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.
KeywordsFault diagnosis Fuzzy clustering FCM algorithm NC algorithm KFCM algorithm Metaheuristics
The authors acknowledge the financial support provided by the project TIN2014-55024-P from the Spanish Ministry of Economy and Competitiveness and P11-TIC-8001 from the Andalusian Government, both with FEDER funds; FAPERJ, Fundacão Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; CNPq, Conselho Nacional de Desenvolvimento Científico e Tecnológico; CAPES, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, research supporting agencies from Brazil; and CUJAE, Universidad Tecnológica de La Habana José Antonio Echeverría.
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