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A Robust Condition Monitoring Approach in Industrial Plants Based on the Pythagorean Membership Grades

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Abstract

In this paper, a novel approach for improving the performance and robustness of the condition monitoring system in industrial plants is presented. In the off-line stage of the proposal, the Pythagorean membership grade and its complement of a set of n classification algorithms are used to build the rule-based decisions for obtaining an enhanced partition matrix, which allows to improve the positioning of the center of the classes and data clustering. The use of Pythagorean fuzzy sets allow to obtain a larger classification space, and then the robustness of the condition monitoring system with respect to noise and external disturbances is improved. This represents a very powerful advantage in industrial plants, where process variables are affected by such features. The proposal was proven using the kernel fuzzy C-means and Gustafson-Kessel algorithms on experimental data sets and on the Tennessee Eastman process benchmark. The percentages of satisfactory classification obtained with the proposal were greater than 90% in most of the experiments. In all cases, the proposed methodology significantly outperformed the results obtained by other algorithms recently presented in the scientific literature.

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Acknowledgements

The authors acknowledge the financial support provided by the International Funds and Projects Management Office (OGFPI) of the Ministry of Science, Technology and Environment (CITMA) of Cuba for the national project with code PN223LH004-023, and by the Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior (Finance Code 001 and CAPES-PRINT Process No. 88881.311758/2018-01) from Brazil. Furthermore, the authors appreciate the support provided by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ), both in Brazil.

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Author notes

  1. Franklin Rivas Echeverría, Antônio Silva Neto and Orestes Llanes-Santiago have contributed equally to this work.

Authors and Affiliations

  1. Automation and Computing, Universidad Tecnológica de la Habana José Antonio Echeverría, CUJAE, 114, 11390, Marianao, La Habana, Cuba

    Adrián Rodríguez-Ramos & Orestes Llanes-Santiago

  2. School of Mathematics and Computational Sciences, Yachay Tech University, Hacienda San José, 100119, Urcuqui, Imbabura, Ecuador

    Franklin Rivas Echeverría

  3. Instituto Politécnico do Rio de Janeiro, Universidade do Estado do Rio de Janeiro, Rua Bonfim, 25, Nova Friburgo, Rio de Janeiro, 28625-570, Brazil

    Antônio Silva Neto

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  1. Adrián Rodríguez-Ramos
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Correspondence to Orestes Llanes-Santiago.

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Rodríguez-Ramos, A., Rivas Echeverría, F., Silva Neto, A. et al. A Robust Condition Monitoring Approach in Industrial Plants Based on the Pythagorean Membership Grades. Arab J Sci Eng 48, 14731–14744 (2023). https://doi.org/10.1007/s13369-023-07789-7

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