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

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Uncertainty Management with Fuzzy and Rough Sets

Part of the book series: Studies in Fuzziness and Soft Computing ((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.

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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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Authors and Affiliations

  1. Departamento de Automática y Computación, Universidad Tecnológica de la Habana José Antonio Echeverría, CUJAE, Calle 114, No. 11901, 10390, La Habana, Cuba

    Adrián Rodríguez-Ramos & Orestes Llanes-Santiago

  2. Instituto Politécnico da Universidade do Estado do Rio de Janeiro (IPRJ/UERJ), Rua Bonfim, 25 - Parte - Campus UERJ, Nova Friburgo, RJ 28625-570, Brazil

    Antônio José da Silva Neto

Authors
  1. Adrián Rodríguez-Ramos
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  2. Antônio José da Silva Neto
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  3. Orestes Llanes-Santiago
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Corresponding author

Correspondence to Orestes Llanes-Santiago .

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Editors and Affiliations

  1. Department of Computer Science, Universidad Central "Marta Abreu" de Las Villas, Santa Clara, Villa Clara, Cuba

    Rafael Bello

  2. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada

    Rafael Falcon

  3. Department of Computer Science and Artificial Intelligence, Technical School of Informatics and Telecommunications Engineering, University of Granada, Granada, Spain

    José Luis Verdegay

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Rodríguez-Ramos, A., da Silva Neto, A.J., Llanes-Santiago, O. (2019). A Proposal of Hybrid Fuzzy Clustering Algorithm with Application in Condition Monitoring of Industrial Processes. In: Bello, R., Falcon, R., Verdegay, J. (eds) Uncertainty Management with Fuzzy and Rough Sets. Studies in Fuzziness and Soft Computing, vol 377. Springer, Cham. https://doi.org/10.1007/978-3-030-10463-4_1

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