title: Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM-CART model.
abstract: In this paper, a novel approach to detect and classify comprehensive fault conditions of induction motors using a hybrid fuzzy min-max (FMM) neural network and 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 real experiments is conducted, whereby the motor current signature analysis method is applied to form a database comprising stator current signatures under different motor conditions. The signal harmonics from the power spectral density are extracted as discriminative input features for fault detection and classification with FMM-CART. A comprehensive list of induction motor fault conditions, viz., broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems, has been successfully classified using FMM-CART with good accuracy rates. The results are comparable, if not better, than those reported in the literature. Useful explanatory rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors.
authors.forename: Manjeevan
authors.initials: M
authors.lastname: Seera
authors.forename: Chee Peng
authors.initials: CP
authors.lastname: Lim
authors.forename: Dahaman
authors.initials: D
authors.lastname: Ishak
authors.forename: Harapajan
authors.initials: H
authors.lastname: Singh