Abstract
Traditional modal parameter identification methods have many disadvantages, especially when used for processing nonlinear and non-stationary signals. In addition, they are usually not able to accurately identify the damping ratio and damage. In this study, methods based on the Hilbert-Huang transform (HHT) are investigated for structural modal parameter identification and damage diagnosis. First, mirror extension and prediction via a radial basis function (RBF) neural network are used to restrain the troublesome end-effect issue in empirical mode decomposition (EMD), which is a crucial part of HHT. Then, the approaches based on HHT combined with other techniques, such as the random decrement technique (RDT), natural excitation technique (NExT) and stochastic subspace identification (SSI), are proposed to identify modal parameters of structures. Furthermore, a damage diagnosis method based on the HHT is also proposed. Time-varying instantaneous frequency and instantaneous energy are used to identify the damage evolution of the structure. The relative amplitude of the Hilbert marginal spectrum is used to identify the damage location of the structure. Finally, acceleration records at gauge points from shaking table testing of a 12-story reinforced concrete frame model are taken to validate the proposed approaches. The results show that the proposed approaches based on HHT for modal parameter identification and damage diagnosis are reliable and practical.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Caicedo JM, Dyke SJ and Johnson EA (2004), “Natural Excitation Technique and Eigensystem Realization Algorithm for Phase I of the IASC-ASCE Benchmark Problem: Simulated Data,” Journal of Engineering Mechanics, 130: 49–60.
Chen B, Xu YL and Qu WL (2005), “Evaluation of Atmospheric Corrosion Damage to Steel Space Structures in Coastal Areas,” International Journal of Solids and Structures, 42(16): 4673–4694.
Curadelli RO, Riera JD, Ambrosini D and Amani MG (2008), “Damage Detection by Means of Structural Damping Identification,” Engineering Structures, 30: 3497–3504.
Doebling SW, Farrar CR, Prime MB and Shevitz DW (1996), “Damage Identification and Health Monitoring of Structural and Mechanical Systems from Changes in Their Vibration Characteristics: A Literature Review,” Los Alamos National Laboratory, Los Alamos, New Mexico.
Farrar CR and Jauregui DA (1998), “Comparative Study of Damage Identification Algorithms Applied to a Bridge: I Experiment,” Smart Materials and Structures, 7: 704–719.
Han JP, Lu XL and Wang FX (2009), “Comparison of Modal Parameter Identification Algorithms Based on Shaking Table Model Test Data,” Proc. of SPIE, 7375: 73750K-1–7.
Han JP, Qian J and Dong XJ (2010), “Suppression of end-Effect in Empirical Mode Decomposition by Mirror Extension and Radial Basis Function Neural Network Prediction,” Journal of Vibration, Measurement & Diagnosis, 30(4): 414–417, 474.
Hsu WK, Chiou DJ, Chen CW, Liu MY, Chiang WL and Huang PC (2013), “Sensitivity of Initial Damage Detection for Steel Structures Using the Hilbert-Huang Transform Method,” Journal of Vibration and Control, 19: 857–878.
Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Tung CC and Liu HH (1998), “The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis,” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 454: 903–995.
Ibrahim SR (1977), “Random Decrement Technique for Modal Identification of Structures,” Journal of Spacecraft and Rockets, 14: 696–700.
Liu TY, Chiang WL Chen CW, Hsu WK, Lin CW, Chiou DJ and Huang PC (2012), “Structural System Identification for Vibration Bridges Using the Hilbert-Huang Transform,” Journal of Vibration and Control, 18: 1939–1956.
Lu XL, Li PZ and Chen YQ (2004), Complete Data of Shaking Table Test on a 12-story Reinforced Concrete frame Model, State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai. (in Chinese)
Rilling G, Flandrin P and Goncalves P (2003), “On Empirical Mode Decomposition and Its Algorithms,” IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, NSIP-03, 1–5.
Van Overschee P and De Moor B (1996), Subspace Identification for Linear Systems: Theoryimplementation-applications, Kluwer Academic Publishers, Boston.
Yang JN and Lei Y (2000), “Identification of Tall Building Using Noisy Wind Vibration Data,” Proc. of the International Conference on Advances in Structural Dynamics, Hongkong, China.
Yu D and Ren W (2005), “EMD-based Stochastic Subspace Identification of Structures from Operational Vibration Measurements,” Engineering Structures, 27: 1741–1751.
Author information
Authors and Affiliations
Corresponding author
Additional information
Supported by: Gansu Science and Technology Key Project under Grant No. 2GS057-A52-008
Rights and permissions
About this article
Cite this article
Han, J., Zheng, P. & Wang, H. Structural modal parameter identification and damage diagnosis based on Hilbert-Huang transform. Earthq. Eng. Eng. Vib. 13, 101–111 (2014). https://doi.org/10.1007/s11803-014-0215-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11803-014-0215-3