Improving the IEC Table for Transformer Failure Diagnosis With Knowledge Extraction From Neural Networks

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 IEC 60599 Table, but also generate a new rule table whose application also leads to better diagnosis results.