A Method for Automated Cavitation Detection with Adaptive Thresholds

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dc.contributor.authorGregg, Seth W.
dc.contributor.authorSteele, John P.H.
dc.contributor.authorVan Bossuyt, Douglas L.
dc.contributor.corporateNaval Postgraduate School (U.S.)en_US
dc.contributor.departmentSystems Engineering (SE)en_US
dc.date2018-02
dc.date.accessioned2020-07-20T23:31:55Z
dc.date.available2020-07-20T23:31:55Z
dc.date.issued2018-02
dc.descriptionThe article of record as published may be found at https://doi.org/10.1016/j.procs.2019.05.089
dc.description.abstractHydroturbine operators who wish to collect cavitation intensity data to estimate cavitation erosion rates and calculate remaining useful life (RUL) of the turbine runner face several practical challenges related to long term cavitation detection. This paper presents a novel method that addresses these challenges including: a method to create an adaptive cavitation threshold, and automation of the cavitation detection process. These two strategies result in collecting consistent cavitation intensity data. While domain knowledge and manual interpretation are used to choose an appropriate cavitation sensitivity parameter (CSP), the remainder of the process is automated using both supervised and unsupervised learning methods. A case study based on ramp-down data, taken from a production hydroturbine, is presented and validated using independently gathered survey data from the same hydroturbine. Results indicate that this fully automated process for selecting cavitation thresholds and classifying cavitation performs well when compared to manually selected thresholds. This approach provides hydroturbine operators and researchers with a clear and effective way to perform automated, long term, cavitation detection, and assessment.en_US
dc.description.funderFunded in part by the Office of Energy Efficiency and Renewable Energy (EERE), U.S. Department of Energy, under Award Number DE-EE0002668 and the Hydro Research Foundation.en_US
dc.description.sponsorshipFunded in part by the Office of Energy Efficiency and Renewable Energy (EERE), U.S. Department of Energy, under Award Number DE-EE0002668 and the Hydro Research Foundation.en_US
dc.format.extent18 p.en_US
dc.identifier.citationGregg, Seth W., John PH Steele, and Douglas L. Van Bossuyt. "A Method for Automated Cavitation Detection with Adaptive Thresholds. International Journal of Prognostics and Health Management, February 2018en_US
dc.identifier.urihttps://hdl.handle.net/10945/65172
dc.publisherWileyen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleA Method for Automated Cavitation Detection with Adaptive Thresholdsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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