Fault Frequency Identification of Rolling Bearing Using Reinforced Ensemble Local Mean Decomposition
The vibration signal of rolling bearing exhibits the characteristics of energy attenuation and complex time-varying modulation caused by the transmission with multiple interfaces and complex paths. In view of this, strong ambient noise easily masks faulty signs of rolling bearings, resulting in inac...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/2744193 |
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| author | Bo Qin Quanyi Luo Juanjuan Zhang Zixian Li Yan Qin |
| author_facet | Bo Qin Quanyi Luo Juanjuan Zhang Zixian Li Yan Qin |
| author_sort | Bo Qin |
| collection | DOAJ |
| description | The vibration signal of rolling bearing exhibits the characteristics of energy attenuation and complex time-varying modulation caused by the transmission with multiple interfaces and complex paths. In view of this, strong ambient noise easily masks faulty signs of rolling bearings, resulting in inaccurate identification or even totally missing the real fault frequencies. To overcome this problem, we propose a reinforced ensemble local mean decomposition method to capture and screen the essential faulty frequencies of rolling bearing, further boosting fault diagnosis accuracy. Firstly, the vibration signal is decomposed into a series of preliminary features through ensemble local mean decomposition, and then the frequency components above the average level are energy-enhanced. In this way, principal frequency components related to rolling bearing failure can be identified with the fast spectral kurtosis algorithm. Finally, the efficacy of the proposed approach is verified through both a benchmark case and a practical platform. The results show that the selected fault characteristic components are accurate, and the identification and diagnosis of rolling bearing status are improved. Especially for the signals with strong noise, the proposed method still could accurately diagnose fault frequency. |
| format | Article |
| id | doaj-art-588b029465ca432b94403a17fd70e7df |
| institution | Kabale University |
| issn | 1687-5257 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-588b029465ca432b94403a17fd70e7df2025-02-03T05:58:23ZengWileyJournal of Control Science and Engineering1687-52572021-01-01202110.1155/2021/2744193Fault Frequency Identification of Rolling Bearing Using Reinforced Ensemble Local Mean DecompositionBo Qin0Quanyi Luo1Juanjuan Zhang2Zixian Li3Yan Qin4School of Mechanical EngineeringSchool of Mechanical EngineeringBaotou Vocational and Technical CollegeSchool of Mechanical EngineeringSchool of Electrical and Electronic EngineeringThe vibration signal of rolling bearing exhibits the characteristics of energy attenuation and complex time-varying modulation caused by the transmission with multiple interfaces and complex paths. In view of this, strong ambient noise easily masks faulty signs of rolling bearings, resulting in inaccurate identification or even totally missing the real fault frequencies. To overcome this problem, we propose a reinforced ensemble local mean decomposition method to capture and screen the essential faulty frequencies of rolling bearing, further boosting fault diagnosis accuracy. Firstly, the vibration signal is decomposed into a series of preliminary features through ensemble local mean decomposition, and then the frequency components above the average level are energy-enhanced. In this way, principal frequency components related to rolling bearing failure can be identified with the fast spectral kurtosis algorithm. Finally, the efficacy of the proposed approach is verified through both a benchmark case and a practical platform. The results show that the selected fault characteristic components are accurate, and the identification and diagnosis of rolling bearing status are improved. Especially for the signals with strong noise, the proposed method still could accurately diagnose fault frequency.http://dx.doi.org/10.1155/2021/2744193 |
| spellingShingle | Bo Qin Quanyi Luo Juanjuan Zhang Zixian Li Yan Qin Fault Frequency Identification of Rolling Bearing Using Reinforced Ensemble Local Mean Decomposition Journal of Control Science and Engineering |
| title | Fault Frequency Identification of Rolling Bearing Using Reinforced Ensemble Local Mean Decomposition |
| title_full | Fault Frequency Identification of Rolling Bearing Using Reinforced Ensemble Local Mean Decomposition |
| title_fullStr | Fault Frequency Identification of Rolling Bearing Using Reinforced Ensemble Local Mean Decomposition |
| title_full_unstemmed | Fault Frequency Identification of Rolling Bearing Using Reinforced Ensemble Local Mean Decomposition |
| title_short | Fault Frequency Identification of Rolling Bearing Using Reinforced Ensemble Local Mean Decomposition |
| title_sort | fault frequency identification of rolling bearing using reinforced ensemble local mean decomposition |
| url | http://dx.doi.org/10.1155/2021/2744193 |
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