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

• Type: Article in International Scientific Journal
• Date: 2005

Authors

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

Journal

Title: IEEE Transactions on Power Delivery

Vol. 20 No. 4

Pages: 2509-2516

ISSN: 0885-8977

Publisher: IEEE

Indexing

ISI Web of Knowledge - 57 Citations

ISI Web of Science

Scopus - 76 Citations

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

CORDIS: Technological sciences > Engineering > Electrical engineering

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8