| 000 | 02561ntm a2200253 a 4500 | ||
|---|---|---|---|
| 001 | vtls000077780 | ||
| 003 | KUKTEM | ||
| 005 | 20251114204602.0 | ||
| 008 | 140425t2012 my da f m 000 0 eng d | ||
| 020 | _aTHE0006479(Local) | ||
| 039 | 9 |
_a201905141207 _bamirul _y201404251123 _zFida |
|
| 040 | _aUMP | ||
| 090 | _aTJ1071 .Q29 2012 rs Bc. | ||
| 100 | 0 | _aMohd Qayyuum Mohd Ridzuan | |
| 245 | 1 | 0 |
_aBearing fault detection using vibration responds : _ba statistical based analysis / _cMohd Qayyuum Mohd Ridzuan |
| 260 |
_aKuantan, Pahang : _bUMP, _c2012 |
||
| 300 |
_axix, 98 p. : _bill. (some col.) ; _c30 cm. + _e1 CD-ROM |
||
| 502 | _aProject paper (Bachelor of Mechanical Engineering) -- Universiti Malaysia Pahang - 2012 | ||
| 504 | _aBibliography : 71-73 | ||
| 520 | 3 | _aBearing is one of the vital parts in any rotating machinery. Failure of this particular part can affect the machinery performance and in time will cause major failure to the machinery. Due to this crucial problem, on-line monitoring has become an alternative in prevention maintenance. The objective of this project is to analyze the vibration signal of both good bearing and defect bearing gain from an accelerometer. A set of good condition bearing is used along five types of defective bearing which are outer race defect, inner race defect, corroded defect, contaminated defect and lastly point defect. The data is acquired using a Bruel & Kjaer accelerometer and a National Instrument Data Acquisition System (NiDAQ). The bearing will be run on three speed rotation which is 287, 1466 and 2664 RPM. The data is then analyzed using DASYLab software. From there it is further analyzed using global statistical parameters of Variance, Standard Deviation, Kurtosis, Skewness, Root Mean Square and also Crest Factor. The data then plotted on statistical distribution to obtain a trend where, the value of this trend is used in Principal Component Analysis (PCA) to form a scatter plot of the data. Where all types of bearing will remain in their scatter area itself. Then it will be clustered using Agglomerative Hierarchical Clustering where a dendrogram is used to show a cluster of data in which the respective data for all types of bearing tested remain in their cluster. Finally, this method is suggested as an alternative in bearing fault detection, especially online monitoring. | |
| 650 | 0 |
_aBearings (Machinery) _xVibration _xTesting |
|
| 650 | 0 |
_aMachine parts _xFailures |
|
| 999 |
_aVIRTUA40 _c4589 _d4595 |
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| 999 | _aVTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5020*5040*5200*6500*6501*9992 | ||