A Symbol Conditional Entropy-Based Method for Incipient Cavitation Prediction in Hydraulic Turbines

The accurate prediction of incipient cavitation is of great significance for ensuring the stable operation of hydraulic turbines. Hydroacoustic signals contain essential information about the turbine’s operating state. Considering that traditional entropy methods are easily affected by environmental noise when the state pattern is chaotic, leading to the extracted cavitation features not being obvious, a Symbol Conditional Entropy (SCE) feature extraction method is proposed to classify the original variables according to different state patterns. The uncertainty is reduced, and the ability to extract fault information is improved, so more effective cavitation features can be extracted to describe the evolving trend of cavitation. The extracted cavitation features are used as indicators to predict incipient cavitation. In order to avoid missing critical information in the prediction process, an interval mean (IM) algorithm is proposed to determine the initial prediction point. The effectiveness of the proposed method is validated with hydroacoustic signals collected at the Harbin Institute of Large Electric Machinery. The root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of incipient cavitation prediction results decreased to 0.0018, 0.0015, and 1.59%, respectively. The RMSE, MAE, and MAPE of the proposed SCE decreased by 84.62%, 85.29%, and 87% compared with the Permutation Entropy (PE) method. The comparison results with different prediction algorithms show that the proposed SCE has excellent trend prediction performance and high precision.