Weak Fault Feature Extraction for Rolling Element Bearing Based on a Two-Stage Method
Timely and effective feature extraction is the key for fault diagnosis of rolling element bearing (REB). However, fault feature extraction will become very difficult in the early weak fault stage of REB due to the interference of strong background noise. To solve the above difficulty, a two-stage fe...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | http://dx.doi.org/10.1155/2023/6671730 |
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| _version_ | 1849470955507679232 |
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| author | LianHui Jia LiJie Jiang YongLiang Wen Hongchao Wang |
| author_facet | LianHui Jia LiJie Jiang YongLiang Wen Hongchao Wang |
| author_sort | LianHui Jia |
| collection | DOAJ |
| description | Timely and effective feature extraction is the key for fault diagnosis of rolling element bearing (REB). However, fault feature extraction will become very difficult in the early weak fault stage of REB due to the interference of strong background noise. To solve the above difficulty, a two-stage feature extraction method for early weak fault of REB is proposed, which mainly combines feature mode decomposition (FMD) with a blind deconvolution (BD) method. Firstly, based on the impulsiveness and cyclostationary characteristics of the vibration signal of faulty REB, FMD is used to decompose the complex original vibration signal into several modes containing single component. Subsequently, the sparse index (SI) is calculated for each mode, and the mode containing sensitive fault feature is selected for further analysis. Subsequently, apply the deconvolution method on the selected mode for further enhancing the impulsive characteristic. At last, traditional envelope spectrum (ES) analysis is applied on the filtered signal, and satisfactory fault features are extracted. Effectiveness and advantages of the proposed method are verified through experimental and engineering signals of REBs. |
| format | Article |
| id | doaj-art-e5f27102a08e472997dab2f75cf40344 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-e5f27102a08e472997dab2f75cf403442025-08-20T03:24:59ZengWileyInternational Journal of Distributed Sensor Networks1550-14772023-01-01202310.1155/2023/6671730Weak Fault Feature Extraction for Rolling Element Bearing Based on a Two-Stage MethodLianHui Jia0LiJie Jiang1YongLiang Wen2Hongchao Wang3School of Mechanical Science and EngineeringChina Railway Engineering Equipment Group Co.China Railway Engineering Equipment Group Co.Mechanical and Electrical Engineering InstituteTimely and effective feature extraction is the key for fault diagnosis of rolling element bearing (REB). However, fault feature extraction will become very difficult in the early weak fault stage of REB due to the interference of strong background noise. To solve the above difficulty, a two-stage feature extraction method for early weak fault of REB is proposed, which mainly combines feature mode decomposition (FMD) with a blind deconvolution (BD) method. Firstly, based on the impulsiveness and cyclostationary characteristics of the vibration signal of faulty REB, FMD is used to decompose the complex original vibration signal into several modes containing single component. Subsequently, the sparse index (SI) is calculated for each mode, and the mode containing sensitive fault feature is selected for further analysis. Subsequently, apply the deconvolution method on the selected mode for further enhancing the impulsive characteristic. At last, traditional envelope spectrum (ES) analysis is applied on the filtered signal, and satisfactory fault features are extracted. Effectiveness and advantages of the proposed method are verified through experimental and engineering signals of REBs.http://dx.doi.org/10.1155/2023/6671730 |
| spellingShingle | LianHui Jia LiJie Jiang YongLiang Wen Hongchao Wang Weak Fault Feature Extraction for Rolling Element Bearing Based on a Two-Stage Method International Journal of Distributed Sensor Networks |
| title | Weak Fault Feature Extraction for Rolling Element Bearing Based on a Two-Stage Method |
| title_full | Weak Fault Feature Extraction for Rolling Element Bearing Based on a Two-Stage Method |
| title_fullStr | Weak Fault Feature Extraction for Rolling Element Bearing Based on a Two-Stage Method |
| title_full_unstemmed | Weak Fault Feature Extraction for Rolling Element Bearing Based on a Two-Stage Method |
| title_short | Weak Fault Feature Extraction for Rolling Element Bearing Based on a Two-Stage Method |
| title_sort | weak fault feature extraction for rolling element bearing based on a two stage method |
| url | http://dx.doi.org/10.1155/2023/6671730 |
| work_keys_str_mv | AT lianhuijia weakfaultfeatureextractionforrollingelementbearingbasedonatwostagemethod AT lijiejiang weakfaultfeatureextractionforrollingelementbearingbasedonatwostagemethod AT yongliangwen weakfaultfeatureextractionforrollingelementbearingbasedonatwostagemethod AT hongchaowang weakfaultfeatureextractionforrollingelementbearingbasedonatwostagemethod |