Selection of variables related to journal bearing faults through logical combinatorial pattern recognition

Authors

  • Joel Pino Gómez Universidad Tecnológica de La Habana
  • Fidel Ernesto Hernández Montero Universidad Tecnológica de La Habana
  • Julio César Gómez Mancilla Instituto Politécnico Nacional

Keywords:

Mixed features, Features selection, Logical combinatorial pattern recognition, Diagnostic, Journal bearing

Abstract

The text experts in industrial diagnostics can provide essential information, expressed in mixed variables (quantitative and qualitative), about journal bearing faults. This work focuses on identifying the most important features for fault classification in steam turbine journal bearings using logical combinatorial pattern recognition tools. The results revealed that qualitative features are more relevant than traditionally employed numerical features.

 

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Author Biographies

Joel Pino Gómez, Universidad Tecnológica de La Habana

Universidad Tecnológica de La Habana, Facultad de Ingeniería en Telecomunicaciones

Fidel Ernesto Hernández Montero, Universidad Tecnológica de La Habana

Universidad Tecnológica de La Habana, Facultad de Ingeniería en Telecomunicaciones

Julio César Gómez Mancilla, Instituto Politécnico Nacional

Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica

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Published

2024-12-20

How to Cite

[1]
J. Pino Gómez, F. E. Hernández Montero, and J. C. Gómez Mancilla, “Selection of variables related to journal bearing faults through logical combinatorial pattern recognition”, Ingeniare, Rev. chil. ing., vol. 28, no. 3, Dec. 2024.

Issue

Vol. 28 No. 3 (2020): Volumen 28 Issue 3

Section

Articulos

License

Copyright (c) 2024 Revista Ingeniare

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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