Fault diagnosis for ship sewage treatment equipment based on clustering support vector machine

Abstract

To solve the problems of condition monitoring and fault diagnosis for ship sewage treatment equipment, a fault diagnosis model based on clustering Support Vector Machines (SVM) was proposed. In the model, the neural network clustering algorithm was used to realize clustering analysis and to obtain the normal subspaces and the abnormal subspaces in the condition monitoring sample space. Then, to the abnormal subspaces, the multi-classification SVM based on binary tree architecture was designed to carry out the fault diagnosis and recognition. Compared with the traditional SVM learning algorithms, the proposed algorithm avoided the blind classification and improved the classification performance in some extent. The model was applied to a ship sewage treatment equipment to train and exam the measured samples. Experiment results show this method has good generalization and expendibility.