Data modeling for joint analysis of symptom variables in 100 MW steam turbines

Authors

  • Francisco Antonio de la Torre Silva Universidad Tecnológica de la Habana José Antonio Echeverría, Centro de Estudios en Ingeniería de Mantenimiento, CEIM. La Habana, Cuba.
  • Evelio Palomino Marín Universidad Tecnológica de la Habana José Antonio Echeverría, Centro de Estudios en Ingeniería de Mantenimiento, CEIM. La Habana, Cuba.
  • Armando Díaz Concepción Universidad Tecnológica de la Habana José Antonio Echeverría, Centro de Estudios en Ingeniería de Mantenimiento, CEIM. La Habana, Cuba.
  • Alexander Alfonso Álvarez Universidad de la Serena. La Serena, Chile.
  • Joel Guillen García Universidad Técnica de Manabí. Puerto Viejo. Ecuador.
  • Alejandra Elena García Toll Universidad Tecnológica de la Habana José Antonio Echeverría, Centro de Estudios en Ingeniería de Mantenimiento, CEIM. La Habana, Cuba.

Keywords:

modelling of data, multivariate statistical control of process (MSPC), statistical processing of data, statistical model of data, condition of steam turbines, data mining

Abstract

The paper presents the modeling of a data sample for the joint analysis of symptom variables in 100 MW steam turbines. The main objective is to obtain a pattern data sample representing the normal variability of the turbine's dynamic mechanical behavior. The preprocessing and processing of the database are detailed, which had not been previously documented in the literature for this type of turbine. The results show that the applied methodology allows for the identification and characterization of normal and abnormal operating conditions of the turbine, providing a solid foundation for future condition monitoring and predictive maintenance studies in steam energy systems. This approach improves the accuracy and reliability of fault diagnosis and performance analysis.

Dr. C. Francisco Antonio de la Torre Silva

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Published

2024-05-20

How to Cite

1.
de la Torre Silva FA, Palomino Marín E, Díaz Concepción A, Alfonso Álvarez A, Guillen García J, García Toll AE. Data modeling for joint analysis of symptom variables in 100 MW steam turbines. Ing. Mec. [Internet]. 2024 May 20 [cited 2025 Sep. 13];27(2):e696. Available from: https://ingenieriamecanica.cujae.edu.cu/index.php/revistaim/article/view/790

Issue

Vol. 27 No. 2 (2024): mayo-agosto

Section

Original article

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