Feature Selection for Monitoring Erosive Cavitation on a Hydroturbine
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Published
Nov 16, 2020
Seth W. Gregg
John P.H. Steele
Douglas L. Van Bossuyt
Abstract
This paper presents a method for comparing and evaluating cavitation detection features - the first step towards estimating remaining useful life (RUL) of hydroturbine runners that are
impacted by erosive cavitation. The method can be used to quickly compare features created from cavitation survey data collected on any type of hydroturbine, sensor type, sensor location, and cavitation sensitivity parameter (CSP). Although manual evaluation and knowledge of hydroturbine cavitation is still required for our feature selection method, the use of principal component analysis greatly reduces the number of plots that require evaluation. We present a case study based on a cavitation survey data collected on a Francis hydroturbine located at a hydroelectric plant and demonstrate the selection of the most advantageous sensor type, sensor location, and CSP to use on this hydroturbine for long-term monitoring of erosive cavitation. Our method provides hydroturbine operators and researchers with a clear and effective means to determine preferred sensors, sensor placements, and CSPs while also laying the groundwork for determining RUL in the future.
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Keywords
monitoring, Hydroturbine, Hydro Power, Cavitation, Cavitation Monitoring
References
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Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of machine learning research, 3(Mar), 1157–1182.
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Heng, A., Zhang, S., Tan, A. C., & Mathew, J. (2009). Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing, 23(3), 724–739. Retrieved from http://linkinghub.elsevier.com/retrieve/pii/S0888327008001489 doi: 10.1016/j.ymssp.2008.06.009
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Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510. Retrieved from http://linkinghub.elsevier.com/retrieve/pii/S0888327005001512 doi: 10.1016/j.ymssp.2005.09.012
Jian, W., Petkovšek, M., Houlin, L., Širok, B., & Dular, M. (2015). Combined Numerical and Experimental Investigation of the Cavitation Erosion Process. Journal of Fluids Engineering, 137(5), 051302. Retrieved from http://fluidsengineering.asmedigitalcollection.asme.org/article.aspx?doi=10.1115/1.4029533 doi: 10.1115/1.4029533
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Kan, M. S., Tan, A. C., & Mathew, J. (2015). A review on prognostic techniques for non-stationary and non-linear rotating systems. Mechanical Systems and Signal Processing, 62-63, 1–20. Retrieved from http://www.sciencedirect.com/science/article/pii/S0888327015000898 doi: 10.1016/j.ymssp.2015.02.016
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Kim, K., Uluyol, O., Parthasarathy, G., & Mylaraswamy, D. (2012). Fault Diagnosis of Gas Turbine Engine LRUs Using the Startup Characteristics. Phm, 1–10. The Knowledge Stream - Detecting Cavitation to Protect and Maintain Hydraulic Turbines. (2014). (Summer 2014). Retrieved from https://www.usbr.gov/research/docs/updates/2014-14-cavitation.pdf
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