Importancia para el mantenimiento de elementos mecánicos y fallos en turbinas de vaporAnálisis de históricos

  1. Joel Pino Gómez
  2. Fidel E Hernández Montero
  3. María E Montesinos Otero
  4. María Antonia Téllez
  5. Julio González Martínez
  6. Yuritza Cruz Guzmán
  7. Jorge C Arce Miranda
Revista:
Ingeniería Energética

ISSN: 1815-5901

Ano de publicación: 2017

Volume: 38

Número: 2

Páxinas: 106-114

Tipo: Artigo

DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Outras publicacións en: Ingeniería Energética

Resumo

Fault diagnosis in steam turbines is a very important task in power industry maintenance due to its influence on the reliability of power stations. However, research papers that specifically are referred to the determination of the most critical mechanical components and faults in steam turbines cannot be found. This work is focused to determine the most important components and faults in steam turbines in a Cuban power plant. For this purpose, the analysis of history data, since 1999 to 2014, of three 100 MW steam turbines was accomplished. The main achievement of this work was the definition of the most important components and faults, and that was attained by defining a new index (called as "importance" index), which intends to overcome limitations imposed by the specific characteristics of available data

Referencias bibliográficas

  • Maurice, L Adams. (2010). Rotating machinery vibration: from analysis to troubleshooting. CRC Press.
  • Yufeng, M, Yibing, L, Hongwen, A. Statistical Analysis of Steam Turbine Faults. IEEE International Conference on Mechatronics and Automation. 2011.
  • Zhang, X, Chen, S, Zhu, Y, Yan, W. Fault Detection and Diagnosis for Steam Turbine Based on Kernel GDA. International Conference on Modelling. Identification and Control.. 2011.
  • Barella, S, Bellogini, M, Boniard, IM, Cincera, S. (2011). Failure analysis of a steam turbine rotor. Engineering Failure Analysis. 18. 1511-1519
  • J.fen, Wu. Application of SOM Neural Network in Fault Diagnosis of the Steam Turbine Regenerative System. First International Conference on Machine Learning and Cybernetics. 2002.
  • Kubiak, JS. (2002). The diagnosis of turbine component degradation: Case histories. Applied Thermal Engineering. 22. 1955-1963
  • Galka, T. (2012). Vibration Based Diagnostics of Steam Turbines. Mechanical Engineering. 34. 315-340
  • Karlsson, C, Arriagada, J, Genrup, M. (2008). Detection and interactive isolation of faults in steam turbines to support maintenance decisions. Simulation Modelling Practice and Theory. 16. 689-1703
  • (2011). Experimental studies on high-cycle fatigue and damping properties of R2MA rotor steel in torsion. Strength of Materials. 43. 455-463
  • Bovsunovskii, AP. (2010). Fatigue damage and failure of steam turbine rotors by torsional vibrations. Strength of Materials. 42. 108-113
  • Yang, N. An Expert System for Vibration Fault Diagnosis of Large Steam Turbine Generator Set. 3rd. IEEE International Conference on Computer Research & Development. 2011.
  • Moubray, John. (1997). Reliability-centered Maintenance. Reed Elsevier.
Fonte de datos: Dialnet