Journal of Structural and Construction Engineering

Journal of Structural and Construction Engineering

Effect of noise on output-only structural identification of beams

Document Type : Original Article

Authors
1 Structural Engineering, Faculty of Civil Engineering, Babol Noshirvani University of Technology, Babol, Iran
2 Faculty of Civil Engineering, Babol Noshirvani University of Technology, Babol, Iran
3 Associate professor, Civil Engineering Department, Babol Noshirvani University of Technology
4 Department of Civil Engineering, Payame Noor University (PNU), Tehran, Iran
Abstract
Output-only structural identification is conducted by output data of the structure. These data usually include structural response together with some noise. Success of output-only methods in determining the vibration parameters of a structure depends on the signal to noise ratio (SNR) of the output data. In this paper, the vibration parameters (Natural frequency and Mode shape) of a contilever beam have been obtained using output data which have different signal to noise ratios. The vibration parameters of the beam were determined using modal analysis of finite element model and considered as reference parameters. Then, appropriate input was applied to the beam and the acceleration signal was obtained. To generate noisy data, noise with different powers compared to signal powers were added to acceleration signal. The modal parameters of the beam were obtained using two output-only methods, Peak Picking (PP) and Stochastic Subspace Identification (SSI). The vibration parameters having signal-to-noise ratios greater than 25 (lower noise level) for all considered modes were identified properly. At a signal-to-noise ratio of 0.25 to 25 (higher noise level), it was not possible to identify the modal parameters of the first mode of the beam, but the parameters of the higher modes were identified with good accuracy.
Keywords

Subjects


[1]. Sirca, Jr. and Adeli, H. (2012). System identification in structural engineering. Scientia Iranica, 19 (6), 1355-1364. 
[2]. Mbarek, A., Del Rincon, A.F., Hammami, A., Iglesias, M., Chaari, F., Viadero, F., Haddar, M., (2018).  Comparison of experimental and operational modal analysis on a back to back planetary gear. Mechanism and Machine Theory, 000, 1–22.
[3]. Giraldo, D.F., Song, W., Dyke, S.J. and Caicedo, J.M. (2009). Modal identification through ambient vibration: comparative study. J. Eng. Mech., 135, 759–770.
[4]. Brincker, R. and Kirkegaard, P.H. (2010). Special issue on operational modal analysis. Mech. Syst. Signal Process., 24, 1209–1212.
[5]. Orlowitz, Esben and Brandt, Anders. (2017). Comparison of experimental and operational modal analysis on a laboratory test plate. Measurement, 102, 121–130.
[6]. Peeters, B., Cornelis, B., Janssens, K. and Van Der Auweraer, H. (2007). Removing disturbing harmonics in operational modal analysis. In Proceedings of International Operational Modal Analysis Conference, Copenhagen, Denmark.
[7]. Brincker, R. (2014). Some elements of operational modal analysis. Shock Vib., 2014, 1–11.
[8]. Felber, A.J. (1994). Development of a hybrid bridge evaluation system. Ph.D. Dissertation, University of British Columbia, Vancouver, Canada.
[9]. Zhang, G., Tang, B. and Tang, G. (2012). An improved stochastic subspace identification for operational modal analysis. Measurement, 45(5), 1246-1256.     
[10]. Peeters, B. and De Roeck, G., 2001. Stochastic system identification for operational modal analysis: a review. Journal of Dynamic Systems, Measurement, and Control, 123(4), 659-667.   
[11]. Brincker, R. and Andersen, P. (2006). Understanding stochastic subspace identification. Proc. 24th IMAC, St. Louis, Missouri, United States, 279–311.
[12]. Reynders, E., Maes, K., Lombaert, G. and De Roeck, G. (2016). Uncertainty quantification in operational modal analysis with stochastic subspace identification: validation and applications. Mechanical Systems and Signal Processing, 66, 13-30.   
[13]. Brincker, R. and Ventura, C. (2015), Introduction to Operational Modal Analysis, John Wiley & Sons Ltd., Chichester, West Sussex, United Kingdom.
[14]. Mellinger, P., Döhler, M. and Mevel, L. (2016). Variance estimation of modal parameters from output-only and input/output subspace-based system identification. Journal of Sound and Vibration, 379, 1-27.   
[15]. Rainieri, C. and Fabbrocino, G. (2014). Operational Modal Analysis of Civil Engineering Structures: An Introduction and Guide for Applications. Springer, New York.     
[16]. Brincker R, Lago¨ TL, Andersen P, Ventura CE. (2005). Improving the classical geophone sensor element by digital correction. In: Proc XXIII International Modal Analysis Conference, Kissimmee, FL.  
[17]. Cyrille, S. (2012). Sensor placement for modal identification. Mechanical Systems and Signal Processing, 27, 461–470.
[18]. Wang, L., Song, R., Wu, Y., and Hu, W. (2016). Statistically Filtering Data for Operational Modal Analysis under Ambient Vibration in Structural Health Monitoring Systems. In MATEC Web of Conferences, Vol. 68, 14010.
[19]. Alamdari, M. M., Li, J. and Samali, B. (2014). FRF-based damage localization method with noise suppression approach. Journal of Sound and Vibration, 333(14), 3305-3320.   
[20]. Brandt, A. (2019). A signal processing framework for operational modal analysis in time and frequency domain. Mechanical Systems and Signal Processing 115, 380–393.
[21]. Rainieri, C. and Fabbrocino, G., (2010). Automated output-only dynamic identification of civil engineering structures. Mechanical Systems and Signal Processing, 24(3), 678-695.   
[22]. Bonness W.K., Jenkins D.M. (2015) Removing Unwanted Noise from Operational Modal Analysis Data. In: Mains M. (eds) Topics in Modal Analysis, Volume 10. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham
[23]. Jiang, X. and Adeli, H. (2004). Wavelet packet-autocorrelation function method for traffic flow pattern analysis. Comput. Civ. Infrastruct. Eng., 19, 324–337.
[24]. Reynders, E. and De Roeck, G. (2008). Reference-based combined deterministic-stochastic subspace identification for experimental and operational modal analysis. Mech. Syst. Signal Process., 22, 617–637.
[25] Au, S.K., Brownjohn, J.M.W., Mottershead, J.E., (2018). Quantifying and managing uncertainty in operational modal
Analysis. Mechanical Systems and Signal Processing 102, 139–157.
[26]. Zhang, J., Prader, J., Grimmelsman, K. A., Moon, F., Aktan, A. E., and Shama, A. (2012). Experimental vibration analysis for structural identification of a long-span suspension bridge. Journal of Engineering Mechanics, 139(6), 748-759.      
[27]. Adeli, H., & Jiang, X. (2006). Dynamic fuzzy wavelet neural network model for structural system identification. Journal of Structural Engineering, 132(1), 102-111.
[28]. Moaveni, B., & Asgarieh, E. (2012). Deterministic-stochastic subspace identification method for identification of nonlinear structures as time-varying linear systems. Mechanical Systems and Signal Processing, 31, 40-55.   
[29]. Perry, M.J. and Koh, C.G. (2008). Output-only structural identification in time domain: Numerical and experimental studies. Earthq. Eng. Struct. Dyn., 37, 517–533.
[30]. Makki Alamdari, M., Samali, B., Li, J., Kalhori, H., and Mustapha, S. (2015). Spectral-based damage identification in structures under ambient vibration. Journal of Computing in Civil Engineering, 30(4), 04015062.   
[31]. Moaveni, B., Barbosa, A. R., Conte, J. P. and Hemez, F. M. (2014). Uncertainty analysis of system identification results obtained for a seven-story building slice tested on the UCSD-NEES shake table. Struct. Control Heal. Monit., 21, 466–483.
[32]. Wu, Z., and Huang, N. E. (2009). Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in adaptive data analysis, 1(01), 1-41.   
[33]. ANSYS Documentation, Mechanical APDL, Element Reference, Element Library, BEAM 188.
[34]. Chopra, A.K. (2001). Dynamics of structures : theory and applications to earthquake engineering, Prentice Hall, USA.
[35]. Mostafavian, S. A., Davoodi, M. R., Vaseghi Amiri, J. (2012). Ball joint behavior in a double layer grid by dynamic model updating. Journal of Constructional Steel Research, 76, 28–38.
[36]. Allemang, R. J. (2003). The modal assurance criterion–twenty years of use and abuse. Sound and vibration, 37(8), 14-23.   
[37]. Gkoktsi, K., Giaralis, A., and TauSiesakul, B. (2016). Sub-Nyquist signal-reconstruction-free operational modal analysis and damage detection in the presence of noise. In SPIE Smart Structures and Materials+ Nondestructive Evaluation and Health Monitoring, International Society for Optics and Photonics, 980312-980312.     

  • Receive Date 10 May 2018
  • Revise Date 23 July 2018
  • Accept Date 02 October 2018