Proposal of an approach to data driven in fault diagnosis using fuzzy clustering techniques

In this work an approach to design data driven based fault diagnosis systems using fuzzy clustering techniques is presented. In the proposal, the data was first preprocessed 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. The proposed approach was validated using the nonlinear continuous stirred-tank reactor benchmark problem. The results obtained indicate the feasibility of the proposal.