Application in Feature Extraction of AE Signal for Rolling Bearing in EEMD and Cloud Similarity Measurement
Due to the powerful ability of EEMD algorithm in noising, it is usually applied to feature extraction of fault signal of rolling bearing. But the selective correctness of sensitive IMF after decomposition can directly influence the correctness of feature extraction of fault signal. In order to solve...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2015/752078 |
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| author | Long Han Chengwei Li Liqun Shen |
| author_facet | Long Han Chengwei Li Liqun Shen |
| author_sort | Long Han |
| collection | DOAJ |
| description | Due to the powerful ability of EEMD algorithm in noising, it is usually applied to feature extraction of fault signal of rolling bearing. But the selective correctness of sensitive IMF after decomposition can directly influence the correctness of feature extraction of fault signal. In order to solve the problem, the paper firstly proposes a new method on selecting sensitive IMF based on Cloud Similarity Measurement. By comparing this method in simulation experiment with the traditional mutual information method, it is obvious that the proposed method has overcome the misjudgment in the traditional method and it has higher accuracy, by factually collecting the normal, damage, and fracture fault AE signal of the inner ring of rolling bearing as samples, which will be decomposed by EEMD algorithm in the experiments. It uses Cloud Similarity Measurement to select sensitive IMF which can reflect the fault features. Finally, it sets the Multivariate Multiscale Entropy (MME) of sensitive IMF as the eigenvalue of original signal; then it is classified by the SVM to determine the fault types exactly. The results of the experiments show that the selected sensitive IMF based on Cloud Similarity Measurement is effective; it can help to improve the accuracy of the fault diagnosis and feature extraction. |
| format | Article |
| id | doaj-art-6c119ed64afc47418ae2a6a1424d3af3 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-6c119ed64afc47418ae2a6a1424d3af32025-02-03T01:23:00ZengWileyShock and Vibration1070-96221875-92032015-01-01201510.1155/2015/752078752078Application in Feature Extraction of AE Signal for Rolling Bearing in EEMD and Cloud Similarity MeasurementLong Han0Chengwei Li1Liqun Shen2School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, ChinaDue to the powerful ability of EEMD algorithm in noising, it is usually applied to feature extraction of fault signal of rolling bearing. But the selective correctness of sensitive IMF after decomposition can directly influence the correctness of feature extraction of fault signal. In order to solve the problem, the paper firstly proposes a new method on selecting sensitive IMF based on Cloud Similarity Measurement. By comparing this method in simulation experiment with the traditional mutual information method, it is obvious that the proposed method has overcome the misjudgment in the traditional method and it has higher accuracy, by factually collecting the normal, damage, and fracture fault AE signal of the inner ring of rolling bearing as samples, which will be decomposed by EEMD algorithm in the experiments. It uses Cloud Similarity Measurement to select sensitive IMF which can reflect the fault features. Finally, it sets the Multivariate Multiscale Entropy (MME) of sensitive IMF as the eigenvalue of original signal; then it is classified by the SVM to determine the fault types exactly. The results of the experiments show that the selected sensitive IMF based on Cloud Similarity Measurement is effective; it can help to improve the accuracy of the fault diagnosis and feature extraction.http://dx.doi.org/10.1155/2015/752078 |
| spellingShingle | Long Han Chengwei Li Liqun Shen Application in Feature Extraction of AE Signal for Rolling Bearing in EEMD and Cloud Similarity Measurement Shock and Vibration |
| title | Application in Feature Extraction of AE Signal for Rolling Bearing in EEMD and Cloud Similarity Measurement |
| title_full | Application in Feature Extraction of AE Signal for Rolling Bearing in EEMD and Cloud Similarity Measurement |
| title_fullStr | Application in Feature Extraction of AE Signal for Rolling Bearing in EEMD and Cloud Similarity Measurement |
| title_full_unstemmed | Application in Feature Extraction of AE Signal for Rolling Bearing in EEMD and Cloud Similarity Measurement |
| title_short | Application in Feature Extraction of AE Signal for Rolling Bearing in EEMD and Cloud Similarity Measurement |
| title_sort | application in feature extraction of ae signal for rolling bearing in eemd and cloud similarity measurement |
| url | http://dx.doi.org/10.1155/2015/752078 |
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