Bearing fault diagnosis method based on improved compressed sensing and deep multi-kernel extreme learning machine
ObjectiveIn response to challenges such as large sampling data, extended diagnosis time, and subjective fault feature selection in traditional bearing fault diagnosis, a CS-DMKELM intelligent diagnosis model for rolling bearings is proposed based on compressed sensing(CS) and deep multi-kernel extre...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2024-01-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails?columnId=78737352&Fpath=home&index=0 |
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| author | FU Qiang HU Dong YANG Tongliang LUO Guoqing TAN Weimin |
| author_facet | FU Qiang HU Dong YANG Tongliang LUO Guoqing TAN Weimin |
| author_sort | FU Qiang |
| collection | DOAJ |
| description | ObjectiveIn response to challenges such as large sampling data, extended diagnosis time, and subjective fault feature selection in traditional bearing fault diagnosis, a CS-DMKELM intelligent diagnosis model for rolling bearings is proposed based on compressed sensing(CS) and deep multi-kernel extreme learning machine(D-MKELM) theory.MethodsFirstly, sparse signals were obtained through threshold processing of transformed domain signals. A Gaussian random matrix was employed as the measurement matrix to compress the processed data. Subsequently, the compressed data was used as the input signal for the D-MKELM. Particle swarm optimization(PSO) algorithm was applied to optimize critical parameters, enabling intelligent fault diagnosis.ResultsResults demonstrate that the proposed method, using only a small amount of bearing diagnostic data, automatically extracts feature information of bearings from a limited number of measurement signals through the D-MKELM. The proposed method enables rapid fault diagnosis of bearings. With a diagnostic time of 0.55 s, a final recognition accuracy of 99.29% was achieved. The proposed method reduces diagnostic time and exhibits high diagnostic accuracy, providing a new approach for handling massive bearing data in fault diagnosis. |
| format | Article |
| id | doaj-art-2d1448cfa50c4a95b6b3064bbaaeb8ac |
| institution | DOAJ |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2024-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-2d1448cfa50c4a95b6b3064bbaaeb8ac2025-08-20T02:42:04ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692024-01-011978737352Bearing fault diagnosis method based on improved compressed sensing and deep multi-kernel extreme learning machineFU QiangHU DongYANG TongliangLUO GuoqingTAN WeiminObjectiveIn response to challenges such as large sampling data, extended diagnosis time, and subjective fault feature selection in traditional bearing fault diagnosis, a CS-DMKELM intelligent diagnosis model for rolling bearings is proposed based on compressed sensing(CS) and deep multi-kernel extreme learning machine(D-MKELM) theory.MethodsFirstly, sparse signals were obtained through threshold processing of transformed domain signals. A Gaussian random matrix was employed as the measurement matrix to compress the processed data. Subsequently, the compressed data was used as the input signal for the D-MKELM. Particle swarm optimization(PSO) algorithm was applied to optimize critical parameters, enabling intelligent fault diagnosis.ResultsResults demonstrate that the proposed method, using only a small amount of bearing diagnostic data, automatically extracts feature information of bearings from a limited number of measurement signals through the D-MKELM. The proposed method enables rapid fault diagnosis of bearings. With a diagnostic time of 0.55 s, a final recognition accuracy of 99.29% was achieved. The proposed method reduces diagnostic time and exhibits high diagnostic accuracy, providing a new approach for handling massive bearing data in fault diagnosis.http://www.jxqd.net.cn/thesisDetails?columnId=78737352&Fpath=home&index=0Compressed sensingBearingKernel functionExtreme learning machineFault diagnosis |
| spellingShingle | FU Qiang HU Dong YANG Tongliang LUO Guoqing TAN Weimin Bearing fault diagnosis method based on improved compressed sensing and deep multi-kernel extreme learning machine Jixie qiangdu Compressed sensing Bearing Kernel function Extreme learning machine Fault diagnosis |
| title | Bearing fault diagnosis method based on improved compressed sensing and deep multi-kernel extreme learning machine |
| title_full | Bearing fault diagnosis method based on improved compressed sensing and deep multi-kernel extreme learning machine |
| title_fullStr | Bearing fault diagnosis method based on improved compressed sensing and deep multi-kernel extreme learning machine |
| title_full_unstemmed | Bearing fault diagnosis method based on improved compressed sensing and deep multi-kernel extreme learning machine |
| title_short | Bearing fault diagnosis method based on improved compressed sensing and deep multi-kernel extreme learning machine |
| title_sort | bearing fault diagnosis method based on improved compressed sensing and deep multi kernel extreme learning machine |
| topic | Compressed sensing Bearing Kernel function Extreme learning machine Fault diagnosis |
| url | http://www.jxqd.net.cn/thesisDetails?columnId=78737352&Fpath=home&index=0 |
| work_keys_str_mv | AT fuqiang bearingfaultdiagnosismethodbasedonimprovedcompressedsensinganddeepmultikernelextremelearningmachine AT hudong bearingfaultdiagnosismethodbasedonimprovedcompressedsensinganddeepmultikernelextremelearningmachine AT yangtongliang bearingfaultdiagnosismethodbasedonimprovedcompressedsensinganddeepmultikernelextremelearningmachine AT luoguoqing bearingfaultdiagnosismethodbasedonimprovedcompressedsensinganddeepmultikernelextremelearningmachine AT tanweimin bearingfaultdiagnosismethodbasedonimprovedcompressedsensinganddeepmultikernelextremelearningmachine |