Knowledge extraction from artificial neural networks: Application to transformer incipient fault diagnosis

Artificial Neural Networks (ANNs) represent an excellent tool that has been used to develop highly accurate models in numerous real-world problem domains. However, despite the proven advantages of ANNs, the notorious difficulty to understand how they arrive at a particular decision has led to a barrier to a more widespread acceptance of them, especially in some industry domains.;Following the line of rule extraction investigation, this thesis presents a new methodology for fuzzy rute extraction from ANN, with fu ll theoretical support and practical validation. The methodology, called Transparent Fuzzy Rule Extraction from Neural Networks (TFRENN), was developed with the goal of overcoming the main limitation of some previous methodologies, i.e. the extraction of transparent fuzzy rules. The importance of obtaining a transparent rule set must be underlined, because it allows fu ll understanding by humans of the hidden knowledge captured by ANNs when trained to fit data in a given problem.;The efficiency of TFRENN methodology and the importance of transparent fuzzy systems for knowledge discovery are verified by the application of the methodology in Transformer incipient Fault Diagnosis using DGA (Dissolved Gas-in-oil Analysis). Many diagnosis systems based on ANN have been developed and presented in the literature with good results. However, these systems have bad difficulty to be accepted by utilities, perhaps due to the lack of ANN behavior explanation. The results presented in this thesis shown that the application of TFRENN methodology to Transformer lncipient Fault Diagnosis can overcome this limitation.;The TFRENN approach allowed producing a very good tool for fault diagnosis, with results better than the ones published for comparable techniques. Furthermore, it allowed the discovery of new rules for classifying faults, which led to building a new diagnosis table useful for practical purposes. This new table is an improvement to the diagnosis table published by LEC, which is actually one of the tables most used to transformer incipient fau lt diagnosis.;In recent years, some works have been developed with the aim of redressing this general problem of lack of ANN explanation capability. In particular, a substantial part of these works have focused on a line of investigation involving the development of techniques for extraction of the hidden knowledge in ANNs. This body of work is actually referred to as rule extraction and this name reflects the fact that these works have been largely concentrated on translating ANN hypotheses into inference-rule languages.