Vibration-based multi-fault diagnosis for centrifugal pumps

Abstract

The thesis proposes a new method for vibration fault diagnosis of centrifugal pumps by combining statistical features, Symlet Wavelet transform, Principal Component Analysis and k-Nearest Neighbors. Six statistical features were extracted from the low frequency part of wavelet decomposition which was then used as input features for the PCA model. The fault detection utilised T2 and Q statistics while fault classification and identification were carried out using score matrices and k-Nearest Neighbors respectively.