Data-driven fault diagnosis is a promising approach for the early detection and isolation of malfunctions in power generation plants deploying solid oxide fuel cells (SOFCs). Despite the supervised classifier used in a data-driven system is trained by samples gathered under one specific design-point operating condition, during real operation the plant can move to a new, unexpected off-design operating condition, reducing the performance of the diagnosis system. This Short Communication demonstrates that this reduction is heavily mitigated if the supervised classifier is adapted to the new condition through the domain adaptation statistical technique. The present study shows that a probability of correct classification between 85% and 94% can be achieved in off-design, when a probability of 95% is obtained at the design-point.

Data-driven fault diagnosis in SOFC-based power plants under off-design operating conditions

Costamagna P.;De Giorgi A.;Moser G.;Trucco A.
2019

Abstract

Data-driven fault diagnosis is a promising approach for the early detection and isolation of malfunctions in power generation plants deploying solid oxide fuel cells (SOFCs). Despite the supervised classifier used in a data-driven system is trained by samples gathered under one specific design-point operating condition, during real operation the plant can move to a new, unexpected off-design operating condition, reducing the performance of the diagnosis system. This Short Communication demonstrates that this reduction is heavily mitigated if the supervised classifier is adapted to the new condition through the domain adaptation statistical technique. The present study shows that a probability of correct classification between 85% and 94% can be achieved in off-design, when a probability of 95% is obtained at the design-point.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/979372
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