Signal Processing Methods for Identification of Induction Motor Bearing Fault
Sudhir Agrawal1, A. N. Tiwari2, V. K. Giri3
1Sudhir Agrawal*, Research Scholar, Dept. of EE, Madan Mohan Malaviya University of Technology, Gorakhpur, India.
2A. N. Tiwari, Professor, Dept. of EE, Madan Mohan Malaviya University of Technology, Gorakhpur, India.
3V. K. Giri, Director, Rajkiya Engineering College, Sonbhdra, India.
Manuscript received on 15 August 2019. | Revised Manuscript received on 25 August 2019. | Manuscript published on 30 September 2019. | PP: 143-151 | Volume-8 Issue-3 September 2019 | Retrieval Number: C3911098319/19©BEIESP| DOI: 10.35940/ijrte.C3911.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: To diagnose early faults as soon as possible, the feature extraction of vibration signals is very important in real engineering applications. Recently, the advanced signal processing-based weak feature extraction method has been becoming a hot research topic. The dominant mode of failure in rolling element bearings is spalling of the races or the rolling elements. Localized defects generate a series of impact vibrations every time whenever running roller passes over the surface of a defect. Therefore, vibration analysis is a conventional method for bearing fault detection. However, the measured vibration signals of rotating machinery often present nonlinear and non-stationary characteristics. This paper deals with the diagnosis of induction motor bearing based on vibration signal analysis. It provides a comparative study between traditional signal processing methods, such as Power Spectrum, Short Time Fourier Transform, Wavelet Transform, and Hilbert Transform. Performances of these techniques are assessed on real vibration data and compared for healthy and faulty bearing.
Keywords : FFT, STFT, WT, Hilbert Transform, TKEO.
Scope of the Article: Digital Signal Processing Theory