Statistical Modeling of Time Series for Ice Accretion Detection on Bridge Cables
Publication: Journal of Cold Regions Engineering
Volume 32, Issue 2
Abstract
In the northern regions, ice accretion on bridge cables poses serious risks to the structure as well as to vehicular traffic when ice falls from the cables onto the road. The detection and quantification of ice formation allows the anticipation of ice falls and increases the safety of the structures. In this paper, an ice accretion detector was developed on the basis of the statistical modeling of vibration response signals. Three different methods were tested on the acceleration signals obtained from a bridge cable. The methods included the Fourier transform analysis, the autoregressive model, and the continuous wavelet transform analysis. Damage-sensitive features (DSF) were extracted from these models and tested with the data collected from a laboratory experiment conducted in a climatic wind tunnel. It was found that all three DSFs were correlated to ice accretion. The wavelet-based DSF had the highest correlation, resulting in the largest change of the DSF value and in the smallest estimation error.
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Acknowledgments
This research was partially supported through a NSF NEESR Grant (105651), the John A. Blume Fellowship from Stanford University, and the Department of Civil Engineering at the Technical University of Denmark. The authors would like to express their gratitude to Celeste Burlina, graduate student at DTU, for her extensive help with the experimentation.
References
Adams, D. E., Dana, S., Kusnick, J., and Myrent, N. (2014). “Dynamics-based health monitoring and control of wind turbine rotors.” WIT Trans. State Art Sci. Eng., 81, 25–66.
Andre, J., Kiremidjian, A., Liao, Y., Georgakis, C., and Rajagopal, R. (2016). “Structural health monitoring approach for detecting ice accretion on bridge cable using the Haar wavelet transform.” SPIE Smart Structures and Materials+ Nondestructive Evaluation and Health Monitoring, International Society for Optics and Photonics, Bellingham, WA, 98030F.
Arsenault, T. J., Achuthan, A., Marzocca, P., Grappasonni, C., and Coppotelli, G. (2013). “Development of a FBG based distributed strain sensor system for wind turbine structural health monitoring.” Smart Mater. Struct., 22(7), 075027.
Bragg, M. B., Hutchison, T., Merret, J., Oltman, R., and Pokhariyal, D. (2000). Effect of ice accretion on aircraft flight dynamics, AIAA, Reston, VA.
Brockwell, P. J., and Davis, R. A. (2006). Introduction to time series and forecasting, Springer Science & Business Media, Berlin.
Canova, F. (1998). “Detrending and business cycle facts.” J. Monetary Econ., 41(3), 475–512.
CBC (Canadian Broadcasting Corporation). (2012). ⟨http://www.cbc.ca/news/canada/british-columbia/port-mann-bridge-reopens-after-ice-damages-cars-1.1279220⟩ (Dec. 19, 2012).
Chopra, A. K. (1995). Dynamics of structures, Vol. 3, Prentice Hall, Upper Saddle River, NJ.
Farzaneh, M., and Savadjiev, K. (2005). “Statistical analysis of field data for precipitation icing accretion on overhead power lines.” IEEE Trans. Power Delivery, 20(2), 1080–1087.
Gimsing, N. J., and Georgakis, C. T. (2011). Cable supported bridges: Concept and design, Wiley, New York.
Homola, M. C., Nicklasson, P. J., and Sundsbø, P. A. (2006). “Ice sensors for wind turbines.” Cold Reg. Sci. Technol., 46(2), 125–131.
Kleissl, K., and Georgakis, C. (2010). “Bridge ice accretion and de-and anti-icing systems: A review.” 7th Int. Cable Supported Bridge Operators’Conf., Jiangsu Runyang Bridge Development Co. Ltd., Jiangsu, China.
Liao, Y., et al. (2014). “SnowFort: An open source wireless sensor network for data analytics in infrastructure and environmental monitoring.” IEEE Sens. J., 14(12), 4253–4263.
Likitkumchorn, N. (2014). “Ice prevention and weather monitoring on cable-stayed bridges.” M.S. thesis, Univ. of Toledo, Toledo, OH.
Matteoni, G., and Georgakis, C. T. (2015). “Effects of surface roughness and cross-sectional distortion on the wind-induced response of bridge cables in dry conditions.” J. Wind Eng. Ind. Aerodyn., 136(1), 89–100.
Nair, K. K., Kiremidjian, A. S., and Law, K. H. (2006). “Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure.” J. Sound Vib., 291(1), 349–368.
Polastre, J., Szewczyk, R., and Culler, D. (2005). “Telos: Enabling ultra-low power wireless research.” IPSN 2005. 4th Int. Symp. on Information Processing in Sensor Networks, ACM SIGBED and IEEE Signal Processing Society, NJ, 364–369.
WindPower. (2011). “Detecting ice on wind-turbine blades.” ⟨http://www.windpowerengineering.com/maintenance/detecting-ice-on-wind-turbine-blades/⟩ (Jul. 21, 2011).
Information & Authors
Information
Published In
Journal of Cold Regions Engineering
Volume 32 • Issue 2 • June 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: Nov 17, 2016
Accepted: Sep 20, 2017
Published online: Feb 14, 2018
Published in print: Jun 1, 2018
Discussion open until: Jul 14, 2018
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