Noise eliminated ensemble empirical mode decomposition for bearing fault diagnosis
Although noise-assisted decomposition methods, ensemble empirical mode decomposition (EEMD) and complementary EEMD (CEEMD) can reduce the drawbacks of empirical mode decomposition (EMD), they cannot fully eliminate the presence of white noise. In this paper, a method named noise eliminated EEMD (NEE...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Springer Link
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/31924/ http://umpir.ump.edu.my/id/eprint/31924/1/jvet%20Final%202.pdf |
| _version_ | 1848823891930120192 |
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| author | Atik, Faysal Ngui, Wai Keng Lim, M. H. |
| author_facet | Atik, Faysal Ngui, Wai Keng Lim, M. H. |
| author_sort | Atik, Faysal |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Although noise-assisted decomposition methods, ensemble empirical mode decomposition (EEMD) and complementary EEMD (CEEMD) can reduce the drawbacks of empirical mode decomposition (EMD), they cannot fully eliminate the presence of white noise. In this paper, a method named noise eliminated EEMD (NEEEMD) was proposed to reduce further the white noise in the intrinsic functions and keep the ensembles optimum. The NEEEMD algorithm also decomposes the ensemble of white noise signals using EMD and subtracts from the outputs of EEMD. A simulated signal was used to demonstrate the performance of NEEEMD using root-mean-square error (RRMSE) and time & envelope spectrum kurtosis (TESK). A sensitive mode (SM) selection method was proposed to select the most sensitive intrinsic mode functions (IMFs) from NEEEMD which takes multiplication of kurtosis in the time domain and energy-entropy in the frequency domain. Finally, to enhance the signal's fault-related impulses, an advanced filter called MOMEDA was applied to the most sensitive IMF. The significance of the proposed method was illustrated using the envelope spectrum from bearing signals containing different types of faults at various speeds and motor loads. The output of the proposed method, EEMD and CEEMD was compared using the envelope spectrum to identify fault characteristic impulses. Envelope spectrum analysis proved that our proposed method performed better in every case by providing more fault-related impulses. |
| first_indexed | 2025-11-15T03:04:21Z |
| format | Article |
| id | ump-31924 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:04:21Z |
| publishDate | 2021 |
| publisher | Springer Link |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-319242021-09-01T09:49:39Z http://umpir.ump.edu.my/id/eprint/31924/ Noise eliminated ensemble empirical mode decomposition for bearing fault diagnosis Atik, Faysal Ngui, Wai Keng Lim, M. H. TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery Although noise-assisted decomposition methods, ensemble empirical mode decomposition (EEMD) and complementary EEMD (CEEMD) can reduce the drawbacks of empirical mode decomposition (EMD), they cannot fully eliminate the presence of white noise. In this paper, a method named noise eliminated EEMD (NEEEMD) was proposed to reduce further the white noise in the intrinsic functions and keep the ensembles optimum. The NEEEMD algorithm also decomposes the ensemble of white noise signals using EMD and subtracts from the outputs of EEMD. A simulated signal was used to demonstrate the performance of NEEEMD using root-mean-square error (RRMSE) and time & envelope spectrum kurtosis (TESK). A sensitive mode (SM) selection method was proposed to select the most sensitive intrinsic mode functions (IMFs) from NEEEMD which takes multiplication of kurtosis in the time domain and energy-entropy in the frequency domain. Finally, to enhance the signal's fault-related impulses, an advanced filter called MOMEDA was applied to the most sensitive IMF. The significance of the proposed method was illustrated using the envelope spectrum from bearing signals containing different types of faults at various speeds and motor loads. The output of the proposed method, EEMD and CEEMD was compared using the envelope spectrum to identify fault characteristic impulses. Envelope spectrum analysis proved that our proposed method performed better in every case by providing more fault-related impulses. Springer Link 2021-08-04 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/31924/1/jvet%20Final%202.pdf Atik, Faysal and Ngui, Wai Keng and Lim, M. H. (2021) Noise eliminated ensemble empirical mode decomposition for bearing fault diagnosis. Journal of Vibration Engineering & Technologies. pp. 1-17. ISSN 2523-3939. (Published) https://link.springer.com/article/10.1007/s42417-021-00358-y#article-info https://doi.org/10.1007/s42417-021-00358-y |
| spellingShingle | TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery Atik, Faysal Ngui, Wai Keng Lim, M. H. Noise eliminated ensemble empirical mode decomposition for bearing fault diagnosis |
| title | Noise eliminated ensemble empirical mode decomposition for bearing fault diagnosis |
| title_full | Noise eliminated ensemble empirical mode decomposition for bearing fault diagnosis |
| title_fullStr | Noise eliminated ensemble empirical mode decomposition for bearing fault diagnosis |
| title_full_unstemmed | Noise eliminated ensemble empirical mode decomposition for bearing fault diagnosis |
| title_short | Noise eliminated ensemble empirical mode decomposition for bearing fault diagnosis |
| title_sort | noise eliminated ensemble empirical mode decomposition for bearing fault diagnosis |
| topic | TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery |
| url | http://umpir.ump.edu.my/id/eprint/31924/ http://umpir.ump.edu.my/id/eprint/31924/ http://umpir.ump.edu.my/id/eprint/31924/ http://umpir.ump.edu.my/id/eprint/31924/1/jvet%20Final%202.pdf |