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• W2903629404 abstract "This paper presents multiple fault diagnosis and detection using artificial neural feed forward network. In this work analysis is done on induction motor, as these motor are widely used in industries because of their robustness, easy maintenance etc. The current and vibration responses of healthy motor, motor with bearing, rotor and stator defects are analysed. The feature extraction process is done in time domain only. From the results it is cleared that among various transfer functions in ANN the trainlm performs best and traingdm performs worst for fault detection." @default.
• W2903629404 created "2018-12-22" @default.
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• W2903629404 date "2018-12-12" @default.
• W2903629404 modified "2023-10-05" @default.
• W2903629404 title "Multiple Faults Diagnosis of Induction Motor Using Artificial Neural Network" @default.
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• W2903629404 doi "https://doi.org/10.1007/978-981-13-3140-4_63" @default.
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