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Open AccessArticle
EEMD Energy Spectrum Decoupling: An Efficient Hilbert–Huang Fusion Approach for Intelligent Bearing Fault Diagnosis
by
Lianyou Lai
Lianyou Lai
,
Weijian Xu
Weijian Xu *
and
Zhongzhe Song
Zhongzhe Song
School of Ocean Information Engineering, Jimei University, Xiamen 361000, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(12), 6458; https://doi.org/10.3390/app15126458 (registering DOI)
Submission received: 13 May 2025
/
Revised: 3 June 2025
/
Accepted: 6 June 2025
/
Published: 8 June 2025
Abstract
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.
Share and Cite
MDPI and ACS Style
Lai, L.; Xu, W.; Song, Z.
EEMD Energy Spectrum Decoupling: An Efficient Hilbert–Huang Fusion Approach for Intelligent Bearing Fault Diagnosis. Appl. Sci. 2025, 15, 6458.
https://doi.org/10.3390/app15126458
AMA Style
Lai L, Xu W, Song Z.
EEMD Energy Spectrum Decoupling: An Efficient Hilbert–Huang Fusion Approach for Intelligent Bearing Fault Diagnosis. Applied Sciences. 2025; 15(12):6458.
https://doi.org/10.3390/app15126458
Chicago/Turabian Style
Lai, Lianyou, Weijian Xu, and Zhongzhe Song.
2025. "EEMD Energy Spectrum Decoupling: An Efficient Hilbert–Huang Fusion Approach for Intelligent Bearing Fault Diagnosis" Applied Sciences 15, no. 12: 6458.
https://doi.org/10.3390/app15126458
APA Style
Lai, L., Xu, W., & Song, Z.
(2025). EEMD Energy Spectrum Decoupling: An Efficient Hilbert–Huang Fusion Approach for Intelligent Bearing Fault Diagnosis. Applied Sciences, 15(12), 6458.
https://doi.org/10.3390/app15126458
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