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Progressive Bearing Fault Detection in a Three-Phase Induction Motor Using S-Transform via Pre-Fault Frequency Cancellation

Progressive Bearing Fault Detection in a Three-Phase Induction Motor Using S-Transform via Pre-Fault Frequency Cancellation

Deekshit K. K. C., G. Venu Madhav
Copyright: © 2022 |Pages: 20
ISBN13: 9781799894261|ISBN10: 1799894266|ISBN13 Softcover: 9781799894278|EISBN13: 9781799894285
DOI: 10.4018/978-1-7998-9426-1.ch011
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MLA

K. K. C., Deekshit, and G. Venu Madhav. "Progressive Bearing Fault Detection in a Three-Phase Induction Motor Using S-Transform via Pre-Fault Frequency Cancellation." Advanced Practical Approaches to Web Mining Techniques and Application, edited by Ahmed J. Obaid, et al., IGI Global, 2022, pp. 209-228. https://doi.org/10.4018/978-1-7998-9426-1.ch011

APA

K. K. C., D. & Madhav, G. V. (2022). Progressive Bearing Fault Detection in a Three-Phase Induction Motor Using S-Transform via Pre-Fault Frequency Cancellation. In A. Obaid, Z. Polkowski, & B. Bhushan (Eds.), Advanced Practical Approaches to Web Mining Techniques and Application (pp. 209-228). IGI Global. https://doi.org/10.4018/978-1-7998-9426-1.ch011

Chicago

K. K. C., Deekshit, and G. Venu Madhav. "Progressive Bearing Fault Detection in a Three-Phase Induction Motor Using S-Transform via Pre-Fault Frequency Cancellation." In Advanced Practical Approaches to Web Mining Techniques and Application, edited by Ahmed J. Obaid, Zdzislaw Polkowski, and Bharat Bhushan, 209-228. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-7998-9426-1.ch011

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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.

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