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

  1. D. Gonzalez Calvo 1
  2. R.M. Aguilar 1
  3. C. Criado Hernandez 1
  4. Luis Antonio González Mendoza 1
  1. 1 Universidad de La Laguna
    info

    Universidad de La Laguna

    San Cristobal de La Laguna, España

    ROR https://ror.org/01r9z8p25

Journal:
Inteligencia artificial: Revista Iberoamericana de Inteligencia Artificial

ISSN: 1137-3601

Year of publication: 2021

Volume: 24

Issue: 68

Pages: 53-71

Type: Article

DOI: 10.4114/INTARTIF.VOL24ISS68PP53-71 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

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.