An Improved Feature Extraction Method for Rolling Bearing Fault Diagnosis Based on MEMD and PE
The health condition of rolling bearing can directly influence to the efficiency and lifecycle of rotating machinery, thus monitoring and diagnosing the faults of rolling bearing is of great importance. Unfortunately, vibration signals of rolling bearing are usually overwhelmed by external noise, so...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Sciendo
2018-08-01
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| Series: | Polish Maritime Research |
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| Online Access: | https://doi.org/10.2478/pomr-2018-0080 |
| _version_ | 1818585073097637888 |
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| author | Zhang Hu Zhao Lei Liu Quan Luo Jingjing Wei Qin Zhou Zude Qu Yongzhi |
| author_facet | Zhang Hu Zhao Lei Liu Quan Luo Jingjing Wei Qin Zhou Zude Qu Yongzhi |
| author_sort | Zhang Hu |
| collection | DOAJ |
| description | The health condition of rolling bearing can directly influence to the efficiency and lifecycle of rotating machinery, thus monitoring and diagnosing the faults of rolling bearing is of great importance. Unfortunately, vibration signals of rolling bearing are usually overwhelmed by external noise, so the fault frequencies of rolling bearing cannot be readily obtained. In this paper, an improved feature extraction method called IMFs_PE, which combines the multivariate empirical mode decomposition with the permutation entropy, is proposed to extract fault frequencies from the noisy bearing vibration signals. First, the raw bearing vibration signals are filtered by an optimal band-pass filter determined by SK to remove the irrelative noise which is not in the same frequency band of fault frequencies. Then the filtered signals are processed by the IMFs_PE to get rid of the relative noise which is in the same frequency band of fault frequencies. Finally, a frequency domain condition indicator FFR(Fault Frequency Ratio), which measures the magnitude of fault frequencies in frequency domain, is calculated to compare the effectiveness of the feature extraction methods. The feature extraction method proposed in this paper has advantages of removing both irrelative noise and relative noise over other feature extraction methods. The effectiveness of the proposed method is validated by simulated and experimental bearing signals. And the results are shown that the proposed method outperforms other state of the art algorithms with regards to fault feature extraction of rolling bearing. |
| first_indexed | 2024-12-16T08:31:16Z |
| format | Article |
| id | doaj.art-dca2331074cb48f7b697bd4a3e860d66 |
| institution | Directory Open Access Journal |
| issn | 2083-7429 |
| language | English |
| last_indexed | 2024-12-16T08:31:16Z |
| publishDate | 2018-08-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Polish Maritime Research |
| spelling | doaj.art-dca2331074cb48f7b697bd4a3e860d662022-12-21T22:37:52ZengSciendoPolish Maritime Research2083-74292018-08-0125s29810610.2478/pomr-2018-0080pomr-2018-0080An Improved Feature Extraction Method for Rolling Bearing Fault Diagnosis Based on MEMD and PEZhang Hu0Zhao Lei1Liu Quan2Luo Jingjing3Wei Qin4Zhou Zude5Qu Yongzhi6School of Information Engineering, Wuhan University of Technology, Wuhan430070, ChinaSchool of Information Engineering, Wuhan University of Technology, Wuhan430070, ChinaSchool of Information Engineering, Wuhan University of Technology, Wuhan430070, ChinaSchool of Information Engineering, Wuhan University of Technology, Wuhan430070, ChinaSchool of Information Engineering, Wuhan University of Technology, Wuhan430070, ChinaSchool of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan430070, ChinaSchool of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan430070, ChinaThe health condition of rolling bearing can directly influence to the efficiency and lifecycle of rotating machinery, thus monitoring and diagnosing the faults of rolling bearing is of great importance. Unfortunately, vibration signals of rolling bearing are usually overwhelmed by external noise, so the fault frequencies of rolling bearing cannot be readily obtained. In this paper, an improved feature extraction method called IMFs_PE, which combines the multivariate empirical mode decomposition with the permutation entropy, is proposed to extract fault frequencies from the noisy bearing vibration signals. First, the raw bearing vibration signals are filtered by an optimal band-pass filter determined by SK to remove the irrelative noise which is not in the same frequency band of fault frequencies. Then the filtered signals are processed by the IMFs_PE to get rid of the relative noise which is in the same frequency band of fault frequencies. Finally, a frequency domain condition indicator FFR(Fault Frequency Ratio), which measures the magnitude of fault frequencies in frequency domain, is calculated to compare the effectiveness of the feature extraction methods. The feature extraction method proposed in this paper has advantages of removing both irrelative noise and relative noise over other feature extraction methods. The effectiveness of the proposed method is validated by simulated and experimental bearing signals. And the results are shown that the proposed method outperforms other state of the art algorithms with regards to fault feature extraction of rolling bearing.https://doi.org/10.2478/pomr-2018-0080improved feature extraction methodrolling bearing fault diagnosismemd and pe |
| spellingShingle | Zhang Hu Zhao Lei Liu Quan Luo Jingjing Wei Qin Zhou Zude Qu Yongzhi An Improved Feature Extraction Method for Rolling Bearing Fault Diagnosis Based on MEMD and PE Polish Maritime Research improved feature extraction method rolling bearing fault diagnosis memd and pe |
| title | An Improved Feature Extraction Method for Rolling Bearing Fault Diagnosis Based on MEMD and PE |
| title_full | An Improved Feature Extraction Method for Rolling Bearing Fault Diagnosis Based on MEMD and PE |
| title_fullStr | An Improved Feature Extraction Method for Rolling Bearing Fault Diagnosis Based on MEMD and PE |
| title_full_unstemmed | An Improved Feature Extraction Method for Rolling Bearing Fault Diagnosis Based on MEMD and PE |
| title_short | An Improved Feature Extraction Method for Rolling Bearing Fault Diagnosis Based on MEMD and PE |
| title_sort | improved feature extraction method for rolling bearing fault diagnosis based on memd and pe |
| topic | improved feature extraction method rolling bearing fault diagnosis memd and pe |
| url | https://doi.org/10.2478/pomr-2018-0080 |
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