Mohd Fadhlan, Mohd Yusof and C. K. E., Nizwan and S. A., Ong and M. Q. M., Ridzuan (2016) Clustering of Frequency Spectrum from Different Bearing Fault using Principle Component Analysis. In: 2nd International Conference on Automotive Innovation and Green Energy Vehicle (AIGEV 2016) , 2-3 August 2016 , Malaysia Automotive Institute, Cyberjaya, Selangor. pp. 1-10.. (Unpublished)
![[img]](http://umpir.ump.edu.my/style/images/fileicons/application_pdf.png) |
PDF
Clustering of Frequency Spectrum from Different Bearing Fault using Principle Component Analysis..pdf Restricted to Repository staff only Download (414kB) | Request a copy |
|
|
PDF
fkm-2016-M.F.M.Yusof-Clustering of Frequency.pdf Download (88kB) | Preview |
Abstract
In this paper, the application of principle component analysis (PCA) as a pre-processing method for hierarchical clustering analysis on the frequency spectrum of the vibration signal was proposed. To achieve the aim, the vibration signal was acquired from the operating bearings with different condition and speed. In the next stage, the principle component analysis was applied to the frequency spectrums of the acquired signals for pattern recognition purpose. Meanwhile the mahalanobis distance model was used to cluster the result from PCA. According to the results, it was found that the amplitude of vibration at Ball Passing Frequency Outer Race and Ball Passing Frequency Inner Race will increase in align with the presence of outer race defect and inner race defect respectively. Moreover, the overall amplitude of vibration spectrum was found to be uniformly increased for the case of corroded bearing due to the widespread uniform corrosion on the entire bearing. By applying principle component analysis, the change in amplitude at any of these fundamental frequencies can be detected. Meanwhile, the application of mahalanobis distance was found to be suitable for clustering the results from principle component analysis. Uniquely, it was discovered that the spectrums from healthy and inner race defect bearing can be clearly distinguished from each other even though the change in amplitude pattern for inner race defect frequency spectrum was too small compared to the healthy one. In this work, it was demonstrated that the use of principle component analysis could sensitively detect the change in the pattern of the frequency spectrums. Likewise, the implementation of mahalanobis distance model for clustering purpose was found to be significant for bearing defect identification.
| Item Type: | Conference or Workshop Item (Speech) |
|---|---|
| Uncontrolled Keywords: | Rolling Element Bearings, Vibration, Principle Component Analysis |
| Subjects: | T Technology > TJ Mechanical engineering and machinery |
| Faculty/Division: | Faculty of Mechanical Engineering |
| Depositing User: | Mrs. Neng Sury Sulaiman |
| Date Deposited: | 20 Sep 2016 06:30 |
| Last Modified: | 23 Mar 2017 02:10 |
| URI: | http://umpir.ump.edu.my/id/eprint/14337 |
| Download Statistic: | View Download Statistics |
Actions (login required)
 |
View Item |