Fault Diagnosis of Hydroturbine Generating Units Based on Least Squares Support Vector Machines

Abstract

The algorithm of support vector machines (SVM), a novel machine learning method based on statistical learning theory, has been successfully used in pattern recognition and function estimation. The theory of least squares support vector machines (LS-SVM) is a least squares version of standard SVM, which involves equality instead of inequality constraints and works with a least squares object function. A systematic approach based on LS-SVM and wavelet decomposition for fault diagnosis of hydroturbine generating units (HGU) is proposed in this paper. The vibration signals under abnormal conditions are collected and preprocessed with the wavelet decomposition and feature information of signals is extracted as the feature vectors for training and testing the LS-SVM. To classify multiple fault modes of HGU, a multiclass classifier based on LS-SVM with minimum output codes (MOC) is constructed and used in the fault diagnosis for HGU. It's showed in the simulation result that the fault types can be identified and diagnosed by the above method. Compared with the result of a RBF neural network, more excellent identification accuracy indicates the feasibility and effectiveness of LS-SVM in the fault diagnosis of HGU.