Cavitation Detection in Centrifugal Pump Based on Interior Flow‐Borne Noise Using WPD‐PCA‐RBF

Cavitation detection is particularly essential for operating efficiency and stability of pumps. In this work, to improve the accuracy and efficiency of identification, an approach combining wavelet packet decomposition (WPD) with principal component analysis (PCA) and radial basic function (RBF) neural network is introduced to detect the cavitation status for centrifugal pumps. The cavitation performance and interior flow‐borne noise are measured under three different flow conditions. Then, time‐frequency domain analysis is performed on the interior flow‐borne noise signal using WPD, and the energy coefficient of each node is calculated to determine the optimal decomposition frequency band. Six‐feature parameters are extracted based on frequency‐division statistics, including three time‐domain features and three wavelet packet features. After that, the PCA is applied for dimensionality reduction. Finally, three cavitation statuses of noncavitation, inception cavitation, and serious cavitation are identified adopting RBF neural network. The results show that the comprehensive identification rate of the proposed method for three cavitation statuses reaches 98.2% with low identification error. The method based on interior flow‐borne noise analysis can be well applied for on‐line monitoring and diagnosis of pump industry.