EEMD Energy Spectrum Decoupling: An Efficient Hilbert–Huang Fusion Approach for Intelligent Bearing Fault Diagnosis
As a critical component of rotating machinery, the operational status of rolling bearings is considered to directly determine the reliability of rail traffic systems. To address the complex modulation effects existing between multiple bearing components and the non-linear, non-stationary characteris...
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MDPI AG
2025-06-01
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| author | Lianyou Lai Weijian Xu Zhongzhe Song |
| author_facet | Lianyou Lai Weijian Xu Zhongzhe Song |
| author_sort | Lianyou Lai |
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| description | As a critical component of rotating machinery, the operational status of rolling bearings is considered to directly determine the reliability of rail traffic systems. To address the complex modulation effects existing between multiple bearing components and the non-linear, non-stationary characteristics exhibited by vibration acceleration signals, an intelligent fault diagnosis method for bearings based on Hilbert envelope demodulation and Ensemble Empirical Mode Decomposition energy distribution features is proposed. First, the original vibration signal is subjected to envelope demodulation processing by the Hilbert transform, thereby effectively separating the envelope signal containing fault characteristics. Subsequently, the demodulated envelope signal is decomposed by EEMD to extract Intrinsic Mode Functions (IMFs), where each IMF component is calculated layer by layer using a normalization method based on the EEMD decomposition sequence. Finally, the proposed algorithm is validated by the standard bearing fault dataset from Case Western Reserve University. Experimental results show that the proposed method achieves 100% accuracy in fault identification, and its superiority is proven to exceed conventional diagnostic approaches significantly. |
| format | Article |
| id | doaj-art-a2c23698b9854650a6b3ece1d45188ef |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-a2c23698b9854650a6b3ece1d45188ef2025-08-20T03:26:09ZengMDPI AGApplied Sciences2076-34172025-06-011512645810.3390/app15126458EEMD Energy Spectrum Decoupling: An Efficient Hilbert–Huang Fusion Approach for Intelligent Bearing Fault DiagnosisLianyou Lai0Weijian Xu1Zhongzhe Song2School of Ocean Information Engineering, Jimei University, Xiamen 361000, ChinaSchool of Ocean Information Engineering, Jimei University, Xiamen 361000, ChinaSchool of Ocean Information Engineering, Jimei University, Xiamen 361000, ChinaAs a critical component of rotating machinery, the operational status of rolling bearings is considered to directly determine the reliability of rail traffic systems. To address the complex modulation effects existing between multiple bearing components and the non-linear, non-stationary characteristics exhibited by vibration acceleration signals, an intelligent fault diagnosis method for bearings based on Hilbert envelope demodulation and Ensemble Empirical Mode Decomposition energy distribution features is proposed. First, the original vibration signal is subjected to envelope demodulation processing by the Hilbert transform, thereby effectively separating the envelope signal containing fault characteristics. Subsequently, the demodulated envelope signal is decomposed by EEMD to extract Intrinsic Mode Functions (IMFs), where each IMF component is calculated layer by layer using a normalization method based on the EEMD decomposition sequence. Finally, the proposed algorithm is validated by the standard bearing fault dataset from Case Western Reserve University. Experimental results show that the proposed method achieves 100% accuracy in fault identification, and its superiority is proven to exceed conventional diagnostic approaches significantly.https://www.mdpi.com/2076-3417/15/12/6458bearing fault diagnosisenvelope spectrum analysisempirical mode decomposition (EMD) |
| spellingShingle | Lianyou Lai Weijian Xu Zhongzhe Song EEMD Energy Spectrum Decoupling: An Efficient Hilbert–Huang Fusion Approach for Intelligent Bearing Fault Diagnosis Applied Sciences bearing fault diagnosis envelope spectrum analysis empirical mode decomposition (EMD) |
| title | EEMD Energy Spectrum Decoupling: An Efficient Hilbert–Huang Fusion Approach for Intelligent Bearing Fault Diagnosis |
| title_full | EEMD Energy Spectrum Decoupling: An Efficient Hilbert–Huang Fusion Approach for Intelligent Bearing Fault Diagnosis |
| title_fullStr | EEMD Energy Spectrum Decoupling: An Efficient Hilbert–Huang Fusion Approach for Intelligent Bearing Fault Diagnosis |
| title_full_unstemmed | EEMD Energy Spectrum Decoupling: An Efficient Hilbert–Huang Fusion Approach for Intelligent Bearing Fault Diagnosis |
| title_short | EEMD Energy Spectrum Decoupling: An Efficient Hilbert–Huang Fusion Approach for Intelligent Bearing Fault Diagnosis |
| title_sort | eemd energy spectrum decoupling an efficient hilbert huang fusion approach for intelligent bearing fault diagnosis |
| topic | bearing fault diagnosis envelope spectrum analysis empirical mode decomposition (EMD) |
| url | https://www.mdpi.com/2076-3417/15/12/6458 |
| work_keys_str_mv | AT lianyoulai eemdenergyspectrumdecouplinganefficienthilberthuangfusionapproachforintelligentbearingfaultdiagnosis AT weijianxu eemdenergyspectrumdecouplinganefficienthilberthuangfusionapproachforintelligentbearingfaultdiagnosis AT zhongzhesong eemdenergyspectrumdecouplinganefficienthilberthuangfusionapproachforintelligentbearingfaultdiagnosis |