A wavelet decomposition analysis of vibration signal for bearing fault detection
This paper presents a study of vibrational signal analysis for bearing fault detection using Discrete Wavelet Transform (DWT). In this study, the vibration data was acquired from three different types of bearing defect i.e. corroded, outer race defect and point defect. The experiments were carried o...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
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IOP Publishing
2013
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/32216/1/Nizwan_2013_IOP_Conf._Ser.__Mater._Sci._Eng._50_012026.pdf |
| _version_ | 1825814002246090752 |
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| author | Che Ku Eddy Nizwan, Che Ku Husin Ong, S. A Mohd Fadhlan, Mohd Yusof Mohamad Zairi, Baharom |
| author_facet | Che Ku Eddy Nizwan, Che Ku Husin Ong, S. A Mohd Fadhlan, Mohd Yusof Mohamad Zairi, Baharom |
| author_sort | Che Ku Eddy Nizwan, Che Ku Husin |
| collection | UMP |
| description | This paper presents a study of vibrational signal analysis for bearing fault detection using Discrete Wavelet Transform (DWT). In this study, the vibration data was acquired from three different types of bearing defect i.e. corroded, outer race defect and point defect. The experiments were carried out at three different speeds which are 10%, 50% and 90% of the maximum motor speed. The time domain vibration data measured from accelerometer was then transformed into frequency domain using a frequency analyzer in order to study the frequency characteristics of the signal. The DWT was utilized to decomposed signal at different frequency scale. Then, root mean square (RMS) for every decomposition level was calculated to detect the defect features in vibration signals by referring to the trend of vibrational energy retention at every decomposition. Based on the result, the defective bearings show significant deviation in retaining RMS value after a few levels of decomposition. The findings indicate that Wavelet decomposition analysis can be used to develop an effective bearing condition monitoring tool. This signal processing analysis is recommended in on-line monitoring while the machine is on operation. |
| first_indexed | 2024-03-06T12:52:21Z |
| format | Conference or Workshop Item |
| id | UMPir32216 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:52:21Z |
| publishDate | 2013 |
| publisher | IOP Publishing |
| record_format | dspace |
| spelling | UMPir322162021-11-11T08:36:00Z http://umpir.ump.edu.my/id/eprint/32216/ A wavelet decomposition analysis of vibration signal for bearing fault detection Che Ku Eddy Nizwan, Che Ku Husin Ong, S. A Mohd Fadhlan, Mohd Yusof Mohamad Zairi, Baharom TJ Mechanical engineering and machinery This paper presents a study of vibrational signal analysis for bearing fault detection using Discrete Wavelet Transform (DWT). In this study, the vibration data was acquired from three different types of bearing defect i.e. corroded, outer race defect and point defect. The experiments were carried out at three different speeds which are 10%, 50% and 90% of the maximum motor speed. The time domain vibration data measured from accelerometer was then transformed into frequency domain using a frequency analyzer in order to study the frequency characteristics of the signal. The DWT was utilized to decomposed signal at different frequency scale. Then, root mean square (RMS) for every decomposition level was calculated to detect the defect features in vibration signals by referring to the trend of vibrational energy retention at every decomposition. Based on the result, the defective bearings show significant deviation in retaining RMS value after a few levels of decomposition. The findings indicate that Wavelet decomposition analysis can be used to develop an effective bearing condition monitoring tool. This signal processing analysis is recommended in on-line monitoring while the machine is on operation. IOP Publishing 2013 Conference or Workshop Item PeerReviewed pdf en cc_by http://umpir.ump.edu.my/id/eprint/32216/1/Nizwan_2013_IOP_Conf._Ser.__Mater._Sci._Eng._50_012026.pdf Che Ku Eddy Nizwan, Che Ku Husin and Ong, S. A and Mohd Fadhlan, Mohd Yusof and Mohamad Zairi, Baharom (2013) A wavelet decomposition analysis of vibration signal for bearing fault detection. In: IOP Conference Series: Materials Science and Engineering, 2nd International Conference on Mechanical Engineering Research (ICMER 2013) , 1-3 July 2013 , Bukit Gambang Resort, Kuantan, Pahang, Malaysia. pp. 1-9., 50 (012026). ISSN 1757-8981 (Published) https://doi.org/10.1088/1757-899X/50/1/012026 012026 |
| spellingShingle | TJ Mechanical engineering and machinery Che Ku Eddy Nizwan, Che Ku Husin Ong, S. A Mohd Fadhlan, Mohd Yusof Mohamad Zairi, Baharom A wavelet decomposition analysis of vibration signal for bearing fault detection |
| title | A wavelet decomposition analysis of vibration signal for bearing fault detection |
| title_full | A wavelet decomposition analysis of vibration signal for bearing fault detection |
| title_fullStr | A wavelet decomposition analysis of vibration signal for bearing fault detection |
| title_full_unstemmed | A wavelet decomposition analysis of vibration signal for bearing fault detection |
| title_short | A wavelet decomposition analysis of vibration signal for bearing fault detection |
| title_sort | wavelet decomposition analysis of vibration signal for bearing fault detection |
| topic | TJ Mechanical engineering and machinery |
| url | http://umpir.ump.edu.my/id/eprint/32216/1/Nizwan_2013_IOP_Conf._Ser.__Mater._Sci._Eng._50_012026.pdf |
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