ZSlices-Based General Type-2 Fuzzy Fusion of Support Vector Machines With Application to Bearing Fault Detection

Abstract

This paper proposes a fusion model to enhance classification accuracy of support vector machines (SVMs) for fault detection. The proposed method consists of two different phases, where in the first phase, different SVMs are constructed based on training datasets, and these trained SVMs are evaluated with respect to test datasets by calculating distances between test samples and trained hyperplanes. In order to achieve better results, an optimization scheme based on particle swarm optimization (PSO) is employed to adjust the SVMs parameters. In the next phase, a fusion model, in which the attained accuracies and distances are considered as inputs, is constructed. The fusion model utilizes zSlices-based representation of general type-2 fuzzy logic systems to combine different SVMs. The proposed approach is then applied for bearing fault detection of an induction motor with inner and outer race defects. To investigate the effectiveness of the proposed method, the general type-2 and type-1 fuzzy sets are compared with other two state-of-the-art techniques. The obtained results confirm the superiority of the proposed approach.