Structural health monitoring of turbo generator foundation

Sharafi, Fatemeh; Karami Mohammadi, Reza

doi:10.61186/NMCE.2306.1024

Structural health monitoring of turbo generator foundation

Document Type : Research

Authors

Fatemeh Sharafi ¹

Reza Karami Mohammadi ²

¹ M.Sc. in Earthquake Engineering, Department of Civil Engineering, KN Toosi University of Technology, Tehran, Iran.

² Associate Professor, Department of Civil Engineering, KN Toosi University of Technology, Tehran, Iran

10.61186/NMCE.2306.1024

Abstract

Structural health monitoring has been widely used during the past decades to evaluate the safety of structural assets, detect damages at an early stage, and prevent unexpected and costly damages. The research works in this field are often concerned with bridges and buildings and little research has been conducted on structural health monitoring of power plant infrastructures such as machine foundations. The turbo generator, also referred to as the heart of the power plant, is supported by massive concrete foundations in the turbine hall of thermal power plants. Most TG foundations in thermal power plants are at or close to their design age. The reports on structural damages in thermal power plants show that cracks are frequently observed on the beams and columns of frame-type TG foundations. It is probably the most appropriate time for developing vigorous methods for health monitoring of power plant structures to extend their reliability and life span. This paper uses the vibration-based approach for damage detection of TG foundations. Analytical mode decomposition (AMD) and experimental mode decomposition (EMD) methods are both used for modal parameter identification. A genetic algorithm is further used for finite element model updating and damage detection. The performance of the method is investigated using a 3-D finite element model of a frame-type TG foundation.

Keywords

Damage detection

Hilbert transform

FE model updating

GA algorithm

Signal processing

Subjects

Earthquake Engineering

[1] J.-H. Lee, J.-E. Jung, N.-G. Kim, and B.-H. Song, “Industrial Pipe-Rack Health Monitoring System Based on Reliable-Secure Wireless Sensor Network,” Int. J. Distrib. Sens. Networks, 2012, doi: 10.1155/2012/641391.

[2] S. M. C. M. Randiligama, D. P. Thambiratnam, T. H. T. Chan, S. Fawzia, and K. D. Nguyen, “Vibration based damage detection in hyperbolic cooling towers using coupled method,” Eng. Fail. Anal., 2021.

[3] R. Manabe, H. Kawae, K. Ogawa, and M. Matsuura, “Maintenance Management of Turbine Generator Foundation Affected by Alkali–Silica Reaction,” J. Adv. Concr. Technol., vol. 14, no. 590–606, 2016.

[4] K. G. Bhatia, Foundations for Industrial Machines Handbook for Practising Engineers. 2008.

[5] K. Roy, “Structural Damage Identification Using Mode Shape Slope and Curvature,” J. Eng. Mech., vol. 143, no. 9, 2017.

[6] F. Frigui, J. P. Faye, C. Martin, O. Dalverny, F. Peres, and F. Judenherc, “Global methodology for damage detection and localization in civil engineering structures,” Eng. Struct., vol. 171, pp. 686–695, 2018.

[7] H. Zhu, L. Li, and X.-Q. He, “Damage detection method for shear buildings using the changes in the first mode shape slopes,” Comput. Struct., vol. 89, pp. 733–743, 2011.

[8] M. S. Cao, G. G. Sha, Y. F. Gao, and W. Ostachowicz, “Structural damage identification using damping: a compendium of uses and features,” Smart Mater. Struct., vol. 26, 2017.

[9] A. Zare Hosseinzadeh, G. Amiri, Ghodrati, S. A. Seyed Razzaghi, K. Y. Koo, and S. H. Sung, “Structural damage detection using sparse sensors installation by optimization procedure based on the modal flexibility matrix,” J. Sound Vib., vol. 381, pp. 65–82, 2016.

[10] Y.-S. Kim and H.-C. Eun, “Comparison of Damage Detection Methods Depending on FRFs within Specified Frequency Ranges,” Adv. Mater. Sci. Eng., 2017, doi: doi.org/10.1155/2017/5821835.

[11] J. Hou, S. Wang, Q. Zhang, and L. Jankowski, “An Improved Objective Function for Modal-Based Damage Identification Using Substructural Virtual Distortion Method,” Appl. Sci., vol. 9, no. 5, 2019.

[12] M. M. ABDEL WAHAB, “EFFECT OF MODAL CURVATURES ON DAMAGE DETECTION USING MODEL UPDATING,” Mech. Syst. Signal Process., vol. 15, no. 2, pp. 439–445, 2001.

[13] L. Yu and T. Yin, “Damage Identification in Frame Structures Based on FE Model Updating,” J. Vib. Acoust., vol. 132, no. 5, 2010.

[14] J. Li and H. Hao, “Substructure damage identification based on wavelet-domain response reconstruction,” Struct. Heal. Monit., 2014, doi: 10.1177/1475921714532991.

[15] N. F. Alkayem, M. Cao, Y. Zhang, M. Bayat, and Z. Su, “Structural damage detection using finite element model updating with evolutionary algorithms: a survey,” Neural Comput. Appl., pp. 389–411, 2017.

[16] Y.-J. Cha and O. Buyukozturk, “Structural Damage Detection Using Modal Strain Energy and Hybrid Multiobjective Optimization,” Comput. Civ. Infrastruct. Eng., vol. 30, pp. 347–358, 2015.

[17] G. Chen and Z. Wang, “A signal decomposition theorem with Hilbert transform and its application to narrowband time series with closely spaced frequency components,” Mech. Syst. Signal Process., vol. 28, pp. 258–279, 2012.

[18] Z. Wang and G. Chen, “Analytical mode decomposition with Hilbert transform for modal parameter identification of buildings under ambient vibration,” Eng. Struct., vol. 59, pp. 173–184, 2014.

[19] M. Feldman, “Non-linear free vibration analysis using Hilbert transform - I. Free vibration analysis method ‘freevib,’” Mech. Syst. Signal Process., vol. 8, no. 2, pp. 119–127, 1994.

[20] M. Feldman, “Time-varying vibration decomposition and analysis based on the Hilbert transform,” J. Sound Vib., vol. 295, no. 3–5, pp.

[21] N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, and Q. Zheng, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” in Proceedings of The Royal Society A, 1998, pp. 903–995.

[22] S. Hassiotis and G. D. Jeong, “Identification of stiffness reductions using natural frequencies,” J. Eng. Mech., vol. 121, no. 10, pp. 1106–1113, 1995.

[23] M. Ratcliffe and N. Lieven, “An improved method for parameter selection in finite element model updating.,” Aeronaut. J., vol. 102, no. 1016, pp. 321–329, 1968.

[24] P. Earthquake and B. Engineering Research Center (PEER), University of California, “OpenSees, ‘Open System for Earthquake Engineering Simulation,’” 2008. .

[25] C.-S. Lin and D.-Y. Chiang, “A modified random decrement technique for modal identification from nonstationary ambient response data only,” J. Mech. Sci. Technol., vol. 26, no. 6, pp. 1687–1696, 2012.

[26] G. H. James, T. G. Carne, and J. P. Lauffer, “The Natural Excitation Technique (NExT) for Modal Parameter Extraction From Operating Wind Turbines,” SAND92-1666. UC-261, Sandia Natl. Lab., 1993.

[27] W. Zuo-Cai, Y. Xin, and W.-X. Ren, “Nonlinear structural model updating based on instantaneous frequencies and amplitudes of the decomposed dynamic responses,” Eng. Struct., vol. 100, pp. 189–200, 2015.