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 areimpacted 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.

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.