Journal of Seismology and Earthquake Engineering

Journal of Seismology and Earthquake Engineering

A New Variable Step Size Adaptive Blind Sources Separation for Online Structural Modal Identification

Document Type : Research Article

Authors
Vida Ghasemi 1
Fereidoun Amini 2
1 Ph.D., School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran
2 Professor, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran
10.48303/jsee.2024.2035560.1113
Abstract
The Equivariant adaptive separation by independence (EASI) algorithm, as an online blind structural identification method, is very important not only to better understand the structural response but also to conduct an efficient maintenance and management strategy. However, the traditional EASI algorithm has some drawbacks. It uses a constant step-size parameter and requires establishing a trade-off between the misadjustment in the steady-state and the convergence rate. This paper proposes a new variable step-size equivariant adaptive source separation via independence (VS-EASI) algorithm for online blind modal identification of structures. Unlike the traditional EASI algorithm,
the proposed algorithm adaptively updates its step-size based on the input signals and the unmixing matrix, through establishing a new function between the step-size and the separating indicator. This results in a better performance for the proposed method, and fast convergence speed is achieved while the steady-state error is low. Furthermore, this algorithm mitigates the irrelevant noise, making it more suitable than the EASI algorithm for practical applications. Simulation results of synthetic examples and a benchmark structure verify the superior convergence and better performance of the proposed algorithm in the steady-state over the conventional EASI with a fixed
step-size in stationary environments as well as non-stationary ones.
Keywords
Adaptive blind sources separation
Equivariant adaptive source separation via independence
Modal identification
On-line structural identification
Variable step size adaptive algorithm
Subjects
Amini, F., & Ghasemi, V. (2018). Adaptive modal identification of structures with equivariant adaptive separation via independence approach. Journal of Sound and Vibration, 413, 66-78.
 Azam, S. E., Mariani, S., & Attari, N. (2017). Online damage detection via a synergy of proper orthogonal decomposition and recursive Bayesian filters. Nonlinear Dynamics, 89(2), 1489-1511.
 Azizi, S., Karami, K., & Mariani, S. (2023). Full-field modal analysis using video measurements and a blind source separation methodology. Engineering Proceedings, 58(1), 105.
 Cardoso, J.-F., & Laheld, B. H. (1996). Equivariant adaptive source separation. IEEE Transactions on Signal Processing, 44(12), 3017-3030.
 Chambers, J., Jafari, M., & McLaughlin, S. (2004). Variable step-size EASI algorithm for sequential blind source separation. Electronics Letters, 40(6), 393-394.
 Chan, T., Li, J., & Caprani, C. (2018). Structural    identification and evaluation for SHM applications. Journal of Civil Structural Health Monitoring, 8(5), 719-720.
 Chen, H., Zhang, H., & Zhang, J. (2013). Retrospective on-line EASI blind source separation algorithm. Journal of Signal Processing, 4, 24-31.
 Cheng, W., Zhang, Z., Lee, S., & He, Z. (2014). Investigations of denoising source separation technique and its application to source separation and identification of mechanical vibration signals. Journal of Vibration and Control, 20(14), 2100-2117.
 Cruces-Alvarez, S., Cichocki, A., & Castedo-Ribas, L. (2000). An iterative inversion approach to blind source separation. IEEE Transactions on Neural Networks, 11(6), 1423-1437.
 De Callafon, R. A., Moaveni, B., Conte, J. P., He, X., & Udd, E. (2008). General realization algorithm for modal identification of linear dynamic systems. Journal of Engineering Mechanics, 134(9), 712-722.
 Douglas, S. C., & Cichocki, A. (1998). Adaptive step size techniques for decorrelation and blind source separation. Conference Record of the Thirty-Second Asilomar Conference on Signals, Systems and Computers, 1, 667-671, IEEE.
 Douglas, S. C., & Gupta, M. (2007). Scaled natural gradient algorithms for instantaneous and convolutive blind source separation. 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, 3, 925-928, IEEE.
 Furukawa, A., & Kiyono, J. (2007). Separation of harmonic excitation responses from contaminated measurements based on ICA. Engineering Structures, 29(4), 591-608.
 Ghaderi, P., & Amini, F. (2019). A new method for online identification of civil structures: Virtual synchronization. International Journal of Adaptive Control and Signal Processing, 33(1), 16-38.
 Ghahremani, B., & Bitaraf, M. (2021). Damage assessment using stiffness matrix decomposition and output-only modal identification. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 45(3), 1623-1631.
 Ghasemi, V., & Amini, F. (2020). Online structural identification by Teager energy operator and blind source separation. Smart Structures and Systems, 26(2), 135-146.
 Ghasemi, V., & Amini, F. (2024). Online modal identification by a novel variable step size adaptive blind source separation. Ninth International Conference on Seismology and Earthquake Engineering, Tehran, Iran.
 Ghasemi, V., Amini, F., & Rajabi, E. (2019). Online modal identification of structures by natural gradient algorithm. 8th International Conference on Seismology and Earthquake Engineering, Tehran, Iran.
 Hyvarinen, A., & Oja, E. (2000). Independent component analysis: Algorithms and applications. Neural Networks, 13(4-5), 411-430.
 Jafari, M. G., Chambers, J. A., & Mandic, D. P. (2004). A novel adaptive learning rate sequential blind source separation algorithm. Signal Processing, 84(4), 801-804.
 Khan, I., Shan, D., & Li, Q. (2016). Continuous modal parameter identification of a cable-stayed bridge based on Robustious decomposition and covariance-driven stochastic subspace identification. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 40(1), 11-22.
 Mahvash, A., & Lakis, A. A. (2016). Independent component analysis as applied to vibration source separation and fault diagnosis. Journal of Vibration and Control, 22(6), 1682-1692.
 Maia, N., & Silva, J. (1997). Theoretical and Experimental Modal Analysis. Research Studies Press.
 Maragos, P., Kaiser, J. F., & Quatieri, T. F. (1993). On amplitude and frequency demodulation using   energy operators. IEEE Transactions on Signal   Processing, 41(4), 1532-1550.
 McNeill, S. (2012). An analytic formulation for blind modal identification. Journal of Vibration and Control, 18(14), 2111-2121.
 Morovati, V., & Kazemi, M. (2016). Detection of sudden structural damage using blind source separation and time-frequency approaches. Smart Materials and Structures, 25(5), 055008.
 Nakajima, H., Nakadai, K., Hasegawa, Y., & Tsujino, H. (2009). Blind source separation with para-meter-free adaptive step-size method for robot audition. IEEE Transactions on Audio, Speech, and Language Processing, 18(6), 1476-1485.
 Poncelet, F., Kerschen, G., Golinval, J.-C., & Verhelst, D. (2007). Output-only modal analysis using blind source separation techniques. Mechanical Systems and Signal Processing, 21(6), 2335-2358.
 Rainieri, C. (2014). Perspectives of second-order   blind identification for operational modal analysis of civil structures. Shock and Vibration, Article 364390.
 Rattray, M., Saad, D., & Amari, S.-I. (1998). Natural gradient descent for on-line learning. Physical Review Letters, 81(24), 5461-5464.
 Sadhu, A., & Goli, G. (2017). Blind source separation-based optimum sensor placement strategy for structures. Journal of Civil Structural Health Monitoring, 7(4), 445-458.
 Tong, L., Soon, V., Huang, Y., & Liu, R. (1990). AMUSE: A new blind identification algorithm. IEEE International Symposium on Circuits and Systems, 1784-1787.
 von Hoff, T. P., & Lindgren, A. G. (2002). Adaptive step-size control in blind source separation. Neurocomputing, 49(1-4), 119-138.
 Wang, C., Hu, Y., Zhan, W., Wang, J., Yang, B., Zhang, Y., & Chen, Y. (2017). Multiple random fault sources adaptive blind separation in situation of time-varying source signals and system.Vibroengineering Procedia, 14, 82-86.
 Yuan, L., Wang, W., & Chambers, J. A. (2005). Variable step-size sign natural gradient algorithm for sequential blind source separation. IEEE Signal Processing Letters, 12(8), 589-592.
 Zhang, X., Zhu, X., & Bao, Z. (2003). Grading learning for blind source separation. Science in   China Series F: Information Sciences, 46(1), 31-44.
Journal of Seismology and Earthquake Engineering
Volume 27, Issue 1
Winter 2025
Pages 35-47

  • Receive Date 14 July 2024
  • Revise Date 05 August 2024
  • Accept Date 17 August 2024
  • Publish Date 01 January 2025