An Approach to Fault Diagnosis Using Fuzzy Clustering Techniques

  • Adrián Rodríguez Ramos
  • José Manuel Bernal de Lázaro
  • Antônio J. da Silva Neto
  • Carlos Cruz Corona
  • José Luís Verdegay
  • Orestes Llanes-SantiagoEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 643)


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.


Fault 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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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Adrián Rodríguez Ramos
    • 1
  • José Manuel Bernal de Lázaro
    • 1
  • Antônio J. da Silva Neto
    • 2
  • Carlos Cruz Corona
    • 3
  • José Luís Verdegay
    • 3
  • Orestes Llanes-Santiago
    • 1
    Email author
  1. 1.Automation and Computing DepartmentUniversidad Tecnológica de La Habana, CUJAEHavanaCuba
  2. 2.Instituto PolitécnicoUniversidade do Estado do Rio de JaneiroNova FriburgoBrazil
  3. 3.DECSAIUniversidad de GranadaGranadaSpain

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