Weak fault diagnosis method for rolling bearings under strong background noise based on EEMD-FK-AMCKD
ObjectiveTo address the challenge of accurately capturing weak features in vibration signals under strong noise interference, a joint filtering method combining ensemble empirical mode decomposition (EEMD), fast kurtogram (FK), and adaptive maximum correlation kurtosis deconvolution (AMCKD) was prop...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Transmission
2025-08-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#DOI：10.16578/j.issn.1004.2539.2025.08.019 |
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| author | XIE Guizhong XU Shuaiqiang DU Wenliao LUO Shuangqiang LI Hao WANG Liangwen GONG Xiaoyun |
| author_facet | XIE Guizhong XU Shuaiqiang DU Wenliao LUO Shuangqiang LI Hao WANG Liangwen GONG Xiaoyun |
| author_sort | XIE Guizhong |
| collection | DOAJ |
| description | ObjectiveTo address the challenge of accurately capturing weak features in vibration signals under strong noise interference, a joint filtering method combining ensemble empirical mode decomposition (EEMD), fast kurtogram (FK), and adaptive maximum correlation kurtosis deconvolution (AMCKD) was proposed.MethodsFirstly, the vibration signal was decomposed into multiple intrinsic mode functions (IMF) via EEMD for multiscale analysis. The IMF components were then screened using cross-correlation coefficients and kurtosis as evaluation metrics, followed by signal reconstruction. Next, the fast kurtogram algorithm was employed to determine the carrier frequency, bandwidth, and the layer with the maximum kurtosis value of the reconstructed signal, enabling the design of a bandpass filter for noise reduction. Subsequently, particle swarm optimization (PSO) was utilized to adaptively determine the MCKD parameters, and the AMCKD algorithm was applied to enhance the features of the filtered signal. Finally, the fault characteristic frequency was extracted via envelope demodulation and compared with the theoretical value to achieve fault diagnosis.ResultsThe results demonstrate that the proposed method effectively extracts weak features under strong noise interference, exhibiting robust noise resistance. This approach provides valuable reference for research on identifying bearing weak features in high-background-noise environments. |
| format | Article |
| id | doaj-art-ced407bc8e824caaa68a3e2f5d5d977e |
| institution | Kabale University |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2025-08-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-ced407bc8e824caaa68a3e2f5d5d977e2025-08-20T03:46:33ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392025-08-0149154163122984044Weak fault diagnosis method for rolling bearings under strong background noise based on EEMD-FK-AMCKDXIE GuizhongXU ShuaiqiangDU WenliaoLUO ShuangqiangLI HaoWANG LiangwenGONG XiaoyunObjectiveTo address the challenge of accurately capturing weak features in vibration signals under strong noise interference, a joint filtering method combining ensemble empirical mode decomposition (EEMD), fast kurtogram (FK), and adaptive maximum correlation kurtosis deconvolution (AMCKD) was proposed.MethodsFirstly, the vibration signal was decomposed into multiple intrinsic mode functions (IMF) via EEMD for multiscale analysis. The IMF components were then screened using cross-correlation coefficients and kurtosis as evaluation metrics, followed by signal reconstruction. Next, the fast kurtogram algorithm was employed to determine the carrier frequency, bandwidth, and the layer with the maximum kurtosis value of the reconstructed signal, enabling the design of a bandpass filter for noise reduction. Subsequently, particle swarm optimization (PSO) was utilized to adaptively determine the MCKD parameters, and the AMCKD algorithm was applied to enhance the features of the filtered signal. Finally, the fault characteristic frequency was extracted via envelope demodulation and compared with the theoretical value to achieve fault diagnosis.ResultsThe results demonstrate that the proposed method effectively extracts weak features under strong noise interference, exhibiting robust noise resistance. This approach provides valuable reference for research on identifying bearing weak features in high-background-noise environments.http://www.jxcd.net.cn/thesisDetails#DOI：10.16578/j.issn.1004.2539.2025.08.019Rolling bearingMaximum correlation deconvolutionFeature enhancementFast kurtogram |
| spellingShingle | XIE Guizhong XU Shuaiqiang DU Wenliao LUO Shuangqiang LI Hao WANG Liangwen GONG Xiaoyun Weak fault diagnosis method for rolling bearings under strong background noise based on EEMD-FK-AMCKD Jixie chuandong Rolling bearing Maximum correlation deconvolution Feature enhancement Fast kurtogram |
| title | Weak fault diagnosis method for rolling bearings under strong background noise based on EEMD-FK-AMCKD |
| title_full | Weak fault diagnosis method for rolling bearings under strong background noise based on EEMD-FK-AMCKD |
| title_fullStr | Weak fault diagnosis method for rolling bearings under strong background noise based on EEMD-FK-AMCKD |
| title_full_unstemmed | Weak fault diagnosis method for rolling bearings under strong background noise based on EEMD-FK-AMCKD |
| title_short | Weak fault diagnosis method for rolling bearings under strong background noise based on EEMD-FK-AMCKD |
| title_sort | weak fault diagnosis method for rolling bearings under strong background noise based on eemd fk amckd |
| topic | Rolling bearing Maximum correlation deconvolution Feature enhancement Fast kurtogram |
| url | http://www.jxcd.net.cn/thesisDetails#DOI：10.16578/j.issn.1004.2539.2025.08.019 |
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