Development of graphical user interface (GUI) for bearing fault detection
Rolling element bearing has vast domestic and the vital parts in any rotating machinery. Appropriate function of these appliances depends on the smooth operation of the bearings. Failure of this particular part can affect the machinery performance and in time will cause major failure to the machiner...
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| Format: | Undergraduates Project Papers |
| Language: | English |
| Published: |
2013
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| Online Access: | http://umpir.ump.edu.my/id/eprint/8668/ http://umpir.ump.edu.my/id/eprint/8668/ http://umpir.ump.edu.my/id/eprint/8668/1/cd8186.pdf |
| Summary: | Rolling element bearing has vast domestic and the vital parts in any rotating machinery. Appropriate function of these appliances depends on the smooth operation of the bearings. Failure of this particular part can affect the machinery performance and in time will cause major failure to the machinery. Result of various studies shows that bearing problems account for over 40% of all machine failures. Due to the crucial problem, online monitoring has become an alternative in preventive maintenance. The objective of this project is to develop software with signal processing tools to detect defect features in mechanical signal of the bearing. Five set of bearing were tested with one of them remains in good condition while the other four has its own type of defects. The data for good bearing were used as baseline data to compare with the defected ones. The data consist with three different speed rotation which are 287, 1466, and 2664 rpm. Then analyzed by using Continuous Wavelet Transform (CWT). From there, it is further analyse using wavelet coefficient for each level of decomposition from CWT method. From the result generated, Fast Fourier Transform (FFT) and wavelet coefficient plays an important role in supporting result analyzed by using CWT that will be used on Graphical User Interface (GUI) software in MATLAB. A system or data with low wavelet coefficient compare to the good condition wavelet coefficient will clearly state as in good condition bearing while the defect features still may be recovered by calculating the wavelet coefficient for each level of decomposition in CWT method. If the wavelet coefficient of data is higher than the good bearing, it proves that the defect occurred on that bearing. The GUI will display the result of the CWT process by displaying condition of the bearing either in good condition or not. Finally, the CWT method also proves to be an effective method for online condition monitoring tool with GUI software. Future research should be detecting type of the defect features based on statistical tool. |
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