Impact Damage Prediction for Wave Energy Converters
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Marine and hydrokinetic energy is of growing interest across the globe because it has the potential to provide a large source of renewable energy from the world’s oceans and rivers. These marine and hydrokinetic devices, such as wave energy converters, must operate remotely in all weather conditions, including severe storms. Thus, these devices can suffer from structural damage affecting their performance and lifespan. Therefore, there is interest in developing structural health monitoring systems that can identify new damage, estimate its severity, and then make a decision to provide crews with a maintenance or control recommendation. In this study, we investigate using the electromechanical impedance response of piezoelectric transducers to actively monitor the structural health of composite materials similar to those used in several marine and hydrokinetic devices. Recurring impact damage experiments were completed on five plates using a test drop stand, consisting of five consecutive impacts at the same location for each plate. Classification and regression methods were evaluated in an attempt to predict impact damage on a new plate. Machine learning algorithms were used on data collected over a frequency range of 10 kHz to 100 kHz for two types of piezoelectric transducers.
How to Cite
##plugins.themes.bootstrap3.article.details##
structural health monitoring
Bishop, C. (2006). Pattern recognition and machine learning. Springer-Verlag New York.
Breiman, L. (2001). Random forests. Machine learning, 45(1), 5–32.
DOE. (2016). U.S. department of energy wind and water power technologies office funding in the united states: Marine and hydrokinetic projects.
Dumoulin, C., Karaiskos, G., Sener, J.-Y., & Deraemaeker, A. (2014). Online monitoring of cracking in concrete structures using embedded piezoelectric transducers. Smart materials and structures, 23(11), 115016.
EERE. (2017). Wave energy prize. Retrieved from https://waveenergyprize.org/ (Online; accessed Aug-2017)
Farinholt, K., Desrosiers, M., Kim, M., Friedersdorf, F., Adams, S., & Beling, P. (2016). Active sensing and damage classification for wave energy converter structural composites. In Asme 2016 conference on smart materials, adaptive structures and intelligent systems.
Frieden, J., Cugnoni, J., Botsis, J., & Gm¨ur, T. (2012a). Low energy impact damage monitoring of composites using dynamic strain signals from FBG sensors–Part II: Damage identification. Composite Structures, 94(2), 593–600.
Frieden, J., Cugnoni, J., Botsis, J., & Gm¨ur, T. (2012b). Low energy impact damage monitoring of composites using dynamic strain signals from FBG sensors–Part I: Impact detection and localization. Composite Structures, 94(2), 438–445.
Lehmann, M., Karimpour, F., Goudey, C. A., Jacobson, P. T., & Alam, M.-R. (2017). Ocean wave energy in the united states: Current status and future perspectives. Renewable and Sustainable Energy Reviews.
Nardi, D., Lampani, L., Pasquali, M., & Gaudenzi, P. (2016). Detection of low-velocity impact-induced delaminations in composite laminates using auto-regressive models. Composite Structures, 151, 108–113.
Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on knowledge and data engineering, 22(10), 1345–1359.
Park, G., & Inman, D. J. (2007). Structural health monitoring using piezoelectric impedance measurements. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 365(1851), 373–392.
Pérez, M. A., Gil, L., & Oller, S. (2014). Impact damage identification in composite laminates using vibration testing. Composite Structures, 108, 267–276.
Taylor, S. G., Farinholt, K., Choi, M., Jeong, H., Jang, J., Park, G., . . . Todd, M. D. (2014). Incipient crack detection in a composite wind turbine rotor blade. Journal of Intelligent Material Systems and Structures, 25(5), 613–620.
Xie, X., Xu, D., Guo, X., Sha, F., & Huang, S. (2016). Nonlinear ultrasonic nondestructive evaluation of damaged concrete based on embedded piezoelectric sensors. Research in Nondestructive Evaluation, 27(3), 125–136.
Xu, D., Banerjee, S., Wang, Y., Huang, S., & Cheng, X. (2015). Temperature and loading effects of embedded smart piezoelectric sensor for health monitoring of concrete structures. Construction and Building Materials, 76, 187–193.
Yoon, J., He, D., & Van Hecke, B. (2015). On the use of a single piezoelectric strain sensor for wind turbine planetary gearbox fault diagnosis. IEEE Transactions on Industrial Electronics, 62(10), 6585–6593.
Yuce, M. I., & Muratoglu, A. (2015). Hydrokinetic energy conversion systems: a technology status review. Renewable and Sustainable Energy Reviews, 43, 72–82.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.