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A Method for Automated Cavitation Detection with Adaptive Thresholds

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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 cavi-tation 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 cavita-tion 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 hy-droturbine. Results indicate that this fully automated process for selecting cavitation thresholds and classifying cav-itation 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.
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