A Method for Automated Cavitation Detection with Adaptive Thresholds
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Authors
Gregg, Seth W.
Steele, John P.H.
Van Bossuyt, Douglas L.
Subjects
Advisors
Date of Issue
2018-02
Date
2018-02
Publisher
Wiley
Language
Abstract
Hydroturbine 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.
Type
Article
Description
The article of record as published may be found at https://doi.org/10.1016/j.procs.2019.05.089
Series/Report No
Department
Systems Engineering (SE)
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Funded 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.
Funder
Funded 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.
Format
18 p.
Citation
Gregg, 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 2018
Distribution Statement
Rights
This 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.