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 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.
Highlights
Cavitation events in hydroturbines can lead to damage to the turbine runners and reduced remaining useful life (RUL)
This paper presents a method for comparing and evaluating cavitation detection features - the first step toward estimating RUL of hydroturbine runners
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)
Summary
Cavitation events in hydroturbines can lead to damage to the turbine runners and reduced remaining useful life (RUL). Current methods of detecting cavitation events and prognosticating RUL have not been successful in providing hydroelectric power plant operators with meaningful information. Structured methods of data collection and feature selection as well as automated methods for cavitation detection, and RUL prediction are needed to provide plant operators with a clear view of hydroturbine health and RUL. Feature selection and automated cavitation detection remain to be addressed. We present background information on cavitation damage in hydroturbines to demonstrate the need for a method to rapidly compare cavitation detection features for long term monitoring. While efforts have been made to establish reliable RUL predictions, hydro power plant operators cannot or choose not to use existing solutions. Cavitation and cavitation erosion is one of the most pervasive problems found in hydroturbines (see Figure 1), pumps, and ship propellers
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