Improving the IEC table for transformer failure diagnosis with knowledge extraction from neural networks

• Tipo: Artigo em Revista Científica Internacional
• Data: 2005

Autores

V. Miranda (Autor) FEUP; Adriana Castro (Autor) FEUP

Revista

Título: IEEE Transactions on Power Delivery

Vol. 20 Nº 4

Páginas: 2509-2516

ISSN: 0885-8977

Editora: IEEE

Indexação

ISI Web of Knowledge - 57 Citações

ISI Web of Science

Scopus - 76 Citações

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia electrotécnica, electrónica e informática

CORDIS: Ciências Tecnológicas > Engenharia > Engenharia electrotécnica

Outras Informações

ID Authenticus: P-000-13M

DOI: 10.1109/tpwrd.2005.855423

Abstract (EN): The paper describes how mapping a neural network into a rule-based fuzzy inference system leads to knowledge extraction. This mapping makes explicit the knowledge implicitly captured by the neural network during the learning stage, by transforming it into a set of rules. By applying the method to transformer fault diagnosis using dissolved gas-in-oil analysis, one could not only develop intelligent diagnosis systems, providing better results than the application of the IIEC 60599 Table, but also generate a new rule table whose application also leads to better diagnosis results.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8