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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
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Author(s): Deekshit K. K. C. (Sreenidhi Institute of Science and Technology, India) and G. Venu Madhav (Anurag University, India)
Copyright: 2022
Pages: 20
Source title: Advanced Practical Approaches to Web Mining Techniques and Application
Source Author(s)/Editor(s): Ahmed J. Obaid (University of Kufa, Iraq), Zdzislaw Polkowski (Wroclaw University of Economics, Poland) and Bharat Bhushan (Sharda University, India)
DOI: 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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