Fault Diagnosis of Rolling Element Bearing Using an Adaptive Multiscale Enhanced Combination Gradient Morphological Filter
The extraction of the vibration impulse signal plays a crucial role in the fault diagnosis of rolling element bearing. However, the detection of weak fault signals generally suffers the strong background noise. To solve this problem, a new adaptive multiscale enhanced combination gradient morphologi...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2019/2059631 |
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| _version_ | 1832561985757118464 |
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| author | Yuanqing Luo Changzheng Chen Shuang Kang Pinyang Zhang |
| author_facet | Yuanqing Luo Changzheng Chen Shuang Kang Pinyang Zhang |
| author_sort | Yuanqing Luo |
| collection | DOAJ |
| description | The extraction of the vibration impulse signal plays a crucial role in the fault diagnosis of rolling element bearing. However, the detection of weak fault signals generally suffers the strong background noise. To solve this problem, a new adaptive multiscale enhanced combination gradient morphological filter (MECGMF) is proposed for the fault diagnosis of rolling element bearing. In this method, according to the filtering ability of four basic morphological filter operators, an enhanced combination gradient morphological operation (ECGMF) is first proposed. This design enhances the ability of MECGMF to extract impulse signals from strong background noise. And accordingly, a new adaptive selection strategy named kurtosis fault feature ratio (KFFR) is proposed to select an optimal structuring element (SE) scale. Subsequently, the optimal SE scale is the largest measure of multiscale morphological filtering for extracting bearing fault information. In the meanwhile, the effectiveness of the proposed method is verified by simulation and experiment. Finally, the experimental results demonstrate that MECGMF can effectively restrain the noise interference and extract fault characteristic signals of rolling element bearing from strong background noise. Moreover, comparative tests show that the proposed method is more effective in detecting wind turbine bearing failures. |
| format | Article |
| id | doaj-art-819c29b2026c4856ae4e362cdc69ee50 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-819c29b2026c4856ae4e362cdc69ee502025-02-03T01:23:44ZengWileyShock and Vibration1070-96221875-92032019-01-01201910.1155/2019/20596312059631Fault Diagnosis of Rolling Element Bearing Using an Adaptive Multiscale Enhanced Combination Gradient Morphological FilterYuanqing Luo0Changzheng Chen1Shuang Kang2Pinyang Zhang3School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, ChinaSchool of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, ChinaSchool of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, ChinaSchool of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, ChinaThe extraction of the vibration impulse signal plays a crucial role in the fault diagnosis of rolling element bearing. However, the detection of weak fault signals generally suffers the strong background noise. To solve this problem, a new adaptive multiscale enhanced combination gradient morphological filter (MECGMF) is proposed for the fault diagnosis of rolling element bearing. In this method, according to the filtering ability of four basic morphological filter operators, an enhanced combination gradient morphological operation (ECGMF) is first proposed. This design enhances the ability of MECGMF to extract impulse signals from strong background noise. And accordingly, a new adaptive selection strategy named kurtosis fault feature ratio (KFFR) is proposed to select an optimal structuring element (SE) scale. Subsequently, the optimal SE scale is the largest measure of multiscale morphological filtering for extracting bearing fault information. In the meanwhile, the effectiveness of the proposed method is verified by simulation and experiment. Finally, the experimental results demonstrate that MECGMF can effectively restrain the noise interference and extract fault characteristic signals of rolling element bearing from strong background noise. Moreover, comparative tests show that the proposed method is more effective in detecting wind turbine bearing failures.http://dx.doi.org/10.1155/2019/2059631 |
| spellingShingle | Yuanqing Luo Changzheng Chen Shuang Kang Pinyang Zhang Fault Diagnosis of Rolling Element Bearing Using an Adaptive Multiscale Enhanced Combination Gradient Morphological Filter Shock and Vibration |
| title | Fault Diagnosis of Rolling Element Bearing Using an Adaptive Multiscale Enhanced Combination Gradient Morphological Filter |
| title_full | Fault Diagnosis of Rolling Element Bearing Using an Adaptive Multiscale Enhanced Combination Gradient Morphological Filter |
| title_fullStr | Fault Diagnosis of Rolling Element Bearing Using an Adaptive Multiscale Enhanced Combination Gradient Morphological Filter |
| title_full_unstemmed | Fault Diagnosis of Rolling Element Bearing Using an Adaptive Multiscale Enhanced Combination Gradient Morphological Filter |
| title_short | Fault Diagnosis of Rolling Element Bearing Using an Adaptive Multiscale Enhanced Combination Gradient Morphological Filter |
| title_sort | fault diagnosis of rolling element bearing using an adaptive multiscale enhanced combination gradient morphological filter |
| url | http://dx.doi.org/10.1155/2019/2059631 |
| work_keys_str_mv | AT yuanqingluo faultdiagnosisofrollingelementbearingusinganadaptivemultiscaleenhancedcombinationgradientmorphologicalfilter AT changzhengchen faultdiagnosisofrollingelementbearingusinganadaptivemultiscaleenhancedcombinationgradientmorphologicalfilter AT shuangkang faultdiagnosisofrollingelementbearingusinganadaptivemultiscaleenhancedcombinationgradientmorphologicalfilter AT pinyangzhang faultdiagnosisofrollingelementbearingusinganadaptivemultiscaleenhancedcombinationgradientmorphologicalfilter |