Bearing fault detection using discrete wavelet transform
Rolling element bearing has vast domestic and industrial applications. Appropriate function of these appliances depends on the smooth operation of the bearings. Result of various studies shows that bearing problems account for over 40% of all machine failures. Therefore this research is to design a...
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| Format: | Undergraduates Project Papers |
| Language: | English |
| Published: |
2012
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/4504/1/25.Bearing%20fault%20detection%20using%20discrete%20wavelet%20transform.pdf http://umpir.ump.edu.my/id/eprint/4504/ |
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| Summary: | Rolling element bearing has vast domestic and industrial applications. Appropriate function of these appliances depends on the smooth operation of the bearings. Result of various studies shows that bearing problems account for over 40% of all machine failures. Therefore this research is to design a test rig to harness data in terms of types of defects and rotation speed and also to develop method to detect features in vibration signals. Six set of bearings were tested with one of them remains in good condition while the other five has its own type of defects have been considered for analysis by using Discrete Wavelet Transform (DWT). The data for a good bearing were used as benchmark to compare with the defective ones. MATLAB’s Discrete Wavelet Transform ToolBox was used to down-sample the vibration signals into noticeable form to detect defect features under certain frequency with respect to time. From the result generated, Fast Fourier Transform (FFT) and Root Mean Square (RMS) plays an important role in supporting results analyzed by using DWT from MATLAB® Toolbox. A system with low operating speed yields unsystematic results due to low excitation. As the speed increases, the excitation increases thus making DWT works effectively. Fordata of insufficient excitation, defect features still may be discovered by calculating and plotting graph for the percentage of RMS value of each decomposition level compared to the original input. This shows that DWT appears to be effective in pointing out the location and frequency of defect when the excitation is high enough. If the excitation is low, RMS value of each decomposition level may support the result. Nevertheless, DWT also proves to be an effective method for online condition monitoring tool. Future research should be detecting defect features by using envelope analysis or based on statistical tools. |
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