Applying ensemble neural networks to analyze industrial maintenance: Influence of Saharan dust transport on gas turbine axial compressor fouling

Authors

  • D. Gonzalez-Calvo University of La Laguna, Spain
  • R.M. Aguilar University of La Laguna, Spain
  • C. Criado-Hernandez University of La Laguna, Spain
  • L.A. Gonzalez-Mendoza University of La Laguna, Spain

DOI:

https://doi.org/10.4114/intartif.vol24iss68pp53-71

Keywords:

Gas Turbine, Compressor Fouling, Neural Networks Ensemble, Saharan dust, Canary Islands

Abstract

The planning of industrial maintenance associated with the production of electricity is vital, as it yields a current and future snapshot of an industrial component in order to optimize the human, technical and economic resources of the installation. This study focuses on the degradation due to fouling of a gas turbine in the Canary Islands, and analyzes fouling levels over time based on the operating regime and local meteorological variables. In particular, we study the relationship between degradation and the suspended dust that originates in the Sahara Desert. To this end, we use a computational procedure that relies on a set of artificial neural networks to build an ensemble, using a cross-validated committees approach, to yield the compressor efficiency. The use of trained models makes it possible to know in advance how the local fouling of an industrial rotating component will evolve, which is useful for maintenance planning and for calculating the relative importance of the variables that make up the system

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Published

2021-09-30

How to Cite

Gonzalez-Calvo, D., Aguilar, R., Criado-Hernandez, C., & Gonzalez-Mendoza, L. (2021). Applying ensemble neural networks to analyze industrial maintenance: Influence of Saharan dust transport on gas turbine axial compressor fouling. Inteligencia Artificial, 24(68), 53–71. https://doi.org/10.4114/intartif.vol24iss68pp53-71