An Approach to Fault Diagnosis Using Fuzzy Clustering Techniques

In this paper a novel approach to design data driven based fault diagnosis systems using fuzzy clustering techniques is presented. In the proposal, the data was first pre-processed using the Noise Clustering algorithm. This permits to eliminate outliers and reduce the confusion as a first part of the classification process. Secondly, the Kernel Fuzzy C-means algorithm was used to achieve greater separability among the classes, and reduce the classification errors. Finally, it can be implemented a step for optimizing the parameters of the NC and KFCM algorithms. The proposed approach was validated using the iris benchmark data sets. The obtained results indicate the feasibility of the proposal.