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A Proposal of Hybrid Fuzzy Clustering Algorithm with Application in Condition Monitoring of Industrial Processes

  • Adrián Rodríguez-Ramos
  • Antônio José da Silva Neto
  • Orestes Llanes-SantiagoEmail author
Chapter
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 377)

Abstract

In this chapter a hybrid algorithm using fuzzy clustering techniques is presented. The algorithm is applied in a condition monitoring scheme with online detection of novel faults and automatic learning. The proposal, initially identifies the outliers based on data density. Later, the outliers are removed and the clustering process is performed. To extract the important features and improve the clustering, the maximum-entropy-regularized weighted fuzzy c-means is used. Then, the use of kernel functions is performed for clustering the data, where there is a non-linear relationship between the variables. Thus, the classification accuracy can be improved because better class separability is achieved. Next, the regulation factor of the resulting partition fuzziness (parameter m) and the Gaussian Kernel bandwidth (parameter \(\sigma \)) are optimized. The feasibility of the proposal is demonstrated by using the DAMADICS benchmark.

Notes

Acknowledgements

The authors acknowledge the financial support provided by 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; UERJ, Universidade do Estado do Rio de Janeiro and CUJAE, Universidad Tecnológica de La Habana José Antonio Echeverría and the help of Dr. Marcos Quiñones Grueiro (Universidad Tecnológica de La Habana José Antonio Echeverría)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Adrián Rodríguez-Ramos
    • 1
  • Antônio José da Silva Neto
    • 2
  • Orestes Llanes-Santiago
    • 1
    Email author
  1. 1.Departamento de Automática y ComputaciónUniversidad Tecnológica de la Habana José Antonio Echeverría, CUJAELa HabanaCuba
  2. 2.Instituto Politécnico da Universidade do Estado do Rio de Janeiro (IPRJ/UERJ)Nova FriburgoBrazil

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