RT info:eu-repo/semantics/article
T1 Applying ensemble neural networks to analyze industrial maintenance: Influence of Saharan dust transport on gas turbine axial compressor fouling.
A1 González Mendoza, Luis Antonio
A1 González Calvo, Daniel
A1 Aguilar Chinea, Rosa María
A1 Criado Hernández, C.
K1 Gas Turbine
K1 Compressor Fouling
K1 Neural Networks Ensemble
K1 Saharan dust
K1 Canary Islands
AB 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.
SN 1137-3601
YR 2021
FD 2021
LK http://riull.ull.es/xmlui/handle/915/34841
UL http://riull.ull.es/xmlui/handle/915/34841
LA en
DS Repositorio institucional de la Universidad de La Laguna
RD 15-oct-2024