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- W4214684988 abstract "Stator winding inter-turn faults (SITFs) diagnosis has enormously exploited motor current signatures, and eccentricity faults (EFs) detection has significantly investigated vibration and current signals. However, the motor-current signature analysis sometimes may need other diagnostics techniques for indicating the faulty events. The measurement of vibration signals using an accelerometer is an expensive task. The researchers appreciably implemented artificial intelligence (AI) systems for incipient fault detection in induction motors (IMs). State of the art mainly signifies fault detection techniques for IMs based on the statistical parameters as an input frame to the neural networks (NN). However, in this paper, the proposed neural networks are equipped with the twelve real input parameters to meet the desired fault identification and classification. The NN is trained by deliberately creating the SITFs, EFs, and both faults simultaneously. Multilayer perceptron (MLP) and radial basis function (RBF) based NN models are designed and verified for optimal performance in fault detection techniques. The experimental data set of a three-phase, 420 V, 4 pole squirrel cage induction motor (SCIM) is harnessed to develop the proposed NN. The proposed fault classifier has proven to have reduced mean square error (MSE) and better classification accuracy as compared to classifiers with statistical parameters used as an input feature space." @default.
- W4214684988 created "2022-03-02" @default.
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- W4214684988 date "2021-12-19" @default.
- W4214684988 modified "2023-10-17" @default.
- W4214684988 title "Incipient Faults Detection in Induction Motor using MLP-NN and RBF-NN-based Fault Classifier" @default.
- W4214684988 doi "https://doi.org/10.1109/stpec52385.2021.9718693" @default.
- W4214684988 hasPublicationYear "2021" @default.
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