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Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM-CART model
journal contributionposted on 2012-01-02, 00:00 authored by M Seera, Chee Peng LimChee Peng Lim, D Ishak, H Singh
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.
JournalIEEE transactions on neural networks and learning systems
Pagination97 - 108
LocationPiscataway, N. J.
Publication classificationC1.1 Refereed article in a scholarly journal
Copyright notice2011, IEEE
CategoriesNo categories selected
classification and regression treefault detection and diagnosisfuzzy min–max neural networkinduction motormotor current signature analysisScience & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, Hardware & ArchitectureComputer Science, Theory & MethodsEngineering, Electrical & ElectronicComputer ScienceEngineeringfuzzy min-max neural networkMAX NEURAL-NETWORKBROKEN ROTOR BARAIRGAP ECCENTRICITYPATTERN-RECOGNITIONSTATOR WINDINGSNUMERICAL DATASHORT CIRCUITSMACHINESEXTRACTIONSYSTEM