Induction Motor Stator Fault Classification Using PCA-ANFIS Technique

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dc.contributor.authorAbdullateef, A. I
dc.contributor.authorSanusi, M.F
dc.contributor.authorFagbolagun, O.S
dc.date.accessioned2021-06-04T08:13:15Z
dc.date.available2021-06-04T08:13:15Z
dc.date.issued2020
dc.description.abstractInduction motors are used commonly for industrial operations due to their ease of operation coupled with ruggedness and reliability. However, they are subjected to stator faults which result in damage and consequently revenue losses. The classification of stator fault in a three-phase induction motor based on Adaptive neuro-fuzzy inference system (ANFIS) in combination with Principal Component Analysis (PCA) is proposed in this study. A burnt motor was redesigned and rewound while data acquisition was developed to acquire the current and vibration data needed for the fault classification. The data feature extraction for the fault classification was carried out by PCA while backpropagation and the least-squares algorithms were used for the training of the data. Three principal components, which severs as input for the ANFIS, were used to represent the entire data. The ANFIS was tested under four different paradigms, while the membership function type and epoch number were changed at each instant. The ANFIS model based on the triangular membership function and 10 epoch number was found appropriate and used, bringing the accuracy of the model to over 99% with the lowest ANFIS training RMSE error of 1.1795e-6. The ANFIS validation results of the fault classification show that the results are accurate, indicating that the PCAANFIS technique is applicable in fault diagnosis and classification of stator faults in induction motors.en_US
dc.identifier.citationAbullateef, A. I., Sanusi, M. F., & Fagbolagun, O. S. (2020): Induction Motor Stator Fault Classification Using PCA-ANFIS Technique. ELEKTRIKA-Journal of Electrical Engineering, 19(1), 26-32, Published by Universiti Teknologi Malaysia. Available online at https://elektrika.utm.my/index.php/ELEKTRIKA_Journal/issue/view/13/showTocen_US
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/20.500.12484/5882
dc.publisherELEKTRIKA-Journal of Electrical Engineering,en_US
dc.subjectStator faulten_US
dc.subjectthree-phase induction motoren_US
dc.subjectClassificationen_US
dc.subjectAdaptive Neuro-fuzzy Inference Systemen_US
dc.subjectPrincipal Componenten_US
dc.titleInduction Motor Stator Fault Classification Using PCA-ANFIS Techniqueen_US
dc.typeArticleen_US

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