A Hybrid FMM-CART Model for Fault Detection and Diagnosis of Induction Motors

A new approach to detect and classify fault conditions of induction motors using a hybrid Fuzzy Min-Max (FMM) neural network and the Classification and Regression Tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of experiments using real data measurements of motor currents from healthy and faulty induction motors is conducted. FMM-CART is able to detect and classify the associated inductor motor faults with good accuracy rates. Useful rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors.