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• W4224255633 startingPage "209" @default.
• W4224255633 abstract "Detection of bearing faults have become crucial in electrical machines, particularly in induction motors. Conventional monitoring procedures using vibration sensors, temperature sensors, etc. are costly and need more tests to estimate the nature of fault. Hence, the current monitoring attracts the concentration of many industries for continuous monitoring. Spectral analysis of stator current to estimate motor faults, FFT analysis, is commonly preferred. But the problems associated with normal FFT analysis will mislead the fault diagnosis. Therefore, advanced spectral methods like wavelet transforms, matrix pencil method, MUSIC algorithm, s-transforms have been proposed. But each technique requires special attention to get good results. On the other hand, faults experienced by the induction motor can be categorized into bearing-related, rotor- and stator-related, and eccentricity. Among these faults, bearing damage accounts for 40-90% and requires additional concentration to estimate." @default.
• W4224255633 created "2022-04-26" @default.
• W4224255633 creator A5004733384 @default.
• W4224255633 creator A5087990722 @default.
• W4224255633 date "2022-03-18" @default.
• W4224255633 modified "2023-09-26" @default.
• W4224255633 title "Progressive Bearing Fault Detection in a Three-Phase Induction Motor Using S-Transform via Pre-Fault Frequency Cancellation" @default.
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• W4224255633 doi "https://doi.org/10.4018/978-1-7998-9426-1.ch011" @default.
• W4224255633 hasPublicationYear "2022" @default.
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