A new support vector data description method for machinery fault diagnosis with unbalanced datasets

In machinery fault diagnosis area, the obtained data samples under faulty conditions are usually far less than those under normal condition, resulting in unbalanced dataset issue. The commonly used machine learning techniques including Neural Network, Support Vector Machine, and Fuzzy C-Means, etc. are subject to high misclassification with unbalanced datasets. On the other hand, Support Vector Data Description is suitable for unbalanced datasets, but it is limited for only two class classification. To address the aforementioned issues, Support Vector Data Description based machine learning model is formulated with Binary Tree for multi-classification problems (e.g. multi fault classification or fault severity recognition, etc.) in machinery fault diagnosis. The binary tree structure of multiple clusters is firstly drawn based on the order of cluster-to-cluster distances calculated by Mahalanobis distance. Support Vector Data Description model is then applied to Binary Tree structure from top to bottom for classification. The parameters of Support Vector Data Description are optimized by Particle Swarm Optimization algorithm taking the recognition accuracy as objective function. The effectiveness of presented method is validated in the rotor unbalance severity classification, and the presented method yields higher classification accuracy comparing with conventional models. (C) 2016 Elsevier Ltd. All rights reserved.