures. Ph.D. Thesis, Department of Civil Engineering, University of Waterloo
Sadhu A, Hazra B, Narasimhan S (2011) Decentralized modal identification using wavelet transforms. In: Proceedings of engineering mechanics institute conference (ASCE/ASME), Boston, USA
Sadhu A, Hazra B, Narasimhan S, Pandey MD (2011) Decentralized modal identification using sparse blind source separation. Smart Mater Struct 20(12):125009
Sadhu A, Narasimhan S, Goldack A (2014) Decentralized modal identification of a pony truss pedestrian bridge using wireless sensors. J Bridg Eng 19(6):04014013
Sadhu A, Hazra B, Narasimhan S (2013) Decentralized modal identification of structures using parallel factor decomposition and sparse blind source separation. Mech Syst Signal Process 41(1–2):396–419
Sadhu A, Hazra B, Narasimhan S (2012) Blind identification of earthquake-excited structures. Smart Mater Struct 21(4):045019
Sadhu A, Hu B, Narasimhan S (2012) Blind source separation towards decentralized modal identification using compressive sampling. In: Proceedings of the 11th IEEE conference on information science, signal processing and their applications: special sessions, Montreal, Canada, pp. 1184–1189
Sadhu A, Narasimhan S (2012) Blind source separation of convolutive mixtures towards modal identification. In: Proceedings of the society for experimental mechanics series, vol 26, pp 209–220
Sadhu A, Goldack A, Narasimhan S (2014) Ambient modal identification using multi-rank parallel factor decomposition. Struct Control Health Monit. doi:10.1002/stc.1706
Belouchrani A, Abed-Meraim K, Cardoso J, Moulines E (1997) A blind source separation technique using second-order statistics. IEEE Trans Signal Process 45(2):434–444
Hyvarinen A (1999) Fast and robust fixed-point algorithm for independent component analysis. IEEE Trans Neural Netw 10(3):626–634
Antoni J, Garibaldi L, Marchesiello S, Sidhamed M (2004) New separation techniques for output-only modal analysis. Shock Vib 11(3-4): 227–242.
Peeters B, Cornelis B, Janssens K, Auweraer HV (2007) Removing disturbing harmonics in operational modal analysis. IOMAC, Copenhagen
Pintelon R, Peeters B, Guillaume P (2008) Continuous-time operational modal analysis in the presence of harmonic disturbances. Mech Syst Signal Process 22:1017–1035
Andersen P, Brincker R, Ventura C, Cantieni R (2008) Mode estimation of civil structures subject to ambient and harmonic excitation. In: Proceeding of the 26th international modal analysis conference, Orlando
Fujino Y, Pacheco BM, Nakamura SI, Warnitchai P (1993) Synchronization of human walking observed during lateral vibration of a congested pedestrian bridge. Earthq Eng Struct Dyn 22(9):741–758
Newland DE (2003) Vibration of the London millennium bridge: cause and cure. Int J Accoust Vib 8(1):9–14
Ingolfsson ET, Georgakis CT (2011) A stochastic load model for pedestrian-induced lateral forces on footbridges. Eng Struct 33(12): 3454–3470
Sadhu A, Narasimhan S (2014) A decentralized blind source separation algorithm for ambient modal identification in the presence of narrowband disturbances. Struct Control Health Monit 21(3):282–302
Rainieri C, Fabbrocino G, Manfredi G, Dolce M (2012) Robust output-only modal identification and monitoring of buildings in the presence of dynamic interactions for rapid post-earthquake emergency management. Eng Struct 34:436–446
Gao Y, Spencer Jr BF, Ruiz-Sandoval M (2006) Distributed computing strategy for structural health monitoring. Struct Control Health Monit 13:488–507
Lynch JP (2007) An overview of wireless structural health monitoring for civil structures. Philos Trans R Soc A 365:345–372
Zimmerman AT, Shiraishi M, Swartz RA, Lynch JP (2008) Automated modal parameter estimation by parallel processing within wireless monitoring systems. J Infrastruct Syst 22:102–113
Bocca M, Eriksson LM, Mahmood A, Jantti R, Kullaa J (2011) A synchronized wireless sensor network for experimental modal analysis in structural health monitoring. Comput-Aided Civ Infrastuct Eng 26:483–499
Sim SH, Carbonell-Marquez JF, Spencer BF, Jo H (2011) Decentralized random decrement technique for efficient data aggregation and system identification in wireless smart sensor networks. Probab Eng Mech 26(1):81–91
Yun GJ, Lee SG, Carletta J, Nagayama T (2011) Decentralized damage identification using wavelet signal analysis embedded on wireless smart sensors. Eng Struct 33(7):2162–2172
Sadhu A, Hazra B, Narasimhan S (2014) Ambient modal identification of structures equipped with tuned mass dampers using parallel factor blind source separation. Smart Struct Syst 13(2):257–280
Coifman RR, Wickerhauser MV (1992) Entropy-based algorithms for best basis selection. IEEE Trans Inf Theory 38(2):713–718
Pesquet JC, Krim H, Carfantan H (1996) Time-invariant orthonormal wavelet representations. IEEE Trans Signal Process 44(8):1964–1970
Wickerhauser M (1994) Adapted wavelet analysis from theory to software. AK Peters, Wellesley
Mallat SG (1998) A wavelet tour of signal processing. Academic Press, San Diego
Efron B, Tibshirani R (1993) An introduction to the bootstrap. Chapman and Hall, New York
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 The Society for Experimental Mechanics, Inc.
About this paper
Cite this paper
Sadhu, A. (2015). Blind Source Separation: A Generalized Modal Identification Tool for Civil Structures. In: Caicedo, J., Pakzad, S. (eds) Dynamics of Civil Structures, Volume 2. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-319-15248-6_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-15248-6_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-15247-9
Online ISBN: 978-3-319-15248-6
eBook Packages: EngineeringEngineering (R0)