In this thesis, a novel approach to detecting and diagnosing comprehensive fault conditions of Induction Motors (IMs) using an Fuzzy Min-Max (FMM) neural network and the Classification and Regression Tree (CART) is proposed. The model, known as FMM-CART, exploits the advantages of both FMM and the CART for undertaking data classification and rule extraction problems. Modifications to FMM and the CART are introduced in order for the resulting model to work efficiently. In order to compare the FMM-CART performance, benchmark data sets from motor bearing faults and from the UCI machine learning repository are used for analysis, with the results discussed and compared with those from other methods
